<?xml version="1.0" encoding="UTF-8"?>
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    <title>machine-learning</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/49/feed"/>
    <updated>2026-09-05T06:45:06+00:00</updated>
    <id>https://links.biapy.com/guest/tags/49/feed</id>
            <entry>
            <id>https://links.biapy.com/links/13486</id>
            <title type="text"><![CDATA[Latitude.sh]]></title>
            <link rel="alternate" href="https://www.latitude.sh/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13486"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The global AI enabler cloud.

Deploy globally, scale easily, and build on the cloud that enables AI-native companies to thrive.
Whether you’re building AI models, blockchain networks, or games, Latitude makes it easy.

Related contents:

- [\#1021: We got addicted to an AI model we can&amp;#039;t talk about @ Syntax](https://syntax.fm/show/1021/we-got-addicted-to-an-ai-model-we-can-t-talk-about).]]>
            </summary>
            <updated>2026-08-02T14:20:53+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/13469</id>
            <title type="text"><![CDATA[World Model Optimizer]]></title>
            <link rel="alternate" href="https://github.com/experientiallabs/world-model-optimizer" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13469"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Build continually improving models on your agent traces by distilling frontier open models.

wmo optimize turns collected agent traces into smaller open-source models using the Tinker API, with optional closed-loop simulation training. wmo serve exposes an endpoint that routes requests between frontier and smaller models; on RouterBench, it maintains frontier quality at 27% lower cost. Rerun the pipeline as new traces arrive to continually improve a model you own.]]>
            </summary>
            <updated>2026-07-31T12:00:22+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/13384</id>
            <title type="text"><![CDATA[Petals]]></title>
            <link rel="alternate" href="https://petals.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13384"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Run LLMs at home, BitTorrent-style.

 You load a part of the model, then join a network of people serving its other parts. Single‑batch inference runs at up to 6 tokens/sec for Llama 2 (70B) and up to 4 tokens/sec for Falcon (180B) — enough for chatbots and interactive apps. 

- [Petals @ GitHub](https://github.com/bigscience-workshop/petals).]]>
            </summary>
            <updated>2026-07-23T11:49:42+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/13378</id>
            <title type="text"><![CDATA[Mesh LLM]]></title>
            <link rel="alternate" href="https://meshllm.cloud/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13378"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat. 

Mesh LLM pools GPUs and memory across machines and exposes the result as one OpenAI-compatible API at http://localhost:9337/v1. Start one node, add more nodes later, and let the mesh decide whether a model runs locally, routes to a peer, or uses Skippy stage splits for models that are too large for one box.

- [Mesh LLM @ GitHub](https://github.com/Mesh-LLM/mesh-llm).]]>
            </summary>
            <updated>2026-07-23T06:34:13+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/13337</id>
            <title type="text"><![CDATA[k8s-aibom]]></title>
            <link rel="alternate" href="https://github.com/GoogleCloudPlatform/k8s-aibom" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13337"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Kubernetes controller that generates CycloneDX 1.6 ML-BOM documents for AI workloads at runtime — inference services, agent stacks, RAG pipelines, training jobs, evaluation harnesses — with auditor-traceable evidence for every attribute. 

Related contents:

- [Securing the AI supply chain on GKE: Introducing k8s-aibom for automated AI BOMs @ Google Cloud Blog](https://cloud.google.com/blog/products/identity-security/introducing-k8s-aibom-on-gke-for-automated-ai-bills-of-materials/).]]>
            </summary>
            <updated>2026-07-20T12:04:54+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/13043</id>
            <title type="text"><![CDATA[gzipt]]></title>
            <link rel="alternate" href="https://github.com/nathan-barry/gzipt" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13043"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A compression based language model.

gzipt generates text using gzip as its only model. No neural network, no training, no parameters. You prime it with a corpus, and it continues a prompt by searching for the byte sequences that compress best, because what compresses well is what the model predicts.

Related contents:

- [Can gzip be a language model? @ Nathan.RS](https://nathan.rs/posts/gzip-lm/).]]>
            </summary>
            <updated>2026-06-18T11:51:02+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12650</id>
            <title type="text"><![CDATA[ML Intern]]></title>
            <link rel="alternate" href="https://github.com/huggingface/ml-intern" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12650"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🤗 ml-intern: an open-source ML engineer that reads papers, trains models, and ships ML models 

An ML intern that autonomously researches, writes, and ships good quality ML related code using the Hugging Face ecosystem — with deep access to docs, papers, datasets, and cloud compute.]]>
            </summary>
            <updated>2026-04-29T14:22:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12593</id>
            <title type="text"><![CDATA[GAIA SDK]]></title>
            <link rel="alternate" href="https://amd-gaia.ai/docs" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12593"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[GAIA Logo GAIA: AI Agent Framework for AMD Ryzen AI.

GAIA is AMD&amp;#039;s open-source framework for building intelligent AI agents that run 100% locally on AMD Ryzen AI hardware. Keep your data private, eliminate cloud costs, and deploy in air-gapped environments—all with hardware-accelerated performance.

- [GAIA @ GitHub](https://github.com/amd/gaia).

Related contents:

- [AMD&amp;#039;s GAIA Makes It Easier To Import/Export Custom AI Agents Across PCs @ phoronix](https://www.phoronix.com/news/AMD-GAIA-Import-Export-Agent).
- [AMD GAIA 0.17.3 sait maintenant empaqueter vos agents IA dans un installeur custom @ Korben :fr:](https://korben.info/amd-gaia-0-17-3-sait-maintenant-empaqueter-vos-agents-ia-dans-un-installeur-custom.html).]]>
            </summary>
            <updated>2026-04-21T13:25:36+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12584</id>
            <title type="text"><![CDATA[DeepGEMM]]></title>
            <link rel="alternate" href="https://github.com/deepseek-ai/DeepGEMM" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12584"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[DeepGEMM: clean and efficient FP8 GEMM kernels with fine-grained scaling.

DeepGEMM is a unified, high-performance tensor core kernel library that brings together the key computation primitives of modern large language models — GEMMs (FP8, FP4, BF16), fused MoE with overlapped communication (Mega MoE), MQA scoring for the lightning indexer, HyperConnection (HC), and more — into a single, cohesive CUDA codebase. All kernels are compiled at runtime via a lightweight Just-In-Time (JIT) module, requiring no CUDA compilation during installation.]]>
            </summary>
            <updated>2026-04-20T11:54:19+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12583</id>
            <title type="text"><![CDATA[EvoMap]]></title>
            <link rel="alternate" href="https://evomap.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12583"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI Self-Evolution Infrastructure. One agent learns. A million inherit.
The GEP-Powered Self-Evolution Engine for AI Agents. Genome Evolution Protocol.

Carbon and silicon, intertwined like a double helix -- agents share evolved capabilities through open protocol, humans provide the intuition that no algorithm can replicate.

- [EvoMap Evolver @ GitHub](https://github.com/EvoMap/evolver).]]>
            </summary>
            <updated>2026-04-20T11:53:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12529</id>
            <title type="text"><![CDATA[Aidge]]></title>
            <link rel="alternate" href="https://eclipse.dev/aidge/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12529"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Aidge is an innovative, open-source framework designed to streamline and accelerate the deployment of Deep Neural Networks onto diverse hardware targets. In today’s rapidly evolving AI landscape, moving from a trained model to a high-performance, production-ready application can be a complex and time-consuming process.

- [Aidge @ Eclipe&amp;#039;s GitLab](https://gitlab.eclipse.org/eclipse/aidge/aidge).

Related contents:

- [AIDGE - Du deep learning sur vos microcontrôleurs @ Korben :fr:](https://korben.info/aidge-framework-ia-embarquee-cea-2.html).]]>
            </summary>
            <updated>2026-04-13T09:22:51+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12445</id>
            <title type="text"><![CDATA[GuppyLM]]></title>
            <link rel="alternate" href="https://github.com/arman-bd/guppylm" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12445"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A ~9M parameter LLM that talks like a small fish.

This project exists to show that training your own language model is not magic. No PhD required. No massive GPU cluster. One Colab notebook, 5 minutes, and you have a working LLM that you built from scratch — data generation, tokenizer, model architecture, training loop, and inference. If you can run a notebook, you can train a language model.]]>
            </summary>
            <updated>2026-04-07T07:32:37+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12430</id>
            <title type="text"><![CDATA[ZML - Model to Metal]]></title>
            <link rel="alternate" href="https://zml.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12430"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[ZML is a production inference stack, purpose-built to decouple AI workloads from proprietary hardware.

Any model, many hardwares, one codebase, peak performance.
Compiled directly to NVIDIA, AMD, TPU, Trainium for peak hardware performance on any accelerator. No rewriting.

- [ZML @ GitHub](https://github.com/zml/zml).

Related contents:

- [Introducing ZML/v2 @ ZML](https://zml.ai/posts/zml-v2/).]]>
            </summary>
            <updated>2026-04-04T09:29:45+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12343</id>
            <title type="text"><![CDATA[KAITO]]></title>
            <link rel="alternate" href="https://kaito-project.github.io/kaito/docs/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12343"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Kubernetes AI Toolchain Operator.

KAITO is an operator that automates the AI/ML model inference or tuning workload in a Kubernetes cluster. The target models are popular open-sourced large models such as falcon and phi-3.

- [KAITO @ GitHub](https://github.com/kaito-project/kaito).

Related contents:

- [Building a Kubernetes-native pattern for AI infrastructure at scale @ The New Stack](https://thenewstack.io/kubernetes-native-ai-infrastructure/).]]>
            </summary>
            <updated>2026-03-28T14:42:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12259</id>
            <title type="text"><![CDATA[SAM 3D Body]]></title>
            <link rel="alternate" href="https://github.com/facebookresearch/sam-3d-body" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12259"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Robust Full-Body Human Mesh Recovery.

SAM 3D Body is one part of SAM 3D, a pair of models for object and human mesh reconstruction. 

 The repository provides code for running inference with the SAM 3D Body Model (3DB), links for downloading the trained model checkpoints and datasets, and example notebooks that show how to use the model. 

Related contents:

- [Fast SAM 3D Body - Quand l&amp;#039;IA scanne votre corps en 3D en 65ms @ Korben :fr:](https://korben.info/fast-sam-3d-body-reconstruction-temps-reel-robots.html).]]>
            </summary>
            <updated>2026-03-23T15:50:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12222</id>
            <title type="text"><![CDATA[Fish Speech]]></title>
            <link rel="alternate" href="https://speech.fish.audio/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12222"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[SOTA Open Source TTS.

State-of-the-art multilingual text-to-speech (TTS) system, redefining the boundaries of voice generation.

Fish Audio S2 Pro is the most advanced multimodal model developed by Fish Audio. Trained on over 10 million hours of audio data covering more than 80 languages, S2 Pro combines a Dual-Autoregressive (Dual-AR) architecture with reinforcement learning (RL) alignment to generate speech that is exceptionally natural, realistic, and emotionally rich, leading the competition among both open-source and closed-source systems.

- [Fish Speech @ GitHub](https://github.com/fishaudio/fish-speech).]]>
            </summary>
            <updated>2026-03-20T14:02:13+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12218</id>
            <title type="text"><![CDATA[Nightingale]]></title>
            <link rel="alternate" href="https://nightingale.cafe/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12218"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Karaoke from your music library.  Machine learning powered Karaoke app (with scores!) .

Turn any song into karaoke. A self-contained party game that separates vocals, transcribes lyrics, and plays it all back with word-level sync and pitch scoring. 

- [Nightingale @ GitHub](https://github.com/rzru/nightingale).]]>
            </summary>
            <updated>2026-03-20T13:50:22+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12137</id>
            <title type="text"><![CDATA[LiteRT]]></title>
            <link rel="alternate" href="https://ai.google.dev/edge/litert/android" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12137"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[LiteRT, successor to TensorFlow Lite. is Google&amp;#039;s On-device framework for high-performance ML &amp;amp; GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization 

- [LiteRT @ GitHub](https://github.com/google-ai-edge/LiteRT).

Related contents:

- [LiteRT - L&amp;#039;IA embarquée de Google passe la seconde @ Korben :fr:](https://korben.info/litert-google-ai-edge-inference-mobile.html).]]>
            </summary>
            <updated>2026-03-16T09:37:19+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12048</id>
            <title type="text"><![CDATA[Ray]]></title>
            <link rel="alternate" href="https://www.ray.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12048"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Scale Machine Learning &amp;amp; AI Computing.

 Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads. 

- [Ray @ GitHub](https://github.com/ray-project/ray).]]>
            </summary>
            <updated>2026-03-09T07:18:36+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12044</id>
            <title type="text"><![CDATA[Label Studio]]></title>
            <link rel="alternate" href="https://labelstud.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12044"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open Source Data Labeling.

The most flexible data labeling platform to fine-tune LLMs, prepare training data, or evaluate AI systems.
 Label Studio is a multi-type data labeling and annotation tool with standardized output format.
Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models.

- [Label Studio @ GitHub](https://github.com/HumanSignal/label-studio/).]]>
            </summary>
            <updated>2026-03-06T14:56:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11598</id>
            <title type="text"><![CDATA[coreml-cli]]></title>
            <link rel="alternate" href="https://github.com/schappim/coreml-cli" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11598"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A native command-line interface for working with Apple Core ML models on macOS.
Inspect, run inference, benchmark, and manage Core ML models without Xcode or Python.

Related contents:

- [CoreML CLI - Gérez vos modèles Apple sans Xcode ! @ Korben :fr:](https://korben.info/coreml-cli-gestion-apple-modele-sans-xcode.html).]]>
            </summary>
            <updated>2026-01-26T08:18:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11421</id>
            <title type="text"><![CDATA[Apache Spark]]></title>
            <link rel="alternate" href="https://spark.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11421"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Unified Engine for large-scale data analytics.

Apache Spark™ is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. 

- [Apache Spark @ GitHub](https://github.com/apache/spark).

Related contents:

- [Introducing Apache Spark® 4.1 @ databricks](https://www.databricks.com/blog/introducing-apache-sparkr-41).
- [From Chaos to Scale: Templatizing Spark Declarative Pipelines with DLT-META @ databricks](https://www.databricks.com/blog/chaos-scale-templatizing-spark-declarative-pipelines-dlt-meta).
- [Breaking the Microbatch Barrier: The Architecture of Apache Spark Real-Time Mode @ databricks](https://www.databricks.com/blog/breaking-microbatch-barrier-architecture-apache-spark-real-time-mode).]]>
            </summary>
            <updated>2026-03-17T12:31:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11389</id>
            <title type="text"><![CDATA[Machine Learning Systems Textbook]]></title>
            <link rel="alternate" href="https://mlsysbook.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11389"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Principles and Practices of Engineering Artificially Intelligent Systems.

- [Machine Learning Systems Textbook @ GitHub](https://github.com/harvard-edge/cs249r_book)]]>
            </summary>
            <updated>2026-01-06T08:16:41+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11176</id>
            <title type="text"><![CDATA[Machine Learning for Beginners - A Curriculum]]></title>
            <link rel="alternate" href="https://github.com/microsoft/ML-For-Beginners" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11176"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all.

Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our AI for Beginners&amp;#039; curriculum. Pair these lessons with our &amp;#039;Data Science for Beginners&amp;#039; curriculum, as well!

Related contents:

- [12 semaines de formation Machine Learning gratos sur GitHub (et en français svp !) @ Korben :fr:](https://korben.info/microsoft-ml-for-beginners-formation-machine-learn.html).]]>
            </summary>
            <updated>2025-12-08T12:43:06+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11133</id>
            <title type="text"><![CDATA[Gradio]]></title>
            <link rel="alternate" href="https://www.gradio.app/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11133"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Build machine learning apps in Python.

Create web interfaces for your ML models in minutes. Deploy anywhere, share with anyone.

Gradio is an open-source Python package that allows you to quickly build a demo or web application for your machine learning model, API, or any arbitrary Python function. You can then share a link to your demo or web application in just a few seconds using Gradio&amp;#039;s built-in sharing features. No JavaScript, CSS, or web hosting experience needed!

- [Gradio @ GitHub](https://github.com/gradio-app/gradio).

Related contents:

- [Gradio 6 débarque pour créer des interfaces encore plus fluides @ Korben :fr:](https://korben.info/gradio-6-python-machine-learning-interface-svelte.html).]]>
            </summary>
            <updated>2025-12-01T09:19:55+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10961</id>
            <title type="text"><![CDATA[Magika]]></title>
            <link rel="alternate" href="https://securityresearch.google/magika/introduction/overview" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10961"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Fast and accurate AI powered file content types detection.

Magika is a novel AI-powered file type detection tool that relies on the recent advance of deep learning to provide accurate detection. Under the hood, Magika employs a custom, highly optimized model that only weighs about a few MBs, and enables precise file identification within milliseconds, even when running on a single CPU. Magika has been trained and evaluated on a dataset of ~100M samples across 200+ content types (covering both binary and textual file formats), and it achieves an average ~99% accuracy on our test set.

- [Magika @ GitHub](https://github.com/google/magika).]]>
            </summary>
            <updated>2025-11-14T15:28:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10900</id>
            <title type="text"><![CDATA[OpenPCC]]></title>
            <link rel="alternate" href="https://github.com/openpcc/openpcc" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10900"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An open-source framework for provably private AI inference.

OpenPCC is an open-source framework for provably private AI inference, inspired by Apple’s Private Cloud Compute but fully open, auditable, and deployable on your own infrastructure. It allows anyone to run open or custom AI models without exposing prompts, outputs, or logs - enforcing privacy with encrypted streaming, hardware attestation, and unlinkable requests.]]>
            </summary>
            <updated>2025-11-07T13:32:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10877</id>
            <title type="text"><![CDATA[Rmlx]]></title>
            <link rel="alternate" href="https://hughjonesd.github.io/Rmlx/index.html" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10877"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[R interface to Apple’s MLX (Machine Learning eXchange) library.

Rmlx provides an R interface to Apple’s MLX framework, enabling high-performance GPU computing on Apple Silicon.

- [Rmlx @ GitHub](https://github.com/hughjonesd/Rmlx/).]]>
            </summary>
            <updated>2025-11-04T12:38:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10756</id>
            <title type="text"><![CDATA[Coral NPU]]></title>
            <link rel="alternate" href="https://github.com/google-coral/coralnpu" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10756"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A machine learning accelerator core designed for energy-efficient AI at the edge. 

Coral NPU is a hardware accelerator for ML inferencing. Coral NPU is an Open Source IP designed by Google Research and is freely available for integration into ultra-low-power System-on-Chips (SoCs) targeting wearable devices such as hearables, augmented reality (AR) glasses and smart watches.]]>
            </summary>
            <updated>2025-10-22T11:30:17+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10697</id>
            <title type="text"><![CDATA[Lance]]></title>
            <link rel="alternate" href="https://lancedb.github.io/lance/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10697"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming.. 

Lance is a modern columnar data format optimized for machine learning and AI applications. It efficiently handles diverse multimodal data types while providing high-performance querying and versioning capabilities.

- [Lance @ GitHub](https://github.com/lancedb/lance).

Related contents:

- [Lance takes aim at Parquet in file format joust @ The Register](https://www.theregister.com/2025/10/14/lance_parquet/).]]>
            </summary>
            <updated>2025-10-17T12:01:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10342</id>
            <title type="text"><![CDATA[Effort.jl]]></title>
            <link rel="alternate" href="https://github.com/CosmologicalEmulators/Effort.jl" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10342"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[EFfective Field theORy surrogaTe.:

Related contents:

- [Quand l&amp;#039;IA apprend à simuler l&amp;#039;univers sur un simple laptop @ Korben :fr:](https://korben.info/effort-jl-ia-simulation-univers-laptop.html).]]>
            </summary>
            <updated>2025-09-22T11:27:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10337</id>
            <title type="text"><![CDATA[Prophet]]></title>
            <link rel="alternate" href="https://facebook.github.io/prophet/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10337"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Forecasting at scale.

 Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. 

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

- [Prophet: Automatic Forecasting Procedure @ GitHub](https://github.com/facebook/prophet).

Related contents:

- [Predictive Autoscaling in Kubernetes with Keda and Prophet @ Minimal Devops&amp;#039; Medium](https://minimaldevops.com/predictive-autoscaling-in-kubernetes-with-keda-and-prophet-cbccd96cf881).]]>
            </summary>
            <updated>2025-09-22T07:10:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10331</id>
            <title type="text"><![CDATA[The Coding Train]]></title>
            <link rel="alternate" href="https://thecodingtrain.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10331"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Welcome to the Coding Train with Daniel Shiffman! A community dedicated to learning creative coding with beginner-friendly tutorials and projects on YouTube and more.

- [The Coding Train: Machine-Learning @ GitHub](https://github.com/CodingTrain/Machine-Learning).

Related contents:

- [5 GitHub Repositories for an Instant Knowledge Boost @ Surajondev](https://www.surajon.dev/5-github-repositories-for-an-instant-knowledge-boost).]]>
            </summary>
            <updated>2025-09-22T06:40:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10264</id>
            <title type="text"><![CDATA[OpenVision 2]]></title>
            <link rel="alternate" href="https://ucsc-vlaa.github.io/OpenVision2/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10264"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning.

OpenVision 2: A Family of Generative Pretrained Visual Encoders that removes the text encoder and contrastive loss, training with caption-only supervision.

- [OpenVision &amp;amp; OpenVision 2 @ GitHub](https://github.com/UCSC-VLAA/OpenVision).]]>
            </summary>
            <updated>2025-09-18T06:09:18+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10261</id>
            <title type="text"><![CDATA[S3GD]]></title>
            <link rel="alternate" href="https://github.com/WhyPhyLabs/s3gd" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10261"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[S3GD is a highly optimized, PyTorch-compatible Triton implementation of the Smoothed SignSGD optimizer, meant for reinforcement learning post-training.

Related contents:

- [S3GD Optimizer Algorithm @ WhyPhyLabs](https://whyphy.ai/blog/Aug%202025/08-22-2025-s3gd-blog.md).]]>
            </summary>
            <updated>2025-09-18T06:02:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10245</id>
            <title type="text"><![CDATA[Chronon]]></title>
            <link rel="alternate" href="https://github.com/airbnb/chronon" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10245"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Chronon is a data platform for serving for AI/ML applications. 

Chronon is a platform that abstracts away the complexity of data computation and serving for AI/ML applications. Users define features as transformation of raw data, then Chronon can perform batch and streaming computation, scalable backfills, low-latency serving, guaranteed correctness and consistency, as well as a host of observability and monitoring tools.

It allows you to utilize all of the data within your organization, from batch tables, event streams or services to power your AI/ML projects, without needing to worry about all the complex orchestration that this would usually entail.]]>
            </summary>
            <updated>2025-09-17T11:19:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10215</id>
            <title type="text"><![CDATA[HAMi]]></title>
            <link rel="alternate" href="https://project-hami.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10215"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open, Device Virtualization, VGPU, Heterogeneous AI Computing.

HAMi (Heterogeneous AI Computing Virtualization Middleware) formerly known as k8s-vGPU-scheduler, is an &amp;#039;all-in-one&amp;#039; chart designed to manage Heterogeneous AI Computing Devices in a k8s cluster. It can provide the ability to share Heterogeneous AI devices and provide resource isolation among tasks.

- [HAMi @ GitHub](https://github.com/Project-HAMi/HAMi).

Related contents:

- [HAMi becomes a CNCF incubating project @ CNCF](https://www.cncf.io/blog/2026/07/15/hami-becomes-a-cncf-incubating-project/).
- [Does Kubernetes DRA Replace HAMi? @ CNCF](https://www.cncf.io/blog/2026/08/07/does-kubernetes-dra-replace-hami/).]]>
            </summary>
            <updated>2026-08-10T12:31:14+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10206</id>
            <title type="text"><![CDATA[GeoAI]]></title>
            <link rel="alternate" href="https://opengeoai.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10206"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A powerful Python package for integrating artificial intelligence with geospatial data analysis and visualization.

- [GeoAI @ GitHub](https://github.com/opengeos/geoai).

Related contents:

- [GeoAI Workshop: Unlocking the Power of GeoAI with Python @ Open Geospatial Solutions&amp;#039; YouTube](https://www.youtube.com/watch?v=jdK-cleFUkc).]]>
            </summary>
            <updated>2025-09-15T13:16:22+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10196</id>
            <title type="text"><![CDATA[ToddlerBot]]></title>
            <link rel="alternate" href="https://toddlerbot.github.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10196"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation.

ToddlerBot is a low-cost, open-source humanoid robot platform designed for scalable policy learning and research in robotics and AI.

This codebase includes low-level control, RL training, DP training, real-world deployment and basically EVERYTHING you need to run ToddlerBot in the real world!

- [ToddlerBot @ GitHub](https://github.com/hshi74/toddlerbot).

Related contents:

- [ToddlerBot - Le robot humanoïde à 4300 $ qui ridiculise les géants de la tech @ Korben :fr:](https://korben.info/toddlerbot-robot-humanoide-250-ridiculise-geants.html).]]>
            </summary>
            <updated>2025-09-15T09:21:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10111</id>
            <title type="text"><![CDATA[TimesFM (Time Series Foundation Model)]]></title>
            <link rel="alternate" href="https://github.com/google-research/timesfm" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10111"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. 

Related contents:

- [Google sort TimesFM, son modèle IA qui prédit l&amp;#039;avenir des séries temporelles @ Korben :fr:](https://korben.info/google-timesfm-modele-prevision.html).]]>
            </summary>
            <updated>2025-09-11T06:20:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10094</id>
            <title type="text"><![CDATA[PyTorch]]></title>
            <link rel="alternate" href="https://pytorch.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10094"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Tensors and Dynamic neural networks in Python with strong GPU acceleration.

PyTorch is a Python package that provides two high-level features:

- Tensor computation (like NumPy) with strong GPU acceleration
- Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

- [PyTorch @ GitHub](https://github.com/pytorch/pytorch).

Related contents:

- [Intro to PyTorch. Easy to follow, visual introduction. @ 0byte.io](https://0byte.io/articles/pytorch_introduction.html).
- [Faire tourner un LLM localement sur votre ordinateur @ Quoi de neuf les devs ? :fr:](https://happytodev.substack.com/p/brent-roose-est-linvite-du-n147-de?open=false#%C2%A7faire-tourner-un-llm-localement-sur-votre-ordinateur).
- [the bug that taught me more about PyTorch than years of using it  @ matmols](https://elanapearl.github.io/blog/2025/the-bug-that-taught-me-pytorch/).
- [The annotated PyTorch training loop @ idlemachines](https://idlemachines.co.uk/essays/pytorch-training-loop).]]>
            </summary>
            <updated>2026-06-26T11:36:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10037</id>
            <title type="text"><![CDATA[PassGAN]]></title>
            <link rel="alternate" href="https://github.com/brannondorsey/PassGAN" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10037"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Deep Learning Approach for Password Guessing.

Related contents:

- [BruteForceAI - L&amp;#039;IA qui cracke vos mots de passe @ Korben :fr:](https://korben.info/bruteforceai.html).
- [BruteForceAI: Free AI-powered login brute force tool @ Help Net Security](https://www.helpnetsecurity.com/2025/09/03/bruteforceai-free-ai-powered-login-brute-force-tool/).]]>
            </summary>
            <updated>2025-09-08T10:01:56+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11</id>
            <title type="text"><![CDATA[Enhance Lab :fr:]]></title>
            <link rel="alternate" href="https://enhancelab.fr/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI and inverse problems for a revolution in digital photography.

Related contents:

- [S5E21 - On a reçu le génie français qui révolutionne la vision artificielle
 @ Underscore_ :fr:](https://shows.acast.com/micode-underscore/episodes/on-a-recu-le-genie-francais-qui-revolutionne-la-vision-artif).]]>
            </summary>
            <updated>2025-09-03T10:04:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/44</id>
            <title type="text"><![CDATA[spaCy]]></title>
            <link rel="alternate" href="https://spacy.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/44"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[💫 Industrial-strength Natural Language Processing (NLP) in Python.

spaCy is a library for advanced Natural Language Processing in Python and Cython. It&amp;#039;s built on the very latest research, and was designed from day one to be used in real products.

spaCy comes with pretrained pipelines and currently supports tokenization and training for 70+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license.

- [spaCy @ GitHub](https://github.com/explosion/spaCy).

Related contents:

- [Embedding Millions of Text Documents With Qwen3 @ daft](https://www.daft.ai/blog/embedding-millions-of-text-documents-with-qwen3).
- [Learn How to Use Transformers with HuggingFace and SpaCy @ towards data science](https://towardsdatascience.com/mastering-nlp-with-spacy-part-4/).]]>
            </summary>
            <updated>2025-09-18T05:52:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/46</id>
            <title type="text"><![CDATA[Qwen/Qwen3-Embedding-0.6B @ Hugging Face]]></title>
            <link rel="alternate" href="https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/46"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.

Related contents:

- [Embedding Millions of Text Documents With Qwen3 @ daft](https://www.daft.ai/blog/embedding-millions-of-text-documents-with-qwen3).]]>
            </summary>
            <updated>2025-09-04T08:33:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/483</id>
            <title type="text"><![CDATA[Monarch]]></title>
            <link rel="alternate" href="https://github.com/pytorch-labs/monarch" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/483"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[PyTorch Single Controller.

Monarch is a distributed execution engine for PyTorch. Our overall goal is to deliver the high-quality user experience that people get from single-GPU PyTorch, but at cluster scale.]]>
            </summary>
            <updated>2025-08-28T17:18:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/500</id>
            <title type="text"><![CDATA[Mojo 🔥]]></title>
            <link rel="alternate" href="https://www.modular.com/mojo" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/500"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Powerful CPU+GPU Programming.
Mojo   is a pythonic language for blazing-fast CPU+GPU execution without CUDA. Optionally use it with MAX for insanely fast AI inference.

- [Modular Platform @ GitHub](https://github.com/modular/modular).

Related contents:

- [Python can run Mojo now @  koaning.io](https://koaning.io/posts/giving-mojo-a-spin/).]]>
            </summary>
            <updated>2025-08-28T17:21:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/521</id>
            <title type="text"><![CDATA[Featureform]]></title>
            <link rel="alternate" href="https://www.featureform.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/521"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Data Layer for Agentic Enrichment and ML Features. The Virtual Feature Store. Turn your existing data infrastructure into a feature store. 

Featureform is a virtual feature store. It enables data scientists to define, manage, and serve their ML model&amp;#039;s features. Featureform sits atop your existing infrastructure and orchestrates it to work like a traditional feature store. By using Featureform, a data science team can solve the following organizational problems:

- [Featureform @ GitHub](https://github.com/featureform/featureform).]]>
            </summary>
            <updated>2025-08-28T17:25:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/522</id>
            <title type="text"><![CDATA[KServe]]></title>
            <link rel="alternate" href="https://kserve.github.io/website/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/522"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Standardized Serverless ML Inference Platform on Kubernetes.
Highly scalable and standards based
Model Inference Platform on Kubernetes
for Trusted AI.

KServe provides a Kubernetes Custom Resource Definition for serving predictive and generative machine learning (ML) models. It aims to solve production model serving use cases by providing high abstraction interfaces for Tensorflow, XGBoost, ScikitLearn, PyTorch, Huggingface Transformer/LLM models using standardized data plane protocols.

- [KServe @ GitHub](https://github.com/kserve/kserve).

Related contents:

- [KServe becomes a CNCF incubating project @ CNCF Blog](https://www.cncf.io/blog/2025/11/11/kserve-becomes-a-cncf-incubating-project/).]]>
            </summary>
            <updated>2025-11-12T14:37:18+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/660</id>
            <title type="text"><![CDATA[sports]]></title>
            <link rel="alternate" href="https://github.com/roboflow/sports" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/660"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[computer vision and sports.

In sports, every centimeter and every second matter. That&amp;#039;s why Roboflow decided to use sports as a testing ground to push our object detection, image segmentation, keypoint detection, and foundational models to their limits. This repository contains reusable tools that can be applied in sports and beyond.]]>
            </summary>
            <updated>2025-08-28T17:48:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/699</id>
            <title type="text"><![CDATA[micrograd]]></title>
            <link rel="alternate" href="https://github.com/karpathy/micrograd?tab=readme-ov-file" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/699"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API.

A tiny Autograd engine (with a bite! :)). Implements backpropagation (reverse-mode autodiff) over a dynamically built DAG and a small neural networks library on top of it with a PyTorch-like API. Both are tiny, with about 100 and 50 lines of code respectively. The DAG only operates over scalar values, so e.g. we chop up each neuron into all of its individual tiny adds and multiplies. However, this is enough to build up entire deep neural nets doing binary classification, as the demo notebook shows. Potentially useful for educational purposes.

Related contents:

- [Writing that changed how I think about PL @ Max Bernstein](https://bernsteinbear.com/blog/pl-writing/).]]>
            </summary>
            <updated>2025-08-28T17:54:18+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/735</id>
            <title type="text"><![CDATA[Ktransformers]]></title>
            <link rel="alternate" href="https://kvcache-ai.github.io/ktransformers/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/735"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Flexible Framework for Experiencing Cutting-edge LLM Inference Optimizations.

 KTransformers, pronounced as Quick Transformers, is designed to enhance your 🤗 Transformers experience with advanced kernel optimizations and placement/parallelism strategies.

KTransformers is a flexible, Python-centric framework designed with extensibility at its core. By implementing and injecting an optimized module with a single line of code, users gain access to a Transformers-compatible interface, RESTful APIs compliant with OpenAI and Ollama, and even a simplified ChatGPT-like web UI. 

- [Ktransformers @ GitHub](https://github.com/kvcache-ai/ktransformers).]]>
            </summary>
            <updated>2025-08-28T18:00:14+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/765</id>
            <title type="text"><![CDATA[ANEMLL]]></title>
            <link rel="alternate" href="https://github.com/Anemll/Anemll" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/765"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Artificial Neural Engine Machine Learning Library.

ANEMLL (pronounced like &amp;quot;animal&amp;quot;) is an open-source project focused on accelerating the porting of Large Language Models (LLMs) to tensor processors, starting with the Apple Neural Engine (ANE).]]>
            </summary>
            <updated>2025-08-28T18:06:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/780</id>
            <title type="text"><![CDATA[Hyperparam]]></title>
            <link rel="alternate" href="https://hyperparam.app/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/780"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Look At Your Data 👀.

Data quality is the most important factor in machine learning success. Hyperparam brings exploration and analysis of massive text datasets to the browser.

- [Hyperparam @ GitHub](https://github.com/hyparam).]]>
            </summary>
            <updated>2025-08-28T18:07:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/790</id>
            <title type="text"><![CDATA[CoRT (Chain of Recursive Thoughts) 🧠🔄]]></title>
            <link rel="alternate" href="https://github.com/PhialsBasement/Chain-of-Recursive-Thoughts" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/790"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[I made my AI think harder by making it argue with itself repeatedly. It works stupidly well. 

CoRT makes AI models recursively think about their responses, generate alternatives, and pick the best one. It&amp;#039;s like giving the AI the ability to doubt itself and try again... and again... and again.]]>
            </summary>
            <updated>2025-08-28T18:10:22+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/813</id>
            <title type="text"><![CDATA[node-mlx]]></title>
            <link rel="alternate" href="https://github.com/frost-beta/node-mlx" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/813"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A machine learning framework for Node.js, based on MLX.]]>
            </summary>
            <updated>2025-08-28T18:14:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/823</id>
            <title type="text"><![CDATA[Boson]]></title>
            <link rel="alternate" href="https://github.com/bosonstack/boson" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/823"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A self-contained, lightweight and OOB research platform for modern ML.

Boson is a lightweight, fully containerized, and feature-rich machine learning research platform. It centralizes essential tools to help teams keep projects lean, organized, and reproducible—while reducing overhead and boosting productivity. Think Databricks/Sagemaker but local and free.

Boson enables engineers and researchers to iterate faster without getting bogged down by infrastructure or tooling complexity.]]>
            </summary>
            <updated>2025-08-28T18:14:29+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1151</id>
            <title type="text"><![CDATA[xorq]]></title>
            <link rel="alternate" href="https://www.xorq.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1151"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[ML Pipelines From Another Planet.Build out-of-this-world ML pipelines.

Run-anywhere computational framework for Python that simplifies and accelerates ML workflows and development. 
xorq is a deferred computational framework for building, running, and serving pandas groupby-apply style pipelines common in ML workflows. xorq is built on top of Ibis and Apache DataFusion.

- [xorq @ GitHub](https://github.com/xorq-labs/xorq).
- [xorq documentation](https://docs.xorq.dev/overview).]]>
            </summary>
            <updated>2025-08-28T19:07:53+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1191</id>
            <title type="text"><![CDATA[Jobset]]></title>
            <link rel="alternate" href="https://jobset.sigs.k8s.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1191"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[JobSet: a k8s native API for distributed ML training and HPC workloads 

JobSet is a Kubernetes-native API for managing a group of k8s Jobs as a unit. It aims to offer a unified API for deploying HPC (e.g., MPI) and AI/ML training workloads (PyTorch, Jax, Tensorflow etc.) on Kubernetes.

- [JobSet @ GitHub](https://github.com/kubernetes-sigs/jobset).

Related contents:

- [Introducing JobSet @ Kubernetes blog](https://kubernetes.io/blog/2025/03/23/introducing-jobset/).]]>
            </summary>
            <updated>2025-08-28T19:14:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1240</id>
            <title type="text"><![CDATA[NVIDIA Dynamo]]></title>
            <link rel="alternate" href="https://developer.nvidia.com/dynamo" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1240"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Datacenter Scale Distributed Inference Serving Framework.

NVIDIA Dynamo is a high-throughput low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments. Dynamo is designed to be inference engine agnostic (supports TRT-LLM, vLLM, SGLang or others) and captures LLM-specific capabilities.

- [Dynamo @ GitHub](https://github.com/ai-dynamo/dynamo).

Related contents:

- [A closer look at Dynamo, Nvidia&amp;#039;s &amp;#039;operating system&amp;#039; for AI inference @ The register](https://www.theregister.com/2025/03/23/nvidia_dynamo/).]]>
            </summary>
            <updated>2025-08-28T19:23:04+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1285</id>
            <title type="text"><![CDATA[Flower]]></title>
            <link rel="alternate" href="https://flower.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1285"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Friendly Federated AI Framework.

A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language.

- [Flower @ GitHub](https://github.com/adap/flower).]]>
            </summary>
            <updated>2025-08-28T19:31:05+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1317</id>
            <title type="text"><![CDATA[Kokoro Web]]></title>
            <link rel="alternate" href="https://voice-generator.pages.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1317"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Free &amp;amp; Open-Source AI Voice Generator.

 A powerful, browser-based AI voice generator that lets you create natural-sounding voices without installing anything.

Use it directly in your browser or self-host it for your own applications with OpenAI API compatibility! 

- [Kokoro Web @ GitHub](https://github.com/eduardolat/kokoro-web).]]>
            </summary>
            <updated>2025-08-28T19:35:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1324</id>
            <title type="text"><![CDATA[WAGMIOS]]></title>
            <link rel="alternate" href="https://github.com/mentholmike/wagmios" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1324"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[WAGMIOS is a self-hosted container management system with AI-powered automation.
It enables you to efficiently manage your containers with W.I.L.L.O.W, an AI assistant that optimizes your workflow.]]>
            </summary>
            <updated>2025-08-28T19:37:05+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1334</id>
            <title type="text"><![CDATA[Evolving Agents Framework]]></title>
            <link rel="alternate" href="https://github.com/matiasmolinas/evolving-agents" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1334"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Evolving agents is a production-grade environment for orchestrating, evolving, and managing AI agents.

A production-grade framework for creating, managing, and evolving AI agents with intelligent agent-to-agent communication. The framework enables you to build collaborative agent ecosystems that can semantically understand requirements, evolve based on past experiences, and communicate effectively to solve complex tasks.]]>
            </summary>
            <updated>2025-08-28T19:39:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1338</id>
            <title type="text"><![CDATA[Letta]]></title>
            <link rel="alternate" href="https://www.letta.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1338"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Platform for Building Stateful Agents.
Build agents with infinite context and human-like memory, that can learn from data and improve with experience.
 Letta (formerly MemGPT) is a framework for creating LLM services with memory. 

👾 Letta is an open source framework for building stateful LLM applications. You can use Letta to build stateful agents with advanced reasoning capabilities and transparent long-term memory. The Letta framework is white box and model-agnostic.

- [Letta @ GitHub](https://github.com/letta-ai/letta).
- [Letta documentation](https://docs.letta.com/).

Related contents:

- [Letta Filesystem @ Letta documentation](https://docs.letta.com/guides/agents/filesystem).]]>
            </summary>
            <updated>2025-10-17T05:38:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1342</id>
            <title type="text"><![CDATA[CAMEL-AI Framework]]></title>
            <link rel="alternate" href="https://camel-ai.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1342"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Finding the Scaling Laws of Agents. The first and the best multi-agent framework.

 🐫 CAMEL is an open-source community dedicated to finding the scaling laws of agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.

The framework enables multi-agent systems to continuously evolve by generating data and interacting with environments. This evolution can be driven by reinforcement learning with verifiable rewards or supervised learning.

- [CAMEL-AI @ GitHub](https://github.com/camel-ai/camel).]]>
            </summary>
            <updated>2025-08-28T19:40:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1409</id>
            <title type="text"><![CDATA[superglue]]></title>
            <link rel="alternate" href="https://superglue.cloud/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1409"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[superglue is an open-source server that sits as a layer between complex APIs and your application. With superglue, you always get the data that you want in the format that you expect. Fetch data from JSON and XML APIs, as well as CSV and Excel files in seconds. 

- [superglue @ GitHub](https://github.com/superglue-ai/superglue).]]>
            </summary>
            <updated>2025-08-28T19:51:17+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1422</id>
            <title type="text"><![CDATA[Spaces @ Hugging Face]]></title>
            <link rel="alternate" href="https://huggingface.co/spaces" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1422"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The AI App Directory.

Related contents:

- [\#106 - Les news web dev pour février 2025 @ Double Slash :fr:](https://double-slash.dev/podcasts/news-feb25/).]]>
            </summary>
            <updated>2025-08-28T19:53:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1505</id>
            <title type="text"><![CDATA[DeepEval]]></title>
            <link rel="alternate" href="https://docs.confident-ai.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1505"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Open-Source LLM Evaluation Framework.

DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that runs locally on your machine for evaluation.

- [DeepEval @ GitHub](https://github.com/confident-ai/deepeval).]]>
            </summary>
            <updated>2025-08-28T20:07:27+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1508</id>
            <title type="text"><![CDATA[GenSX]]></title>
            <link rel="alternate" href="https://www.gensx.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1508"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The TypeScript framework for agents &amp;amp; workflows with react-like components. Lightning fast dev loop. Easy to learn. Easy to extend. 

Build complex AI applications with React-like components.
GenSX is a simple typescript framework for building agents and workflows with reusable React-like components.
GenSX takes a lot of inspiration from React, but the programming model is very different - it’s a Node.js framework designed for data flow.

- [GenSX @ GitHub](https://github.com/gensx-inc/gensx).]]>
            </summary>
            <updated>2025-08-28T20:07:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1520</id>
            <title type="text"><![CDATA[Zonos]]></title>
            <link rel="alternate" href="https://github.com/Zyphra/Zonos" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1520"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Zonos-v0.1 is a leading open-weight text-to-speech model trained on more than 200k hours of varied multilingual speech, delivering expressiveness and quality on par with—or even surpassing—top TTS providers. 

Our model enables highly natural speech generation from text prompts when given a speaker embedding or audio prefix, and can accurately perform speech cloning when given a reference clip spanning just a few seconds. The conditioning setup also allows for fine control over speaking rate, pitch variation, audio quality, and emotions such as happiness, fear, sadness, and anger. The model outputs speech natively at 44kHz.

Related contents:

- [Zonos, l’IA ultime pour cloner une voix ? @ Choses à Savoir TECH :fr:](https://shows.acast.com/choses-a-savoir-technologie/episodes/zonos-lia-ultime-pour-cloner-une-voix).]]>
            </summary>
            <updated>2025-08-28T20:09:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1576</id>
            <title type="text"><![CDATA[BirdNET-Analyzer]]></title>
            <link rel="alternate" href="https://kahst.github.io/BirdNET-Analyzer/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1576"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[BirdNET-Analyzer is an open source tool for analyzing bird calls using machine learning models. It can process large amounts of audio recordings and identify (bird) species based on their calls.

- [BirdNET-Analyzer @ GitHub](https://github.com/kahst/BirdNET-Analyzer).]]>
            </summary>
            <updated>2025-08-28T20:19:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1580</id>
            <title type="text"><![CDATA[OptaPlanner]]></title>
            <link rel="alternate" href="https://www.optaplanner.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1580"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The fast, Open Source and easy-to-use solver.
Solve planning and scheduling problems with OptaPlanner.

A fast, easy-to-use, open source AI constraint solver for software developers

- [OptaPlanner @ GitHub](https://github.com/kiegroup/optaplanner).

Related contents:

- [How I built an AI company to save my open source project @ timefold](https://timefold.ai/blog/how-i-built-an-ai-company-to-save-my-open-source-project).]]>
            </summary>
            <updated>2025-08-28T20:19:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1590</id>
            <title type="text"><![CDATA[smolmodels ✨]]></title>
            <link rel="alternate" href="https://github.com/plexe-ai/smolmodels" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1590"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[build ml models in natural language and minimal code.

Create machine learning models with minimal code by describing what you want them to do in plain words. You explain the task, and the library builds a model for you, including data generation, feature engineering, training, and packaging.]]>
            </summary>
            <updated>2025-08-28T20:21:29+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1606</id>
            <title type="text"><![CDATA[Open LLM Lists]]></title>
            <link rel="alternate" href="https://openllmlist.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1606"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Trending Open AI Models.]]>
            </summary>
            <updated>2025-08-28T20:23:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1611</id>
            <title type="text"><![CDATA[Modern-Day Oracles or Bullshit Machines ?]]></title>
            <link rel="alternate" href="https://thebullshitmachines.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1611"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[For better or for worse, LLMs are here to stay. We all read content that they produce online, most of us interact with LLM chatbots, and many of us use them to produce content of our own.

In a series of five- to ten-minute lessons, we will explain what these machines are, how they work, and how to thrive in a world where they are everywhere.

You will learn when these systems can save you a lot of time and effort. You will learn when they are likely to steer you wrong. And you will discover how to see through the hype to tell the difference. ?]]>
            </summary>
            <updated>2025-08-28T20:24:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1615</id>
            <title type="text"><![CDATA[AI by Hand ✍️ Exercises in Excel]]></title>
            <link rel="alternate" href="https://github.com/ImagineAILab/ai-by-hand-excel" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1615"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI by Hand ✍️ Exercises in Excel]]>
            </summary>
            <updated>2025-08-28T20:25:32+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1677</id>
            <title type="text"><![CDATA[How To Scale Your Model]]></title>
            <link rel="alternate" href="https://jax-ml.github.io/scaling-book/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1677"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Systems View of LLMs on TPUs.

This book aims to demystify the art of scaling LLMs on TPUs. We try to explain how TPUs work, how LLMs actually run at scale, and how to pick parallelism schemes during training and inference that avoid communication bottlenecks.

- [How To Scale Your Model @ GitHub](https://github.com/jax-ml/scaling-book/).]]>
            </summary>
            <updated>2025-08-28T20:35:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1698</id>
            <title type="text"><![CDATA[Oumi]]></title>
            <link rel="alternate" href="https://oumi.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1698"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open Universal Machine Intellingence.
E2E Foundation Model Research Platform.
Everything you need to build state-of-the-art foundation models, end-to-end. 

Oumi is a fully open-source platform that streamlines the entire lifecycle of foundation models - from data preparation and training to evaluation and deployment. Whether you&amp;#039;re developing on a laptop, launching large scale experiments on a cluster, or deploying models in production, Oumi provides the tools and workflows you need.

- [Oumi @ GitHub](https://github.com/oumi-ai/oumi).]]>
            </summary>
            <updated>2025-08-28T20:39:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1783</id>
            <title type="text"><![CDATA[🤗 Transformers]]></title>
            <link rel="alternate" href="https://huggingface.co/docs/transformers/index" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1783"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX.

🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. 

- [🤗 Transformers @ GitHub](https://github.com/huggingface/transformers).

Related contents:

- [Running inference in web extensions @ dist://ed](https://blog.mozilla.org/en/mozilla/ai/ai-tech/running-inference-in-web-extensions/).]]>
            </summary>
            <updated>2025-08-28T20:53:50+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1784</id>
            <title type="text"><![CDATA[ONNX Runtime]]></title>
            <link rel="alternate" href="https://onnxruntime.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1784"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator.

ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. 

- [ONNX Runtime @ GitHub](https://github.com/microsoft/onnxruntime).

Related contents:

- [Running inference in web extensions @ dist://ed](https://blog.mozilla.org/en/mozilla/ai/ai-tech/running-inference-in-web-extensions/).]]>
            </summary>
            <updated>2025-08-28T20:53:50+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1834</id>
            <title type="text"><![CDATA[vLLM]]></title>
            <link rel="alternate" href="https://vllm.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1834"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Easy, fast, and cheap LLM serving for everyone.

vLLM is a fast and easy-to-use library for LLM inference and serving.

Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evloved into a community-driven project with contributions from both academia and industry.

- [vLLM @ GitHub](https://github.com/vllm-project/vllm).

Related contents:

- [How to serve LLMs with vLLM and OVHcloud AI Deploy @ OVHcloud](https://blog.ovhcloud.com/how-to-serve-llms-with-vllm-and-ovhcloud-ai-deploy/).
- [Episode 616: From Boston to bootc @ Linux Unplugged](https://linuxunplugged.com/616).
- [What is vLLM @ RedHat](https://www.redhat.com/en/topics/ai/what-is-vllm).
- [Faire tourner un LLM localement sur votre ordinateur @ Quoi de neuf les devs ? :fr:](https://happytodev.substack.com/p/brent-roose-est-linvite-du-n147-de?open=false#%C2%A7faire-tourner-un-llm-localement-sur-votre-ordinateur).
- [Inside vLLM: Anatomy of a High-Throughput LLM Inference System @ Aleksa Gordić blog](https://www.aleksagordic.com/blog/vllm).
- [vLLM : Maîtriser l&amp;#039;Inference Haute Performance pour les LLM @ DevSecOps :fr:](https://blog.stephane-robert.info/docs/developper/programmation/python/vllm/).
- [Docker Model Runner now supports vLLM on Windows @ Docker](https://www.docker.com/blog/docker-model-runner-vllm-windows/).
- [Running a self-hosted LLM in Kubernetes with vLLM @ CNCF](https://www.cncf.io/blog/2026/07/16/running-a-self-hosted-llm-in-kubernetes-with-vllm/).]]>
            </summary>
            <updated>2026-07-21T06:33:51+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1835</id>
            <title type="text"><![CDATA[Common Crawl]]></title>
            <link rel="alternate" href="https://commoncrawl.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1835"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open Repository of Web Crawl Data.

Common Crawl maintains a free, open repository of web crawl data that can be used by anyone.

Related contents:

- [S5E7 - Sommes-nous à l&amp;#039;aube d&amp;#039;un effondrement des IA ? @ Underscore_&amp;#039;s acast :fr:](https://shows.acast.com/micode-underscore/episodes/s5e7-sommes-nous-a-laube-dun-effondrement-des-ia).]]>
            </summary>
            <updated>2025-08-28T21:01:56+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1836</id>
            <title type="text"><![CDATA[FineWeb]]></title>
            <link rel="alternate" href="https://huggingface.co/datasets/HuggingFaceFW/fineweb" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1836"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[15 trillion tokens of the finest data the 🌐 web has to offer.

The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library.

🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full dataset under the ODC-By 1.0 license. However, by carefully adding additional filtering steps, we managed to push the performance of 🍷 FineWeb well above that of the original 🦅 RefinedWeb, and models trained on our dataset also outperform models trained on other commonly used high quality web datasets (like C4, Dolma-v1.6, The Pile, SlimPajama, RedPajam2) on our aggregate group of benchmark tasks.

Related contents:

- [🍷 FineWeb: decanting the web for the finest text data at scale @ HugginFace](https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1).
- [S5E7 - Sommes-nous à l&amp;#039;aube d&amp;#039;un effondrement des IA ? @ Underscore_&amp;#039;s acast :fr:](https://shows.acast.com/micode-underscore/episodes/s5e7-sommes-nous-a-laube-dun-effondrement-des-ia).]]>
            </summary>
            <updated>2025-08-28T21:01:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1843</id>
            <title type="text"><![CDATA[Materia AI]]></title>
            <link rel="alternate" href="https://www.trymateria.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1843"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Partner of Accounting Leaders. Generative AI platform for intelligent accounting.

The preferred partner of accounting leaders.

Related contents:

- [\#304.bin - Bilan 2024: Le début de la révolution avec Quentin Adam @ &amp;lt;ifttd&amp;gt;](https://www.ifttd.io/episodes/bilan-2024).]]>
            </summary>
            <updated>2025-08-28T21:03:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1846</id>
            <title type="text"><![CDATA[NeurIPS Conference]]></title>
            <link rel="alternate" href="https://neurips.cc/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1846"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Annual Conference on Neural Information Processing Systems.

Related content:

- [S5E6 - On reçoit le PDG de Mistral, Arthur Mensch @ Underscore_&amp;#039;s acast :fr:](https://shows.acast.com/micode-underscore/episodes/s5e6-on-recoit-le-pdg-de-mistral-arthur-mensch).]]>
            </summary>
            <updated>2025-08-28T21:04:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1862</id>
            <title type="text"><![CDATA[BrowserAI]]></title>
            <link rel="alternate" href="https://github.com/sauravpanda/BrowserAI" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1862"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Run local LLMs inside your browser.]]>
            </summary>
            <updated>2025-08-28T21:07:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1875</id>
            <title type="text"><![CDATA[Replicate]]></title>
            <link rel="alternate" href="https://replicate.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1875"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Run AI with an API.
Run and fine-tune open-source models. Deploy custom models at scale. All with one line of code.

Thousands of models contributed by our community.
All the latest open-source models are on Replicate.
They’re not just demos — they all actually work and have production-ready APIs.

AI shouldn’t be locked up inside academic papers and demos. Make it real by pushing it to Replicate.

Related contents:

- [\#104 Développer des projets IA - introduction @ Double Slash :fr:](https://double-slash.dev/podcasts/devia1-1/).]]>
            </summary>
            <updated>2025-08-28T21:09:00+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1883</id>
            <title type="text"><![CDATA[Groq]]></title>
            <link rel="alternate" href="https://groq.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1883"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Groq is Fast AI Inference.

Related contents:

- [\#104 Développer des projets IA - introduction @ Double Slash :fr:](https://double-slash.dev/podcasts/devia1-1/).]]>
            </summary>
            <updated>2025-08-28T21:09:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1884</id>
            <title type="text"><![CDATA[LM Studio]]></title>
            <link rel="alternate" href="https://lmstudio.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1884"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Discover, download, and run local LLMs.

- [LM Studio @ GitHub](https://github.com/lmstudio-ai).

Related contents:

- [\#104 Développer des projets IA - introduction @ Double Slash :fr:](https://double-slash.dev/podcasts/devia1-1/).
- [Faire tourner un LLM localement sur votre ordinateur @ Quoi de neuf les devs ? :fr:](https://happytodev.substack.com/p/brent-roose-est-linvite-du-n147-de?open=false#%C2%A7faire-tourner-un-llm-localement-sur-votre-ordinateur).
- [Drames et dramas d’août @ Le RDV Tech podcast](https://frenchspin.fr/2025/08/drames-et-dramas-daout-rdv-tech/).
- [LM Studio : Faire tourner son IA (LLM) facilement (Chat, Developpement, ...) @ Adrien Linuxtricks&amp;#039; YouTube :fr:](https://www.youtube.com/watch?v=FFEzgPG1AZM).]]>
            </summary>
            <updated>2025-10-21T06:18:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1897</id>
            <title type="text"><![CDATA[yek]]></title>
            <link rel="alternate" href="https://github.com/bodo-run/yek" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1897"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A fast tool to read text-based files in a repository or directory, chunk them, and serialize them for LLM consumption.]]>
            </summary>
            <updated>2025-08-28T21:12:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1898</id>
            <title type="text"><![CDATA[Agent Recipes]]></title>
            <link rel="alternate" href="https://www.agentrecipes.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1898"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Explore Agent Recipes

Explore common agent recipes with ready to copy code to improve your LLM applications. 

Related contents:

- [Building effective agents @ Anthropic](https://www.anthropic.com/research/building-effective-agents).]]>
            </summary>
            <updated>2025-08-28T21:13:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1919</id>
            <title type="text"><![CDATA[SPAR3D: Stable Point Aware 3D]]></title>
            <link rel="alternate" href="https://platform.stability.ai/docs/api-reference#tag/3D/paths/~1v2beta~13d~1stable-point-aware-3d/post" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1919"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Stable Point Aware 3D (SPAR3D) can make real-time edits and create the complete structure of a 3D object from a single image in a few seconds. SPAR3D combines the strengths of point-cloud diffusion (probabilistic) and mesh regression (deterministic) to have improved details on the unseen back regions in the input image. 

- [Stable Point Aware 3D @ GitHub](https://github.com/Stability-AI/stable-point-aware-3d).
- [SPAR3D: Stable Point Aware 3D @ Hugging Face](https://huggingface.co/stabilityai/stable-point-aware-3d).

Related contents:

- [Introducing Stable Point Aware 3D: Real-Time Editing and Complete Object Structure Generation @ stability.ai](https://stability.ai/news/stable-point-aware-3d).
- [Supplément: Vidéos de minions gores @ Tech Café :fr:](https://techcafe.fr/videos-de-minions-gores/).]]>
            </summary>
            <updated>2025-08-28T21:16:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1950</id>
            <title type="text"><![CDATA[structured-logprobs]]></title>
            <link rel="alternate" href="https://arena-ai.github.io/structured-logprobs/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1950"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[structured-logprobs is an open-source Python library that enhances OpenAI&amp;#039;s structured outputs by providing detailed information about token log probabilities.

This library is designed to offer valuable insights into the reliability of an LLM&amp;#039;s structured outputs. It works with OpenAI&amp;#039;s Structured Outputs, a feature that ensures the model consistently generates responses adhering to a supplied JSON Schema. This eliminates concerns about missing required keys or hallucinating invalid values.

- [structured-logprobs @ GitHub](https://github.com/arena-ai/structured-logprobs).]]>
            </summary>
            <updated>2025-08-28T21:21:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1970</id>
            <title type="text"><![CDATA[GPU Glossary]]></title>
            <link rel="alternate" href="https://modal.com/gpu-glossary/readme" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1970"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[We wrote this glossary to solve a problem we ran into working with GPUs here at Modal : the documentation is fragmented, making it difficult to connect concepts at different levels of the stack, like Streaming Multiprocessor Architecture , Compute Capability , and nvcc compiler flags .]]>
            </summary>
            <updated>2025-08-28T21:24:22+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1974</id>
            <title type="text"><![CDATA[Lucie LLM]]></title>
            <link rel="alternate" href="https://huggingface.co/collections/OpenLLM-France/lucie-llm-67099ba7b992dee2c32b1f92" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1974"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open weights LLM for French, English, German, Spanish and Italian.

- [Lucie Training @ GitHub](https://github.com/OpenLLM-France/Lucie-Training).
 
Related contents:

- [C’est un grand jour pour LINAGORA et la communauté OpenLLM 🇫🇷 🇪🇺-france @ Jean-Pierre LORRE&amp;#039;s LinkedIn](https://www.linkedin.com/posts/jplorre_llm-opensource-ia-activity-7284686154807869440-40OZ/).
- [OpenLLM-France homepage :fr:](https://www.openllm-france.fr/).]]>
            </summary>
            <updated>2025-08-28T21:25:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1975</id>
            <title type="text"><![CDATA[OpenLLM France :fr:]]></title>
            <link rel="alternate" href="https://www.openllm-france.fr/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1975"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[LLM génératifs ouverts et performants.

Le Consortium OpenLLM France réunit 17 acteurs qui se sont rassemblés dans le prolongement de la création de la communauté OpenLLM France qui fédère à ce jour un écosystème de près de 200 entités (laboratoires publics de recherche, fournisseurs potentiels de données, acteurs technologiques spécialisés, fournisseurs de cas d&amp;#039;usage...). Ces acteurs échangent de manière publique et transparente depuis le début de l’été 2023 sur le serveur Discord de la communauté.]]>
            </summary>
            <updated>2025-08-28T21:25:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1980</id>
            <title type="text"><![CDATA[Giskard]]></title>
            <link rel="alternate" href="https://docs.giskard.ai/en/stable/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1980"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🐢 Open-Source Evaluation &amp;amp; Testing for AI &amp;amp; LLM systems.

The testing framework dedicated to ML models, from tabular to LLMs.
Control risks of performance, bias and security issues in AI systems.

- [Giskard @ GitHub](https://github.com/Giskard-AI/giskard).]]>
            </summary>
            <updated>2025-08-28T21:26:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1981</id>
            <title type="text"><![CDATA[JAX]]></title>
            <link rel="alternate" href="https://jax.readthedocs.io/en/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1981"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[High performance array computing.

 Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more 

- [JAX @ GitHub](https://github.com/jax-ml/jax).

Related contents:

- [The PyTorch developer&amp;#039;s guide to JAX fundamentals @ Google Cloud Blog](https://cloud.google.com/blog/products/ai-machine-learning/guide-to-jax-for-pytorch-developers).]]>
            </summary>
            <updated>2025-08-28T21:26:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1985</id>
            <title type="text"><![CDATA[sitefetch]]></title>
            <link rel="alternate" href="https://github.com/egoist/sitefetch" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1985"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Fetch an entire site and save it as a text file (to be used with AI models).]]>
            </summary>
            <updated>2025-08-28T21:28:12+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1989</id>
            <title type="text"><![CDATA[MLOKit]]></title>
            <link rel="alternate" href="https://github.com/xforcered/MLOKit" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1989"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[MLOps Attack Toolkit

MLOKit is a toolkit that can be used to attack MLOps platforms by taking advantage of the available REST API. This tool allows the user to specify an attack module, along with specifying valid credentials (API key or stolen access token) for the respective MLOps platform. The attack modules supported include reconnaissance, data extraction and model extraction. MLOKit was built in a modular approach, so that new modules can be added in the future by the information security community.]]>
            </summary>
            <updated>2025-08-28T21:28:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2038</id>
            <title type="text"><![CDATA[Fast Music Remover]]></title>
            <link rel="alternate" href="https://github.com/omeryusufyagci/fast-music-remover" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2038"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Take control of the media you consume every day with Fast Music Remover!.

A C++ based, lightweight music and noise remover for YouTube and other internet media, using DeepFilterNet for audio enhancement.

We consume, willingly or not, large amounts of media everyday, and that includes content that is emposed on us. Fast Music Remover gives you the choice to opt-out of them without missing out on the core content.

We&amp;#039;re building a feature rich media processor that is efficient, modular and cross platform. It&amp;#039;s being built for you! That means clean APIs for programmers, containerized on GHCR for remote users, with a Web UI providing seamless access to anyone interested!]]>
            </summary>
            <updated>2025-08-28T21:36:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2100</id>
            <title type="text"><![CDATA[Cosmos]]></title>
            <link rel="alternate" href="https://developer.nvidia.com/cosmos" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2100"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[NVIDIA Cosmos™ is a platform of state-of-the-art generative world foundation models, advanced tokenizers, guardrails, and an accelerated data processing and curation pipeline for autonomous vehicles (AVs) and robotics developers.

 Cosmos is a world model development platform that consists of world foundation models, tokenizers and video processing pipeline to accelerate the development of Physical AI at Robotics &amp;amp; AV labs. Cosmos is purpose built for physical AI. The Cosmos repository will enable end users to run the Cosmos models, run inference scripts and generate videos. 

- [Cosmos @ GitHub](https://github.com/NVIDIA/Cosmos).

Related contents:

- [Ep. 476 : Episode du 8 janvier 2025 @ Les Technos :fr:](https://les-technos.lepodcast.fr/les-technos-476-episode-du-8-janvier-2025).]]>
            </summary>
            <updated>2025-08-28T21:46:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2154</id>
            <title type="text"><![CDATA[Neural Networks: Zero to Hero]]></title>
            <link rel="alternate" href="https://github.com/karpathy/nn-zero-to-hero" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2154"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A course on neural networks that starts all the way at the basics. The course is a series of YouTube videos where we code and train neural networks together. The Jupyter notebooks we build in the videos are then captured here inside the lectures directory. Every lecture also has a set of exercises included in the video description. (This may grow into something more respectable).]]>
            </summary>
            <updated>2025-08-28T21:56:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2160</id>
            <title type="text"><![CDATA[MMAudio]]></title>
            <link rel="alternate" href="https://hkchengrex.com/MMAudio/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2160"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[MMAudio generates synchronized audio given video and/or text inputs.

Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis.

- [MMAudio @ GitHub](https://github.com/hkchengrex/MMAudio).

Related contents:

- [MMAudio - Un outil pour synchroniser l&amp;#039;audio et la vidéo @ Korben :fr:](https://korben.info/mmaudio-outil-synchronisation-audio-video.html).]]>
            </summary>
            <updated>2025-08-28T21:56:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2161</id>
            <title type="text"><![CDATA[Galaxy Zoo]]></title>
            <link rel="alternate" href="https://www.zooniverse.org/projects/zookeeper/galaxy-zoo" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2161"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Euclid scientists need your help. Euclid captures images of tens of millions of galaxies like those we’re showing here. To classify that impossibly-large pile of galaxies, we’re using your classifications to train AI algorithms (remember the Zoobot AI assistant?). But the AI algorithms need to be ready for the scientists by the end of August - in only one month! We need as many volunteer classifications for teaching the AI algorithms as we can get; our goal is 100,000 classifications. We’re calling this The Euclid Challenge. Spread the word and dive in!

- [Galaxy Zoo - Aidez les astronomes à classer des millions de galaxies @ Korben :fr:](https://korben.info/galaxy-zoo-classification-galaxies-telescope-euclid.html).]]>
            </summary>
            <updated>2025-08-28T21:56:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2270</id>
            <title type="text"><![CDATA[OpenVINO]]></title>
            <link rel="alternate" href="https://docs.openvino.ai/2024/index.html" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2270"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.

OpenVINO is an open-source toolkit for optimizing and deploying deep learning models from cloud to edge. It accelerates deep learning inference across various use cases, such as generative AI, video, audio, and language with models from popular frameworks like PyTorch, TensorFlow, ONNX, and more. Convert and optimize models, and deploy across a mix of Intel® hardware and environments, on-premises and on-device, in the browser or in the cloud.

- [OpenVINO @ GitHub](https://github.com/openvinotoolkit/openvino).
- [OpenVINO @ Hugging Face](https://huggingface.co/OpenVINO).]]>
            </summary>
            <updated>2026-01-23T10:38:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2274</id>
            <title type="text"><![CDATA[EmoBox]]></title>
            <link rel="alternate" href="https://emo-box.github.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2274"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark.

EmoBox, a groundbreaking multilingual multi-corpus speech emotion recognition (SER) toolkit designed to streamline research in this field. EmoBox is accompanied by a meticulously curated benchmark tailored for both intra-corpus and cross-corpus evaluation settings. 

- [EmoBox @ GitHub](https://github.com/emo-box/emobox).]]>
            </summary>
            <updated>2025-08-28T22:16:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2480</id>
            <title type="text"><![CDATA[QRNet]]></title>
            <link rel="alternate" href="https://github.com/zhaoyuzhi/QRNet" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2480"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[QRNet is a state-of-the-art neural network that transforms the Quad-Bayer pattern into sharp and clean RGB images]]>
            </summary>
            <updated>2025-08-28T22:49:56+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2487</id>
            <title type="text"><![CDATA[brainstorm]]></title>
            <link rel="alternate" href="https://github.com/Invicti-Security/brainstorm/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2487"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A smarter web fuzzing tool that combines local LLM models and ffuf to optimize directory and file discovery.

This tool enhances traditional web fuzzing by using local AI language models (via Ollama) to generate intelligent guesses for potential paths and filenames. 

- [Brainstorm tool release: Optimizing web fuzzing with local LLMs @ invicti](https://www.invicti.com/blog/security-labs/brainstorm-tool-release-optimizing-web-fuzzing-with-local-llms/).]]>
            </summary>
            <updated>2025-08-28T22:50:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2496</id>
            <title type="text"><![CDATA[Automate Presentation Creation with Python]]></title>
            <link rel="alternate" href="https://github.com/gpenessot/Automate-Presentation-Creation-with-Python" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2496"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[This repository contains the code and resources used in the article &amp;quot;Stop Wasting Time, Automate Your Presentation with Python&amp;quot; published on Medium.

- [Stop Wasting Time: Automate Your Presentation with Python @ Gael Penessot&amp;#039;s Medium](https://medium.data-decision.io/stop-wasting-time-automate-your-presentation-with-python-a721fe39c8e5)]]>
            </summary>
            <updated>2025-08-28T22:53:00+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2511</id>
            <title type="text"><![CDATA[🌟 Awesome LLM Apps]]></title>
            <link rel="alternate" href="https://github.com/Shubhamsaboo/awesome-llm-apps" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2511"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Collection of awesome LLM apps with RAG using OpenAI, Anthropic, Gemini and opensource models. 

A curated collection of awesome LLM apps built with RAG and AI agents. This repository features LLM apps that use models from OpenAI, Anthropic, Google, and even open-source models like LLaMA that you can run locally on your computer.]]>
            </summary>
            <updated>2025-08-28T22:54:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2519</id>
            <title type="text"><![CDATA[TorchGeo]]></title>
            <link rel="alternate" href="http://blank" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2519"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data.

TorchGeo is a PyTorch domain library, similar to torchvision, providing datasets, samplers, transforms, and pre-trained models specific to geospatial data.

- [TorchGeo @ GitHub](https://github.com/microsoft/torchgeo).]]>
            </summary>
            <updated>2025-08-28T22:57:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2527</id>
            <title type="text"><![CDATA[mPLUG-DocOwl]]></title>
            <link rel="alternate" href="https://github.com/X-PLUG/mPLUG-DocOwl" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2527"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Powerful Multi-modal LLM Family for OCR-free Document Understanding.
Modularized Multimodal Large Language Model for Document Understanding.]]>
            </summary>
            <updated>2025-08-28T22:58:00+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2531</id>
            <title type="text"><![CDATA[ChainReactor]]></title>
            <link rel="alternate" href="https://github.com/ucsb-seclab/chainreactor" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2531"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[ChainReactor is a research project that leverages AI planning to discover exploitation chains for privilege escalation on Unix systems. The project models the problem as a sequence of actions to achieve privilege escalation from initial access to a target system. 

- [ChainReactor: Automated Privilege Escalation Chain Discovery via AI Planning @ Usenix association](https://www.usenix.org/conference/usenixsecurity24/presentation/de-pasquale).]]>
            </summary>
            <updated>2025-08-28T22:58:03+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2685</id>
            <title type="text"><![CDATA[The Open Interpreter Project]]></title>
            <link rel="alternate" href="https://www.openinterpreter.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2685"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A natural language interface for computers.

Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.

- [Open Interpreter @ GitHub](https://github.com/OpenInterpreter/open-interpreter).]]>
            </summary>
            <updated>2025-08-28T23:25:14+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2711</id>
            <title type="text"><![CDATA[Mastra]]></title>
            <link rel="alternate" href="https://mastra.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2711"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Typescript AI framework.

Mastra is an opinionated Typescript framework that helps you build AI applications and features quickly. It gives you the set of primitives you need: workflows, agents, RAG, integrations, syncs and evals. You can run Mastra on your local machine, or deploy to a serverless cloud.

- [Mastra @ GitHub](https://github.com/mastra-ai/mastra).

Related contents:

- [\#107 - Les news web dev pour mars 2025 @ Double Slash :fr:](https://double-slash.dev/podcasts/news-mar-25/).
- [Mastra 101: Learn how to build agents @ Mastra University](https://mastra.ai/course).]]>
            </summary>
            <updated>2025-08-28T23:29:17+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2722</id>
            <title type="text"><![CDATA[TiDB]]></title>
            <link rel="alternate" href="https://tidb.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2722"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI Assistant. Knowledge Graph based RAG built with TiDB Serverless Vector Storage and LlamaIndex.

An open source GraphRAG (Knowledge Graph) built on top of TiDB Vector and LlamaIndex and DSPy.
pingcap/autoflow is a Graph RAG based and conversational knowledge base tool built with TiDB Serverless Vector Storage.

- [autoflow @ GitHub](https://github.com/pingcap/autoflow).]]>
            </summary>
            <updated>2025-08-28T23:30:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2733</id>
            <title type="text"><![CDATA[AIOps modules]]></title>
            <link rel="alternate" href="https://github.com/awslabs/aiops-modules?tab=readme-ov-file" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2733"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AIOps modules is a collection of reusable Infrastructure as Code (IaC) modules for Machine Learning (ML), Foundation Models (FM), Large Language Models (LLM) and GenAI development and operations on AWS]]>
            </summary>
            <updated>2025-08-28T23:33:18+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2741</id>
            <title type="text"><![CDATA[Pensieve]]></title>
            <link rel="alternate" href="https://github.com/arkohut/pensieve" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2741"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Pensieve is a privacy-focused passive recording project. It can automatically record screen content, build intelligent indices, and provide a convenient web interface to retrieve historical records.

This project draws heavily from two other projects: one called Rewind and another called Windows Recall. However, unlike both of them, Pensieve allows you to have complete control over your data, avoiding the transfer of data to untrusted data centers.]]>
            </summary>
            <updated>2025-08-28T23:33:23+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2770</id>
            <title type="text"><![CDATA[Willow]]></title>
            <link rel="alternate" href="https://heywillow.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2770"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Willow Is a Practical, Open Source, Privacy-focused Platform for Voice Assistants and Other Applications.
Open source, local, and self-hosted Amazon Echo/Google Home competitive Voice Assistant alternative.

Willow is an ESP IDF based project primarily targeting the ESP32-S3-BOX hardware family from Espressif. Our goal is to provide Amazon Echo/Google Home competitive performance, accuracy, cost and functionality with Home Assistant, openHAB and other platforms.

- [Willow @ GitHub](https://github.com/toverainc/willow).]]>
            </summary>
            <updated>2025-08-28T23:38:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2786</id>
            <title type="text"><![CDATA[Ultralytics YOLO]]></title>
            <link rel="alternate" href="https://docs.ultralytics.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2786"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Ultralytics YOLO11 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLO11 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.

- [Ultralytics YOLO @ GitHub](https://github.com/ultralytics/ultralytics).]]>
            </summary>
            <updated>2025-08-28T23:41:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2814</id>
            <title type="text"><![CDATA[Chroma]]></title>
            <link rel="alternate" href="https://www.trychroma.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2814"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[the AI-native open-source embedding database.  The fastest way to build Python or JavaScript LLM apps with memory! 
Chroma is the open-source AI application database. Batteries included.

Embeddings, vector search, document storage, full-text search, metadata filtering, and multi-modal. All in one place. Retrieval that just works. As it should be.

- [Chroma @ GitHub](https://github.com/chroma-core/chroma).

Related contents:

- [ChromaDB: An Open-source vector embedding database @ Futuresmart AI Blog](https://blog.futuresmart.ai/chromadb-an-open-source-vector-embedding-database).
- [How to Build a Local RAG App with Ollama and ChromaDB in the R Programming Language @ freeCodeCamp](https://www.freecodecamp.org/news/build-a-local-rag-app-with-ollama-and-chromadb-in-r/).]]>
            </summary>
            <updated>2026-02-09T16:46:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2819</id>
            <title type="text"><![CDATA[Documind]]></title>
            <link rel="alternate" href="https://www.documind.xyz/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2819"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Extract structured data from PDFs.
Stop wasting time extracting PDFs.
Transform your PDF documents into structured data with Documind. Simple, powerful and open-source.

Documind is an advanced document processing tool that leverages AI to extract structured data from PDFs. It is built to handle PDF conversions, extract relevant information, and format results as specified by customizable schemas.

- [Documind @ GitHub](https://github.com/DocumindHQ/documind/).]]>
            </summary>
            <updated>2025-08-28T23:46:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2820</id>
            <title type="text"><![CDATA[Fast GraphRAG]]></title>
            <link rel="alternate" href="https://github.com/circlemind-ai/fast-graphrag" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2820"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[RAG that intelligently adapts to your use case, data, and queries.

Streamlined and promptable Fast GraphRAG framework designed for interpretable, high-precision, agent-driven retrieval workflows.]]>
            </summary>
            <updated>2025-08-28T23:46:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2880</id>
            <title type="text"><![CDATA[Deep-Live-Cam]]></title>
            <link rel="alternate" href="https://github.com/hacksider/Deep-Live-Cam" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2880"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[real time face swap and one-click video deepfake with only a single image.

This software is intended as a productive contribution to the AI-generated media industry. It aims to assist artists with tasks like animating custom characters or using them as models for clothing, etc.]]>
            </summary>
            <updated>2025-08-28T23:57:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2884</id>
            <title type="text"><![CDATA[PandasAI]]></title>
            <link rel="alternate" href="https://pandas-ai.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2884"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Conversational Data Analysis.

PandasAI is a Python platform that makes it easy to ask questions to your data in natural language. It helps non-technical users to interact with their data in a more natural way, and it helps technical users to save time, and effort when working with data.

PandasAI is a Python library that integrates generative artificial intelligence capabilities into pandas, making dataframes conversational.
Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG. 

- [PandasAI @ GitHub](https://github.com/Sinaptik-AI/pandas-ai).]]>
            </summary>
            <updated>2025-08-28T23:57:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2887</id>
            <title type="text"><![CDATA[LLM Prompt Tuning Playbook]]></title>
            <link rel="alternate" href="https://github.com/varungodbole/prompt-tuning-playbook" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2887"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A playbook for effectively prompting post-trained LLMs.

This document is for anyone who would like to get better at prompting post-trained LLMs. We assume that readers have had some basic interactions with some sort of LLM (e.g. Gemini), but we do not assume a rigorous technical understanding.]]>
            </summary>
            <updated>2025-08-28T23:57:37+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2900</id>
            <title type="text"><![CDATA[Chonkie]]></title>
            <link rel="alternate" href="https://github.com/bhavnicksm/chonkie" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2900"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🦛 CHONK your texts with Chonkie ✨ - The no-nonsense RAG chunking library.
The no-nonsense RAG chunking library that&amp;#039;s lightweight, lightning-fast, and ready to CHONK your texts]]>
            </summary>
            <updated>2025-08-28T23:59:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2960</id>
            <title type="text"><![CDATA[🌐 Browser-Use]]></title>
            <link rel="alternate" href="https://github.com/gregpr07/browser-use" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2960"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open-Source Web Automation library with any LLM.

Let LLMs interact with websites through a simple interface.]]>
            </summary>
            <updated>2025-08-29T00:10:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2978</id>
            <title type="text"><![CDATA[DataChain]]></title>
            <link rel="alternate" href="https://datachain.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2978"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI Data Management at Scale - Curate, Enrich, and Version Datasets.

DataChain is a modern Pythonic data-frame library designed for artificial intelligence. It is made to organize your unstructured data into datasets and wrangle it at scale on your local machine. Datachain does not abstract or hide the AI models and API calls, but helps to integrate them into the postmodern data stack.

Datachain enables multimodal API calls and local AI inferences to run in parallel over many samples as chained operations. The resulting datasets can be saved, versioned, and sent directly to PyTorch and TensorFlow for training. Datachain can persist features of Python objects returned by AI models, and enables vectorized analytical operations over them.

- [DataChain @ GitHub](https://github.com/iterative/datachain).]]>
            </summary>
            <updated>2025-08-29T00:13:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3048</id>
            <title type="text"><![CDATA[ml.js]]></title>
            <link rel="alternate" href="https://github.com/mljs/ml" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3048"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Machine learning tools in JavaScript 

This library is a compilation of the tools developed in the mljs organization.
It is mainly maintained for use in the browser. If you are working with Node.js, you might prefer to add to your dependencies only the libraries that you need, as they are usually published to npm more often.
We prefix all our npm package names with ml- (eg. ml-matrix) so they are easy to find.]]>
            </summary>
            <updated>2025-08-29T00:24:27+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3050</id>
            <title type="text"><![CDATA[Zerox OCR]]></title>
            <link rel="alternate" href="https://github.com/getomni-ai/zerox" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3050"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Zero shot pdf OCR with gpt-4o-mini.

A dead simple way of OCR-ing a document for AI ingestion. Documents are meant to be a visual representation after all. With weird layouts, tables, charts, etc. The vision models just make sense!]]>
            </summary>
            <updated>2025-08-29T00:24:29+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3054</id>
            <title type="text"><![CDATA[Taipy]]></title>
            <link rel="alternate" href="https://taipy.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3054"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Build Python Data &amp;amp; AI web applications.
Turns Data and AI algorithms into production-ready web applications in no time. 

Taipy is designed for data scientists and machine learning engineers to build data &amp;amp; AI web applications.  

From simple pilots to production-ready web applications in no time. No more compromise on performance, customization, and scalability.

- [Taipy @ GitHub](https://github.com/Avaiga/taipy).]]>
            </summary>
            <updated>2025-08-29T00:25:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3064</id>
            <title type="text"><![CDATA[Outlines]]></title>
            <link rel="alternate" href="https://dottxt-ai.github.io/outlines/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3064"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Structured text generation and robust prompting for language models.

Outlines is a Python library that allows you to use Large Language Model in a simple and robust way (with structured generation). It is built by .txt, and is already used in production by many companies.

- [Outlines @ GitHub](https://github.com/dottxt-ai/outlines).

Related contents:

- [7 Lessons from building a small-scale AI application @ Richard Li](https://www.thelis.org/blog/lessons-from-ai).]]>
            </summary>
            <updated>2025-08-29T00:28:23+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3069</id>
            <title type="text"><![CDATA[Amphion]]></title>
            <link rel="alternate" href="https://openhlt.github.io/amphion/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3069"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development. 

- [Amphion @ GitHub](https://github.com/open-mmlab/Amphion).]]>
            </summary>
            <updated>2025-08-29T00:28:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3071</id>
            <title type="text"><![CDATA[TrafficLLM]]></title>
            <link rel="alternate" href="https://github.com/ZGC-LLM-Safety/TrafficLLM" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3071"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The repository of TrafficLLM, a universal LLM adaptation framework to learn robust traffic representation for all open-sourced LLM in real-world scenarios and enhance the generalization across diverse traffic analysis tasks. 

 The repository of TrafficLLM, a universal LLM adaptation framework to learn robust traffic representation for all open-sourced LLM in real-world scenarios and enhance the generalization across diverse traffic analysis tasks.]]>
            </summary>
            <updated>2025-08-29T00:28:27+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3074</id>
            <title type="text"><![CDATA[Amazon SageMaker]]></title>
            <link rel="alternate" href="https://aws.amazon.com/sagemaker/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3074"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.

Amazon SageMaker is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML) for any use case. With SageMaker, you can build, train and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more – all in one integrated development environment (IDE). 

- [Déployez vos modèles de Machine Learning avec Amazon SageMaker @ Cockpit io :fr:](https://blog.cockpitio.com/artificial-intelligence/introduction-sagemaker/).]]>
            </summary>
            <updated>2025-08-29T00:28:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3083</id>
            <title type="text"><![CDATA[ExecuTorch]]></title>
            <link rel="alternate" href="https://pytorch.org/executorch/stable/index.html" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3083"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[On-device AI across mobile, embedded and edge for PyTorch 

ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of PyTorch models to edge devices.

- [ExecuTorch @ GitHub](https://github.com/pytorch/executorch).]]>
            </summary>
            <updated>2025-08-29T00:30:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3130</id>
            <title type="text"><![CDATA[Remini]]></title>
            <link rel="alternate" href="https://remini.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3130"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI Photo Enhancer. The only photo and video enhancer you&amp;#039;ll ever need.

Transformative technology gives your low-quality visuals a stunning HD upgrade. Restore old photos to incredible detail and elevate your content to a professional level.]]>
            </summary>
            <updated>2025-08-29T00:38:30+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3144</id>
            <title type="text"><![CDATA[Wifi Solver]]></title>
            <link rel="alternate" href="https://wifi-solver.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3144"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[WiFi Solver is a web-based simulation tool that lets you simulate the WiFi signal strength in your house.

It is the evolution of the Android app WiFi Solver FDTD, originally launched in 2014. This app was written in the aftermath of a blog post about simulating WiFi called Helmhurts, which went a bit viral back then.]]>
            </summary>
            <updated>2025-08-29T00:40:43+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3164</id>
            <title type="text"><![CDATA[Meta Lingua]]></title>
            <link rel="alternate" href="https://github.com/facebookresearch/lingua" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3164"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Meta Lingua: a lean, efficient, and easy-to-hack codebase to research LLMs. 

Meta Lingua is a minimal and fast LLM training and inference library designed for research. Meta Lingua uses easy-to-modify PyTorch components in order to try new architectures, losses, data, etc. We aim for this code to enable end to end training, inference and evaluation as well as provide tools to better understand speed and stability. While Meta Lingua is currently under development, we provide you with multiple apps to showcase how to use this codebase.]]>
            </summary>
            <updated>2025-08-29T00:44:40+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3204</id>
            <title type="text"><![CDATA[🍓 Ichigo]]></title>
            <link rel="alternate" href="https://github.com/homebrewltd/ichigo" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3204"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Llama3.1 learns to Listen. Local real-time voice AI (Formerly llama3-s).

🍓 Ichigo is an open, ongoing research experiment to extend a text-based LLM to have native &amp;quot;listening&amp;quot; ability. Think of it as an open data, open weight, on device Siri.]]>
            </summary>
            <updated>2025-08-29T00:50:41+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3212</id>
            <title type="text"><![CDATA[Evidently]]></title>
            <link rel="alternate" href="https://www.evidentlyai.com/evidently-oss" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3212"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open-Source ML Monitoring and LLM Observability.

Open-source evaluation and observability for ML and LLM systems
Evaluate, test, and monitor AI-powered systems. From tabular data to LLMs. Built for data scientists, AI, and ML engineers.

- [Evidently @ GitHub](https://github.com/evidentlyai/evidently).]]>
            </summary>
            <updated>2025-08-29T00:52:43+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3228</id>
            <title type="text"><![CDATA[Arch]]></title>
            <link rel="alternate" href="https://archgw.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3228"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Intelligent Prompt Gateway.

Arch is an intelligent prompt gateway. Engineered with (fast) LLMs for the secure handling, robust observability, and seamless integration of prompts with APIs - all outside business logic. Built by the core contributors of Envoy proxy, on Envoy. 

Arch is an intelligent Layer 7 gateway designed to protect, observe, and personalize LLM applications (agents, assistants, co-pilots) with your APIs.

- [Arch @ GitHub](https://github.com/katanemo/arch).]]>
            </summary>
            <updated>2025-08-29T00:54:42+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3234</id>
            <title type="text"><![CDATA[Tabled]]></title>
            <link rel="alternate" href="https://github.com/VikParuchuri/tabled" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3234"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Detect and extract tables to markdown and csv.

Tabled is a small library for detecting and extracting tables. It uses surya to find all the tables in a PDF, identifies the rows/columns, and formats cells into markdown, csv, or html.]]>
            </summary>
            <updated>2025-08-29T00:56:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3240</id>
            <title type="text"><![CDATA[exo]]></title>
            <link rel="alternate" href="https://github.com/exo-explore/exo" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3240"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚ 

Forget expensive NVIDIA GPUs, unify your existing devices into one powerful GPU: iPhone, iPad, Android, Mac, Linux, pretty much any device!

Related contents:

- [Exo - Pour créer un super cluster IA avec tous les appareils qui trainent chez vous @ Korben :fr:](https://korben.info/exo-cluster-ia-distribue-appareils-gpu.html).
- [I built an AI supercomputer with 5 Mac Studios @ NetworkChuck&amp;#039;s YouTube](https://www.youtube.com/watch?v=Ju0ndy2kwlw).]]>
            </summary>
            <updated>2026-01-15T07:43:46+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3289</id>
            <title type="text"><![CDATA[Scriberr]]></title>
            <link rel="alternate" href="https://scriberr.app/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3289"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Self-hosted AI audio transcription.

This is Scriberr, a self-hostable AI audio transcription app. Scriberr uses the open-source Whisper models from OpenAI, to transcribe audio files locally on your hardware. It uses the Whisper.cpp high-performance inference engine for OpenAI&amp;#039;s Whisper. Scriberr also allows you to summarize transcripts using OpenAI&amp;#039;s ChatGPT API, with your own custom prompts. Summarization using ollama is also supported.

- [Scriberr @ GitHub](https://github.com/rishikanthc/Scriberr).

Related contents:

- [Scriberr - La transcription IA qui reste chez vous @ Korben :fr:](https://korben.info/scriberr-transcription-audio-ia-self-hosted.html).]]>
            </summary>
            <updated>2025-12-29T08:40:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3308</id>
            <title type="text"><![CDATA[Donobu]]></title>
            <link rel="alternate" href="https://www.donobu.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3308"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Automate dogfoodingfor your team.  Get more eyes on your web app.
Testing your product is now just a simple command away. Get more eyes on your website. Ship with confidence.

Donobu simplifies automation through intuitive prompts, getting you started in no time.]]>
            </summary>
            <updated>2025-08-29T01:08:54+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3337</id>
            <title type="text"><![CDATA[LLVM BOLT]]></title>
            <link rel="alternate" href="https://github.com/llvm/llvm-project/tree/main/bolt" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3337"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[BOLT is a post-link optimizer developed to speed up large applications. It achieves the improvements by optimizing application&amp;#039;s code layout based on execution profile gathered by sampling profiler, such as Linux perf tool.

- [Playing with BOLT and Postgres @ Tomas Vondra](https://vondra.me/posts/playing-with-bolt-and-postgres/).]]>
            </summary>
            <updated>2025-08-29T01:12:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3344</id>
            <title type="text"><![CDATA[KnobGen]]></title>
            <link rel="alternate" href="https://github.com/aminK8/KnobGen" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3344"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Official Implementation of KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion Models]]>
            </summary>
            <updated>2025-08-29T01:13:55+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3373</id>
            <title type="text"><![CDATA[Posterior-Mean Rectified Flow]]></title>
            <link rel="alternate" href="https://pmrf-ml.github.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3373"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Towards Minimum MSE Photo-Realistic Image Restoration.

PMRF is a novel photo-realistic image restoration algorithm. It (provably) approximates the optimal estimator that minimizes the Mean Squared Error (MSE) under a perfect perceptual quality constraint. 

- [PMRF @ GitHub](https://github.com/ohayonguy/PMRF).]]>
            </summary>
            <updated>2025-08-29T01:18:56+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3381</id>
            <title type="text"><![CDATA[Modal]]></title>
            <link rel="alternate" href="https://modal.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3381"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[High-performance cloud for developers.
The serverless platform for teams. Run generative AI models, large-scale batch jobs, job queues, and much more.
Bring your own code — we run the infrastructure. 

Modal is a serverless platform for data and AI/ML engineers to run and deploy code in the cloud without having to think about infrastructure. Run generative AI models, large-scale batch jobs, job queues, and more, all faster than ever before.

- [Building a cost-effective analytics stack with Modal, dlt, and dbt @ Modal](https://modal.com/blog/analytics-stack).
- [Your October Dose of Data - October 2024 @ Data Council](https://mailchi.mp/datacouncil/october-2024).
- [Querying Billions of GitHub Events Using Modal and DuckDB (Part 1: Ingesting Data) @ noreasontopanic](https://noreasontopanic.com/p/querying-billions-of-github-events).
- [What every dev should know about AI sandboxes @ Engineer&amp;#039;s Codex](https://read.engineerscodex.com/p/every-dev-should-know-about-ai-sandboxes).]]>
            </summary>
            <updated>2026-04-27T13:06:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3383</id>
            <title type="text"><![CDATA[AutoArena]]></title>
            <link rel="alternate" href="https://www.kolena.com/autoarena/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3383"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Rank LLMs, RAG systems, and prompts using automated judge evaluation.

- [AutoArena @ GitHub](https://github.com/kolenaIO/autoarena).
- [Your October Dose of Data - October 2024 @ Data Council](https://mailchi.mp/datacouncil/october-2024).]]>
            </summary>
            <updated>2025-08-29T01:21:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3390</id>
            <title type="text"><![CDATA[Pika]]></title>
            <link rel="alternate" href="https://pika.art/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3390"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Video on command. Pika puts creative control where it belongs: in your hands.

Pika is a image to video generator using ai.]]>
            </summary>
            <updated>2025-08-29T01:21:56+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3391</id>
            <title type="text"><![CDATA[PerCo]]></title>
            <link rel="alternate" href="https://github.com/Nikolai10/PerCo" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3391"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[PyTorch implementation of PerCo (Towards Image Compression with Perfect Realism at Ultra-Low Bitrates, ICLR 2024)]]>
            </summary>
            <updated>2025-08-29T01:21:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3424</id>
            <title type="text"><![CDATA[Crew AI]]></title>
            <link rel="alternate" href="https://www.crewai.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3424"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. 

- [Crew AI @ GitHub](https://github.com/crewAIInc/crewAI).

Related contents:

- [S5E8 - Votre téléphone vous ment (et c&amp;#039;est fascinant) @ Underscore_&amp;#039;s acast :fr:](https://shows.acast.com/micode-underscore/episodes/s5e8-votre-telephone-vous-ment-et-cest-fascinant).
- [Tour d&amp;#039;horizon des frameworks pour créer des applications basées sur les LLM @ Data-Crafting.io :fr:](https://datacrafting.substack.com/p/tour-dhorizon-des-frameworks-pour).]]>
            </summary>
            <updated>2025-08-29T01:27:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3425</id>
            <title type="text"><![CDATA[AutoGen]]></title>
            <link rel="alternate" href="https://microsoft.github.io/autogen/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3425"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An Open-Source Programming Framework for Agentic AI

- [AutoGen @ GitHub](https://github.com/microsoft/autogen).
- [Tour d&amp;#039;horizon des frameworks pour créer des applications basées sur les LLM @ Data-Crafting.io :fr:](https://datacrafting.substack.com/p/tour-dhorizon-des-frameworks-pour).]]>
            </summary>
            <updated>2025-08-29T01:27:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3426</id>
            <title type="text"><![CDATA[LlamaIndex]]></title>
            <link rel="alternate" href="https://www.llamaindex.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3426"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Data Framework for LLM Applications.

LlamaIndex (GPT Index) is a data framework for your LLM application. Building with LlamaIndex typically involves working with LlamaIndex core and a chosen set of integrations (or plugins). There are two ways to start building with LlamaIndex in Python:

- [LlamaIndex @ GitHub](https://github.com/run-llama/llama_index).

Related contents:

- [Tour d&amp;#039;horizon des frameworks pour créer des applications basées sur les LLM @ Data-Crafting.io :fr:](https://datacrafting.substack.com/p/tour-dhorizon-des-frameworks-pour).
- [7 Lessons from building a small-scale AI application @ Richard Li](https://www.thelis.org/blog/lessons-from-ai).
- [Production RAG: what I learned from processing 5M+ documents @ Abdellatif Abdelfattah](https://blog.abdellatif.io/production-rag-processing-5m-documents).]]>
            </summary>
            <updated>2025-10-21T11:49:44+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3453</id>
            <title type="text"><![CDATA[Kotaemon]]></title>
            <link rel="alternate" href="https://cinnamon.github.io/kotaemon/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3453"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An open-source RAG-based tool for chatting with your documents. 

An open-source clean &amp;amp; customizable RAG UI for chatting with your documents. Built with both end users and developers in mind.

- [Kotaemon @ GitHub](https://github.com/Cinnamon/kotaemon).]]>
            </summary>
            <updated>2025-08-29T01:33:04+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3462</id>
            <title type="text"><![CDATA[Prompt Engineering Guide]]></title>
            <link rel="alternate" href="https://www.promptingguide.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3462"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🐙 Guides, papers, lecture, notebooks and resources for prompt engineering.

Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs).

- [Prompt Engineering Guide @ GitHub](https://github.com/dair-ai/Prompt-Engineering-Guide).
- [AI tools for software engineers, but without the hype – with Simon Willison (co-creator of Django) @ The Pragmatic Engineer](https://newsletter.pragmaticengineer.com/p/ai-tools-for-software-engineers-simon-willison).]]>
            </summary>
            <updated>2025-08-29T01:33:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3478</id>
            <title type="text"><![CDATA[TensorRT SDK]]></title>
            <link rel="alternate" href="https://developer.nvidia.com/tensorrt" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3478"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[NVIDIA® TensorRT™ is an ecosystem of APIs for high-performance deep learning inference. TensorRT includes an inference runtime and model optimizations that deliver low latency and high throughput for production applications. The TensorRT ecosystem includes TensorRT, TensorRT-LLM, TensorRT Model Optimizer, and TensorRT Cloud.

- [TensorRT Open Source Software @ GitHub](https://github.com/NVIDIA/TensorRT).]]>
            </summary>
            <updated>2025-08-29T01:37:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3490</id>
            <title type="text"><![CDATA[TransformersPHP]]></title>
            <link rel="alternate" href="https://codewithkyrian.github.io/transformers-php/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3490"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[State-of-the-art Machine Learning for PHP. Run Transformers natively in your PHP projects.

Transformers PHP is a toolkit for PHP developers to add machine learning magic to their projects easily. 
TransformersPHP is designed to be functionally equivalent to the Python library, while still maintaining the same level of performance and ease of use. This library is built on top of the Hugging Face&amp;#039;s Transformers library, which provides thousands of pre-trained models in 100+ languages. It is designed to be a simple and easy-to-use library for PHP developers using a similar API to the Python library. These models can be used for a variety of tasks, including text generation, summarization, translation, and more.

- [TransformersPHP @ GitHub](https://github.com/CodeWithKyrian/transformers-php).
- [Building a Background Removal Tool with Laravel and TransformersPHP @ CodeWithKyrian](https://codewithkyrian.com/p/building-a-background-removal-tool-with-laravel-and-transformersphp).]]>
            </summary>
            <updated>2025-08-29T01:38:06+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3537</id>
            <title type="text"><![CDATA[PrivateGPT.dev]]></title>
            <link rel="alternate" href="https://privategpt.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3537"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Local &amp;amp; OnPremise AI. Run LLMs offline. Tap into your private knowledge base.
Interact with your documents using the power of GPT, 100% privately, no data leaks 

PrivateGPT is a production-ready AI project that allows you to ask questions about your documents using the power of Large Language Models (LLMs), even in scenarios without an Internet connection. 100% private, no data leaves your execution environment at any point.

- [PrivateGPT @ GitHub](https://github.com/zylon-ai/private-gpt).]]>
            </summary>
            <updated>2025-08-29T01:46:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3557</id>
            <title type="text"><![CDATA[ell]]></title>
            <link rel="alternate" href="https://docs.ell.so/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3557"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[ell is a lightweight prompt engineering library treating prompts as functions.

- [ell @ GitHub](https://github.com/MadcowD/ell).]]>
            </summary>
            <updated>2025-08-29T01:49:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3576</id>
            <title type="text"><![CDATA[Dot]]></title>
            <link rel="alternate" href="https://dotapp.uk/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3576"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Text-To-Speech, RAG, and LLMs. All local!

Dot is a standalone, open-source application designed for seamless interaction with documents and files using local LLMs and Retrieval Augmented Generation (RAG). It is inspired by solutions like Nvidia&amp;#039;s Chat with RTX, providing a user-friendly interface for those without a programming background. Using the Phi-3 LLM by default, Dot ensures accessibility and simplicity right out of the box.

- [Dot – L’app IA locale pour interagir avec vos documents (RAG) @ Korben :fr:](https://korben.info/dot-app-ia-locale-interagir-documents.html).]]>
            </summary>
            <updated>2025-08-29T01:53:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3666</id>
            <title type="text"><![CDATA[Firecrawl]]></title>
            <link rel="alternate" href="https://www.firecrawl.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3666"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Turn websites into LLM-ready data.

Power your AI apps with clean data crawled from any website. It&amp;#039;s also open-source.
 🔥 Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API. 

- [Firecrawl @ GitHub](https://github.com/mendableai/firecrawl).

Related contents:

- [Firecrawl @ GitHub](https://github.com/firecrawl/firecrawl).
- [Firecrawl Observer @ GitHub](https://github.com/firecrawl/firecrawl-observer).

Related contents:

- [🚨 Someone built a tool that turns any website into clean data your AI can actually use @ Nav Toor&amp;#039;s X](https://nitter.net/heynavtoor/status/2031626457110425760).
- [Hermes Agent : veille technique auto-hébergée avec Matrix, FreshRSS et Firecrawl @ Cryptolab :fr:](https://cryptolab.re/posts/2026/hermes-agent-framework-self-hosted/).]]>
            </summary>
            <updated>2026-06-23T05:55:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3677</id>
            <title type="text"><![CDATA[open-appsec]]></title>
            <link rel="alternate" href="https://www.openappsec.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3677"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Automatic Open Source WAF &amp;amp; API Security.

open-appsec is a machine learning security engine that preemptively and automatically prevents threats against Web Application &amp;amp; APIs.

- [open-appsec @ GitHub](https://github.com/openappsec/openappsec).]]>
            </summary>
            <updated>2025-08-29T02:09:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3699</id>
            <title type="text"><![CDATA[Visual Copilot]]></title>
            <link rel="alternate" href="https://www.builder.io/m/design-to-code" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3699"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Design to Code. Automated.

Turn days into minutes by bringing AI into your design-to-code workflow. Convert Figma designs into clean code with Visual Copilot.

- [Good Refactoring vs Bad Refactoring @ builder.io](https://www.builder.io/blog/good-vs-bad-refactoring).]]>
            </summary>
            <updated>2025-08-29T02:13:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3719</id>
            <title type="text"><![CDATA[GraphRAG]]></title>
            <link rel="alternate" href="https://microsoft.github.io/graphrag/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3719"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs.

GraphRAG is a structured, hierarchical approach to Retrieval Augmented Generation (RAG), as opposed to naive semantic-search approaches using plain text snippets. The GraphRAG process involves extracting a knowledge graph out of raw text, building a community hierarchy, generating summaries for these communities, and then leveraging these structures when perform RAG-based tasks.

- [GraphRAG @ GitHub](https://github.com/microsoft/graphrag).
- [GraphRAG: Microsoft’s Open-Source Solution for Enhanced Document Understanding @ Towards AI&amp;#039;s Medium](https://pub.towardsai.net/graphrag-microsofts-open-source-solution-for-enhanced-document-understanding-b42d05f5fec5).]]>
            </summary>
            <updated>2025-08-29T02:17:27+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3737</id>
            <title type="text"><![CDATA[StemRoller]]></title>
            <link rel="alternate" href="https://www.stemroller.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3737"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Make stems, instrumental, or acapella version of any song! Isolate vocals, drums, bass, and other instrumental stems from any song.

StemRoller is the first free app which enables you to separate vocal and instrumental stems from any song with a single click! StemRoller uses Facebook&amp;#039;s state-of-the-art Demucs algorithm for demixing songs and integrates search results from YouTube.

- [StemRoller](https://github.com/stemrollerapp/stemroller).]]>
            </summary>
            <updated>2025-08-29T02:19:27+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3768</id>
            <title type="text"><![CDATA[GROBID]]></title>
            <link rel="alternate" href="https://grobid.readthedocs.io/en/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3768"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[GROBID (or Grobid, but not GroBid nor GroBiD) means GeneRation Of BIbliographic Data.

GROBID is a machine learning library for extracting, parsing and re-structuring raw documents such as PDF into structured XML/TEI encoded documents with a particular focus on technical and scientific publications. First developments started in 2008 as a hobby, following a suggestion by Laurent Romary (Inria, France). In 2011, the tool has been made available in open source. Work on GROBID has been steady as a side project since the beginning and is expected to continue as such, facilitated in particular to the continuous support of Inria.

- [Grobid @ GitHub](https://github.com/kermitt2/grobid).]]>
            </summary>
            <updated>2025-08-29T02:25:32+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3773</id>
            <title type="text"><![CDATA[InstructLab]]></title>
            <link rel="alternate" href="https://instructlab.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3773"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A new community-based approach to build truly open-source LLMs.

InstructLab Command-Line Interface. Use this to chat with a model and execute the InstructLab workflow to train a model using custom taxonomy data. 

- [InstructLab 🐶 (ilab) @ GitHub](https://github.com/instructlab/instructlab).
- [Spécial été 2024 : retour sur la conférence WeAreDevs et les tendances Tech @ AXOPEN YouTube :fr:](https://www.youtube.com/watch?v=daVHfuM1ios).
- [InstructLab: Advancing generative AI through open source @ Red Hat Developer](https://developers.redhat.com/articles/2024/05/07/instructlab-open-source-generative-ai).]]>
            </summary>
            <updated>2025-08-29T02:25:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3785</id>
            <title type="text"><![CDATA[Flux @ Black Forest Labs]]></title>
            <link rel="alternate" href="https://blackforestlabs.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3785"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[state-of-the-art performance image generation with top of the line prompt following, visual quality, image detail and output diversity. 

- [Flux Is The Open-Source Midjourney Killer Everyone&amp;#039;s Talking About! @ It&amp;#039;s FOSS News](https://news.itsfoss.com/flux-image-generator/).]]>
            </summary>
            <updated>2025-08-29T02:27:32+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3805</id>
            <title type="text"><![CDATA[Dioptra]]></title>
            <link rel="alternate" href="https://pages.nist.gov/dioptra/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3805"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Dioptra is a software test platform for assessing the trustworthy characteristics of artificial intelligence (AI). Trustworthy AI is: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair - with harmful bias managed1. Dioptra supports the Measure function of the NIST AI Risk Management Framework by providing functionality to assess, analyze, and track identified AI risks.

- [Dioptra @ GitHub](https://github.com/usnistgov/dioptra).]]>
            </summary>
            <updated>2025-08-29T02:30:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3806</id>
            <title type="text"><![CDATA[oTranscribe]]></title>
            <link rel="alternate" href="https://otranscribe.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3806"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A free &amp;amp; open tool for transcribing audio interviews.

oTranscribe is a free web app designed to take the pain out of transcribing recorded interviews.

- [oTranscribe @ GitHub](https://github.com/oTranscribe/oTranscribe).]]>
            </summary>
            <updated>2025-08-29T02:30:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3808</id>
            <title type="text"><![CDATA[Deep Live Cam]]></title>
            <link rel="alternate" href="https://deeplive.cam/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3808"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Real-Time Face Swapping and One-Click Video Deepfake Tool. The Next Leap in Real-Time Face Swapping and Video Deepfake Technology.

Deep Live Cam harnesses cutting-edge AI to push the boundaries of real-time face swapping and video deepfakes.
Achieve high-quality face replacement with just a single image.

- [Deep Live Cam @ GitHub](https://github.com/hacksider/Deep-Live-Cam).]]>
            </summary>
            <updated>2025-09-08T08:15:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3841</id>
            <title type="text"><![CDATA[Verba]]></title>
            <link rel="alternate" href="https://github.com/weaviate/Verba" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3841"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Retrieval Augmented Generation (RAG) chatbot powered by Weaviate.

Welcome to Verba: The Golden RAGtriever, an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. In just a few easy steps, explore your datasets and extract insights with ease, either locally with HuggingFace and Ollama or through LLM providers such as OpenAI, Cohere, and Google.]]>
            </summary>
            <updated>2025-08-29T02:37:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3867</id>
            <title type="text"><![CDATA[Perplexica]]></title>
            <link rel="alternate" href="https://github.com/ItzCrazyKns/Perplexica/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3867"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Perplexica is an AI-powered search engine. It is an Open source alternative to Perplexity AI.

Perplexica is an open-source AI-powered searching tool or an AI-powered search engine that goes deep into the internet to find answers. Inspired by Perplexity AI, it&amp;#039;s an open-source option that not just searches the web but understands your questions. It uses advanced machine learning algorithms like similarity searching and embeddings to refine results and provides clear answers with sources cited.

- [Perplexica – Le moteur de recherche open source propulsé à l’IA @ Korben :fr:](https://korben.info/perplexica-outil-open-source-revolutionne-recherche-web.html).]]>
            </summary>
            <updated>2025-08-29T02:41:41+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3880</id>
            <title type="text"><![CDATA[say]]></title>
            <link rel="alternate" href="https://github.com/8ta4/say" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3880"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[say is always on, recording and transcribing your voice 24/7. Whenever inspiration strikes, just say it.]]>
            </summary>
            <updated>2025-08-29T02:43:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3921</id>
            <title type="text"><![CDATA[Celp]]></title>
            <link rel="alternate" href="https://www.celp.ai/?sc=2710522184f852cc1f70f5bb8057466bc8ea5681d" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3921"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Write code, not tests.

Contextually Aware AI-Driven Unit Test Generation for Typescript Node.js projects.
Celp generates contextually relevant unit tests for Typescript node.js projects, saving you hours.]]>
            </summary>
            <updated>2025-08-29T02:49:48+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3930</id>
            <title type="text"><![CDATA[PGVecto.rs]]></title>
            <link rel="alternate" href="https://pgvecto.rs/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3930"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. 

PGVecto.rs is a Postgres extension that enables scalable vector search, allowing you to build powerful similarity-based applications on top of your Postgres database.

- [PGvector.rs documentation](https://docs.pgvecto.rs/).
- [PGvector.rs @ GitHub](https://github.com/tensorchord/pgvecto.rs).]]>
            </summary>
            <updated>2025-08-29T02:51:44+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4027</id>
            <title type="text"><![CDATA[Open WebUI]]></title>
            <link rel="alternate" href="https://openwebui.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4027"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Unlock your LLM&amp;#039;s creativity.  User-friendly WebUI for LLMs (Formerly Ollama WebUI) 

Open WebUI is an extensible, self-hosted interface for AI that adapts to your workflow, all while operating entirely offline.

Open WebUI is an extensible, feature-rich, and user-friendly self-hosted WebUI designed to operate entirely offline. It supports various LLM runners, including Ollama and OpenAI-compatible APIs. For more information, be sure to check out our Open WebUI Documentation.

- [Open WebUi @ GitHub](https://github.com/open-webui/open-webui).

Related contents:

- [host ALL your AI locally @ NetworkChuck&amp;#039;s YouTube](https://www.youtube.com/watch?v=Wjrdr0NU4Sk).
- [DeepSeek Local: How to Self-Host DeepSeek (Privacy and Control) @ LinuxBlog.io](https://linuxblog.io/deepseek-local-self-host/).
- [Self-Hosted Private LLM using Ollama and Open WebUI @ Getting Started](https://www.gettingstarted.ai/self-host-llm/)h
- [LLMs on Kubernetes Part 1: Understanding the threat model @ CNCF](https://www.cncf.io/blog/2026/03/30/llms-on-kubernetes-part-1-understanding-the-threat-model/).]]>
            </summary>
            <updated>2026-04-07T05:46:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4029</id>
            <title type="text"><![CDATA[PostgresML]]></title>
            <link rel="alternate" href="https://postgresml.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4029"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Infra for RAG apps that work in prod.
You know Postgres. Now you know machine learning.

Index, filter &amp;amp; rank vectors. Create embeddings. Generate real-time, fact-based outputs.

Korvus is a search SDK that unifies the entire RAG pipeline in a single database query. Built on top of Postgres with bindings for Python, JavaScript and Rust, Korvus delivers high-performance, customizable search capabilities with minimal infrastructure concerns.

- [Korvus @ GitHub](https://github.com/postgresml/korvus).]]>
            </summary>
            <updated>2025-08-29T03:07:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4062</id>
            <title type="text"><![CDATA[C2PA]]></title>
            <link rel="alternate" href="https://c2pa.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4062"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Coalition for Content Provenance and Authenticity.

An open technical standard providing publishers, creators, and consumers the ability to trace the origin of different types of media. 

- [C2PA @ GitHub](https://github.com/c2pa-org).

Related contents:

- [481 : Episode du 12 février 2025 @ Les Technos :fr:](https://lestechnos.be/les-technos-481-episode-du-12-fevrier/).]]>
            </summary>
            <updated>2025-08-29T03:14:03+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4107</id>
            <title type="text"><![CDATA[MindsDB]]></title>
            <link rel="alternate" href="https://mindsdb.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4107"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI Development Cloud Platform, Real Time AI &amp;amp; ML, Deploy &amp;amp; Manage AI.
MindsDB is the most widely used AI data automation platform.

MindsDB is the platform for customizing AI from enterprise data. You can create, serve, and fine-tune models in real-time from your database, vector store, and application data. 

- [MindsDB @ GitHub](https://github.com/mindsdb/mindsdb).]]>
            </summary>
            <updated>2025-08-29T03:22:03+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4138</id>
            <title type="text"><![CDATA[ToonCrafter]]></title>
            <link rel="alternate" href="https://toon-crafter.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4138"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI Cartoon Animation Generator.

Bring your cartoons to life with ToonCrafter, the AI-powered animation tool. Simply upload your keyframes and let our advanced algorithms create smooth, stylized animations that match your artwork perfectly. Unleash your creativity and start animating today!

- [ToonCrafter @ GitHub](https://github.com/ToonCrafter/ToonCrafter).
- [Computex 2024 @ Tech Café :fr:](https://techcafe.fr/computex-2024/).]]>
            </summary>
            <updated>2025-08-29T03:26:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4158</id>
            <title type="text"><![CDATA[Supervision]]></title>
            <link rel="alternate" href="https://supervision.roboflow.com/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4158"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us!

Supervision provides a seamless process for annotating predictions generated by various object detection and segmentation models.

- [Supervision @ GitHub](https://github.com/roboflow/supervision).]]>
            </summary>
            <updated>2025-08-29T03:30:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4172</id>
            <title type="text"><![CDATA[Transformers.js]]></title>
            <link rel="alternate" href="https://github.com/xenova/transformers.js" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4172"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!

Transformers.js is designed to be functionally equivalent to Hugging Face&amp;#039;s transformers python library, meaning you can run the same pretrained models using a very similar API. These models support common tasks in different modalities, such as:]]>
            </summary>
            <updated>2025-08-29T03:32:06+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4174</id>
            <title type="text"><![CDATA[Devika]]></title>
            <link rel="alternate" href="https://github.com/stitionai/devika" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4174"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Devika is an Agentic AI Software Engineer that can understand high-level human instructions, break them down into steps, research relevant information, and write code to achieve the given objective. Devika aims to be a competitive open-source alternative to Devin by Cognition AI.

- [Devika – Votre ingénieur logiciel personnel @ Korben :fr:](https://korben.info/devika-ia-revolutionnaire-developpement-logiciel.html).]]>
            </summary>
            <updated>2025-08-29T03:32:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4184</id>
            <title type="text"><![CDATA[sherpa-onnx]]></title>
            <link rel="alternate" href="https://k2-fsa.github.io/sherpa/onnx/index.html" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4184"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Speech-to-text, text-to-speech, and speaker recongition using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, Raspberry Pi, RISC-V, x86_64 servers, websocket server/client, C/C++, Python, Kotlin, C#, Go, NodeJS, Java, Swift.

- [sherpa-onnx @ GitHub](https://github.com/k2-fsa/sherpa-onnx).]]>
            </summary>
            <updated>2025-08-29T03:34:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4214</id>
            <title type="text"><![CDATA[Dataherald AI]]></title>
            <link rel="alternate" href="https://dataherald.readthedocs.io/en/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4214"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Dataherald is a natural language-to-SQL engine built for enterprise-level question answering over relational data. It allows you to set up an API from your database that can answer questions in plain English.]]>
            </summary>
            <updated>2025-08-29T03:39:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4275</id>
            <title type="text"><![CDATA[Data Version Control · DVC]]></title>
            <link rel="alternate" href="https://dvc.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4275"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🦉 ML Experiments and Data Management with Git 

Data Version Control or DVC is a command line tool and VS Code Extension to help you develop reproducible machine learning projects:

- [Data Version Control @ GitHub](https://github.com/iterative/dvc).
- [270 - DB Vectorielle - Noé Achache @ &amp;lt;ifttd&amp;gt; :fr:](https://www.ifttd.io/episodes/db-vectorielle).]]>
            </summary>
            <updated>2025-08-29T03:50:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4300</id>
            <title type="text"><![CDATA[Albert]]></title>
            <link rel="alternate" href="https://github.com/etalab-ia/albert" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4300"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Backend ressources for Albert. Albert, also known as LIA (Legal Information Assistant), is a conversational agent that uses official French data sources to answer administrative agents questions.]]>
            </summary>
            <updated>2025-08-29T03:54:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4337</id>
            <title type="text"><![CDATA[Whisper.c++]]></title>
            <link rel="alternate" href="https://github.com/ggerganov/whisper.cpp" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4337"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Port of OpenAI&amp;#039;s Whisper model in C/C++]]>
            </summary>
            <updated>2025-08-29T03:59:21+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4340</id>
            <title type="text"><![CDATA[SUPIR]]></title>
            <link rel="alternate" href="https://supir.xpixel.group/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4340"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Text-driven, intelligent restoration, blending AI technology with creativity to give every image a brand new life.  Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild.

SUPIR aims at developing Practical Algorithms for Photo-Realistic Image Restoration In the Wild.

- [SUPIR @ GitHub](https://github.com/Fanghua-Yu/SUPIR).
- [SUPIR – L’IA qui restaure vos photos comme jamais @ Korben :fr:](https://korben.info/supir-revolutionnary-ai-powered-image-restoration-high-quality-text-guided-control.html).]]>
            </summary>
            <updated>2025-08-29T04:00:19+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4370</id>
            <title type="text"><![CDATA[ClarityAI]]></title>
            <link rel="alternate" href="https://clarityai.cc/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4370"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI Image Upscaler &amp;amp; Enhancer - Magnific Alternative. Upscale your images with AI Magic.

ClarityAI is a high resolution upscaler which also can enhance your images and add details. You can decide how much you want the AI to add details to your images.

- [ClarityAI @ GitHub](https://github.com/philz1337x/clarity-upscaler/).
- [s4e13 - UNDERSCORE_ : $50 000 pour hacker l&amp;#039;IA de Google ! @ Micode&amp;#039;s Twitch :fr:](https://www.twitch.tv/videos/2103081056).]]>
            </summary>
            <updated>2025-08-29T04:06:23+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4371</id>
            <title type="text"><![CDATA[MLX]]></title>
            <link rel="alternate" href="https://github.com/ml-explore/mlx" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4371"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An array framework for Apple silicon.

MLX is an array framework for machine learning research on Apple silicon, brought to you by Apple machine learning research.

- [s4e13 - UNDERSCORE_ : $50 000 pour hacker l&amp;#039;IA de Google ! @ Micode&amp;#039;s Twitch :fr:](https://www.twitch.tv/videos/2103081056).]]>
            </summary>
            <updated>2025-08-29T04:06:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4372</id>
            <title type="text"><![CDATA[Ferret]]></title>
            <link rel="alternate" href="https://github.com/apple/ml-ferret" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4372"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Refer and Ground Anything Anywhere at Any Granularity.

An End-to-End MLLM that Accept Any-Form Referring and Ground Anything in Response.

- [🚀🚀Introducing Ferret, a new MLLM that can refer and ground anything anywhere at any granularity @ Zhe Gan&amp;#039;s Twitter](https://twitter.com/zhegan4/status/1712345137983201716).
- [Apple&amp;#039;s &amp;#039;Ferret&amp;#039; is a new open-source machine learning model @ Apple Insider](https://appleinsider.com/articles/23/12/24/apples-ferret-is-a-new-open-source-machine-learning-model).
- [s4e13 - UNDERSCORE_ : $50 000 pour hacker l&amp;#039;IA de Google ! @ Micode&amp;#039;s Twitch :fr:](https://www.twitch.tv/videos/2103081056).]]>
            </summary>
            <updated>2025-08-29T04:06:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4389</id>
            <title type="text"><![CDATA[llamafile]]></title>
            <link rel="alternate" href="https://mozilla-ai.github.io/llamafile/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4389"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Distribute and run LLMs with a single file.

Our goal is to make open LLMs much more accessible to both developers and end users. We&amp;#039;re doing that by combining llama.cpp with Cosmopolitan Libc into one framework that collapses all the complexity of LLMs down to a single-file executable (called a &amp;quot;llamafile&amp;quot;) that runs locally on most computers, with no installation.

- [llamafile @ GitHub](https://github.com/mozilla-ai/llamafile).

Related contents:

- [LLaMA Now Goes Faster on CPUs @ justine&amp;#039;s web page](https://justine.lol/matmul/).
- [Justine Tunney booste encore une fois les performances de llama.cpp @ Korben :fr:](https://korben.info/justine-tunney-booste-performances-llama-cpp-nouveaux-kernels-algebre-lineaire.html).
- [Llamafile - Exécutez des modèles de langage en un seul fichier ! @ Korben :fr:](https://korben.info/llamafile-executez-modeles-langage-fichier.html).]]>
            </summary>
            <updated>2026-03-23T16:22:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4410</id>
            <title type="text"><![CDATA[Reor]]></title>
            <link rel="alternate" href="https://www.reorproject.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4410"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Self-organizing AI note-taking app that runs models locally.

Reor is an AI-powered desktop note-taking app: it automatically links related ideas, answers questions on your notes and provides semantic search. Everything is stored locally and you can edit your notes with an Obsidian-like markdown editor.

- [Reor @ GitHub](https://github.com/reorproject/reor).
- [Reor – L’appli magique de prise de notes boostée à l’IA locale et gratuite @ Korben :fr:](https://korben.info/outil-prise-notes-intelligence-artificielle-reor-project.html).]]>
            </summary>
            <updated>2025-08-29T04:11:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4411</id>
            <title type="text"><![CDATA[Jan]]></title>
            <link rel="alternate" href="https://jan.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4411"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Rethink the Computer. Turn your computer into an AI machine.

Jan is an open source alternative to ChatGPT that runs 100% offline on your computer.

- [Jan @ GitHub](https://github.com/janhq/jan).

Related contents:

- [Jan – Le « ChatGPT » local et open source à installer sur votre machine @ Korben :fr:](https://korben.info/jan-open-source-chatgpt-alternative-hors-ligne-personnalisable.html).]]>
            </summary>
            <updated>2025-08-29T04:12:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4413</id>
            <title type="text"><![CDATA[life2vec - Official Model and Paper Page]]></title>
            <link rel="alternate" href="https://life2vec.dk/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4413"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Using Sequences of Life-events to Predict Human Lives.

We represent human lives in a way that shares structural similarity to language, and we exploit this similarity to adapt natural language processing techniques to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on a comprehensive registry dataset, which is available for Denmark across several years, and that includes information about life-events related to health, education, occupation, income, address and working hours, recorded with day-to-day resolution. 

- [live2vec @ GitHub](https://github.com/SocialComplexityLab/life2vec).
- [Life2vec – Une IA danoise qui prédit votre vie et… votre mort ! @ Korben :fr:](https://korben.info/life2vec-ia-danoise-predit-vie-donnees.html).]]>
            </summary>
            <updated>2025-08-29T04:12:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4432</id>
            <title type="text"><![CDATA[MIA :fr:]]></title>
            <link rel="alternate" href="https://www.mia-app.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4432"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[La plateforme d&amp;#039;IA qui simplifie l&amp;#039;analyse des données à tous les métiers. Piloter aujourd&amp;#039;hui. Anticiper demain. Dès maintenant.

MIA est une solution No Code d’analyse de données, basée sur l’Intelligence Artificielle, qui s&amp;#039;adapte à votre profil. Simple. Efficace. Rapide

- [261 - No code pour l’IA - Pascal Corrotti @ &amp;lt;ifttd&amp;gt; :fr:](https://www.ifttd.io/episodes/no-code-pour-l-ia).]]>
            </summary>
            <updated>2025-08-29T04:15:36+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4439</id>
            <title type="text"><![CDATA[FaceFusion]]></title>
            <link rel="alternate" href="https://github.com/facefusion/facefusion" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4439"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Next generation face swapper and enhancer]]>
            </summary>
            <updated>2025-08-29T04:16:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4457</id>
            <title type="text"><![CDATA[MyShell]]></title>
            <link rel="alternate" href="https://myshell.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4457"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Democratizing &amp;amp; Decentralizing AI-native apps.

MyShell is a decentralized and comprehensive platform for discovering, creating, and staking AI-native apps.]]>
            </summary>
            <updated>2025-08-29T04:19:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4492</id>
            <title type="text"><![CDATA[BRIA Background Removal v1.4 @ Hugging Face]]></title>
            <link rel="alternate" href="https://huggingface.co/briaai/RMBG-1.4" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4492"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[RMBG v1.4 is our state-of-the-art background removal model, designed to effectively separate foreground from background in a range of categories and image types. This model has been trained on a carefully selected dataset, which includes: general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use cases powering enterprise content creation at scale. The accuracy, efficiency, and versatility currently rival leading open source models. It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount.

- [BRIA Background Removal v1.4 Model Card @ GitHub](https://github.com/chenxwh/cog-RMBG).
- [S4E10 - Quel destin pour l’Apple Vision Pro ? @ Underscore_&amp;#039;s Acast :fr:](https://shows.acast.com/micode-underscore/episodes/s4e10-quel-destin-pour-lapple-vision-pro).]]>
            </summary>
            <updated>2025-08-29T04:26:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4493</id>
            <title type="text"><![CDATA[Floneum]]></title>
            <link rel="alternate" href="https://floneum.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4493"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A toolkit for controllable, private AI on consumer hardware in rust.

Floneum is a graph editor that makes it easy to develop your own AI workflows.
Floneum allows you to build workflows that use large language models with a simple drag and drop interface.

- [Floneum @ GitHub](https://github.com/floneum/floneum).
- [S4E10 - Quel destin pour l’Apple Vision Pro ? @ Underscore_&amp;#039;s Acast :fr:](https://shows.acast.com/micode-underscore/episodes/s4e10-quel-destin-pour-lapple-vision-pro).]]>
            </summary>
            <updated>2025-08-29T04:26:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4496</id>
            <title type="text"><![CDATA[Unsloth AI]]></title>
            <link rel="alternate" href="https://unsloth.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4496"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Finetune AI &amp;amp; LLMs faster.
 Web UI for training and running open models like Gemma 4, Qwen3.6, DeepSeek, gpt-oss locally. 

Unslow AI training &amp;amp; finetuning Get 30x faster with unsloth.  5X faster 60% less memory QLoRA finetuning. Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory!

- [Unsloth @ GitHub](https://github.com/unslothai/unsloth).
- [Unsloth AI @ Hugging Face](https://huggingface.co/unsloth).

Related contents:

- [S4E10 - Quel destin pour l’Apple Vision Pro ? @ Underscore_&amp;#039;s Acast :fr:](https://shows.acast.com/micode-underscore/episodes/s4e10-quel-destin-pour-lapple-vision-pro).
- [7 Lessons from building a small-scale AI application @ Richard Li](https://www.thelis.org/blog/lessons-from-ai).
- [Qwen3.6 35B A3B en IQ2_M sur une GTX 1070 : mission accomplie @ Yves Rougy :fr:](https://www.rougy.net/blog/20260526-qwen3.6-35b-a3b-gtx1070/).]]>
            </summary>
            <updated>2026-06-17T06:03:54+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4497</id>
            <title type="text"><![CDATA[Ollama]]></title>
            <link rel="alternate" href="https://ollama.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4497"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Get up and running with large language models, locally.

Run Llama 2, Code Llama, Mistral, Gemma, and other models. Customize and create your own.

- [Ollama @ GitHub](https://github.com/ollama/ollama).

Related contents:

- [Local RAG with Ollama, Mistral, and Turso @ Turso&amp;#039;s blog](https://turso.tech/blog/local-rag-with-ollama-and-turso-sqlite).
- [S4E10 - Quel destin pour l’Apple Vision Pro ? @ Underscore_&amp;#039;s Acast :fr:](https://shows.acast.com/micode-underscore/episodes/s4e10-quel-destin-pour-lapple-vision-pro).
- [Ollama Course – Build AI Apps Locally @ freeCodeCamp.org&amp;#039;s YouTube](https://www.youtube.com/watch?v=GWB9ApTPTv4).
- [Detecting Exposed LLM Servers: A Shodan Case Study on Ollama @ Cisco Blogs](https://blogs.cisco.com/security/detecting-exposed-llm-servers-shodan-case-study-on-ollama).
- [Ollama - 14 000 serveurs IA laissés en libre-service sur Internet @ Korben :fr:](https://korben.info/ollama-serveurs-vulnerabilites-secrete.html).
- [Faire tourner un LLM localement sur votre ordinateur @ Quoi de neuf les devs ? :fr:](https://happytodev.substack.com/p/brent-roose-est-linvite-du-n147-de?open=false#%C2%A7faire-tourner-un-llm-localement-sur-votre-ordinateur).
- [The Ultimate Beginner&amp;#039;s Guide to Self-Hosting Your Own AI @ Arsturn](https://www.arsturn.com/blog/the-ultimate-beginners-guide-to-self-hosting-your-own-ai).
- [How to Run and Customize LLMs Locally with Ollama @ freeCodeCamp](https://www.freecodecamp.org/news/run-and-customize-llms-locally-with-ollama/).
- [LLMs on Kubernetes Part 1: Understanding the threat model @ CNCF](https://www.cncf.io/blog/2026/03/30/llms-on-kubernetes-part-1-understanding-the-threat-model/).
- [Faire tourner un modèle IA chez soi avec Ollama @ DomoPi :fr:](https://domopi.eu/faire-tourner-un-modele-ia-chez-soi-avec-ollama/).
- [Using AI for Terraform: running locally with Langflow, OpenSearch, &amp;amp; Ollama @ Rosemary Wang&amp;#039;s dev.to](https://dev.to/joatmon08/using-ai-for-terraform-running-a-locally-with-langflow-opensearch-ollama-5co6).
- [Using Local Coding Agents @ Ahead of AI](https://magazine.sebastianraschka.com/p/using-local-coding-agents).]]>
            </summary>
            <updated>2026-06-29T12:38:37+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4541</id>
            <title type="text"><![CDATA[Triton]]></title>
            <link rel="alternate" href="https://triton-lang.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4541"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Triton is a language and compiler for parallel programming. It aims to provide a Python-based programming environment for productively writing custom DNN compute kernels capable of running at maximal throughput on modern GPU hardware.

- [Triton @ GitHub](https://github.com/openai/triton).]]>
            </summary>
            <updated>2025-08-29T04:34:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4590</id>
            <title type="text"><![CDATA[pinokio]]></title>
            <link rel="alternate" href="https://pinokio.computer/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4590"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Install, Run &amp;amp; Control Databases on Your Computer with 1 Click.

Pinokio is a browser that lets you install, run, and programmatically control ANY application, automatically. No more opening the terminal. No more git clone. No more conda install. No more pip install. No more messing with execution environments.

- [Pinokio @ GitHub](https://github.com/pinokiocomputer/pinokio).
- [Pinokio – Automatisez et scriptez vos IA en un clic @ Korben :fr:](https://korben.info/pinokio-automatiser-scripter-ia-un-clic.html).]]>
            </summary>
            <updated>2025-08-29T04:42:44+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4612</id>
            <title type="text"><![CDATA[Dark Visitors]]></title>
            <link rel="alternate" href="https://darkvisitors.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4612"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A List of Known AI Agents on the Internet.

Insight into the hidden ecosystem of autonomous chatbots and data scrapers crawling across the web. Protect your website from unwanted AI agent access.

Related contents:

- [Comment protéger vos serveurs et lutter efficacement contre les crawlers d’IA @ Bearstech :fr:](https://bearstech.com/societe/blog/comment-proteger-vos-serveurs-et-lutter-efficacement-contre-les-crawlers-dia).]]>
            </summary>
            <updated>2025-10-30T06:47:32+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4623</id>
            <title type="text"><![CDATA[Applied ML]]></title>
            <link rel="alternate" href="https://github.com/eugeneyan/applied-ml" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4623"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[📚 Papers &amp;amp; tech blogs by companies sharing their work on data science &amp;amp; machine learning in production.

Curated papers, articles, and blogs on data science &amp;amp; machine learning in production. ⚙️]]>
            </summary>
            <updated>2025-08-29T04:46:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4646</id>
            <title type="text"><![CDATA[Tabby]]></title>
            <link rel="alternate" href="https://tabby.tabbyml.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4646"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Opensource, self-hosted AI coding assistant.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. 

- [Tabby @ GitHub](https://github.com/TabbyML/tabby).
- [Tabby, mon Copilot libre @ Devoxx FR&amp;#039;s YouTube :fr:](https://www.youtube.com/watch?v=ZAVIXXQDtbM).
- [086 - Les news web dev pour Mai 2024 - partie 2 @ Double Slash :fr:](https://double-slash.dev/podcasts/news05-2-24/).]]>
            </summary>
            <updated>2025-08-29T04:50:54+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4690</id>
            <title type="text"><![CDATA[Distil-Whisper]]></title>
            <link rel="alternate" href="https://github.com/huggingface/distil-whisper" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4690"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Distilled variant of Whisper for speech recognition. 6x faster, 50% smaller, within 1% word error rate.

- [Distil-Whisper @ Hugging Face](https://huggingface.co/collections/distil-whisper/distil-whisper-models-65411987e6727569748d2eb6).
- [Distil-Whisper – Pour faire de la reconnaissance vocale rapide @ Korben :fr:](https://korben.info/distil-whisper-revolution-reconnaissance-vocale-automatique.html).]]>
            </summary>
            <updated>2025-08-29T04:58:56+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4727</id>
            <title type="text"><![CDATA[Mistral AI :fr:]]></title>
            <link rel="alternate" href="https://mistral.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4727"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open source models

- [Mistral Transformer @ GitHub](https://github.com/mistralai/mistral-src).

Related content:

- [S5E6 - On reçoit le PDG de Mistral, Arthur Mensch @ Underscore_&amp;#039;s acast :fr:](https://shows.acast.com/micode-underscore/episodes/s5e6-on-recoit-le-pdg-de-mistral-arthur-mensch).
- [Mistral AI lance son premier LLM : ouvert, mais jusqu’où ? @ Silicon :fr:](https://www.silicon.fr/mistral-ai-llm-471793.html).
- [Comment la communauté s’empare du modèle ouvert de Mistral AI @ Silicon :fr:](https://www.silicon.fr/communaute-modele-ouvert-mistral-ai-473858.html).]]>
            </summary>
            <updated>2025-08-29T05:04:54+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4729</id>
            <title type="text"><![CDATA[Qlik]]></title>
            <link rel="alternate" href="https://www.qlik.com/us" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4729"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Data Integration, Data Quality, &amp;amp; Analytics Solutions.

Qlik, now with Talend, delivers a data fabric for modern data architectures and next-gen analytics powered by Qlik Staige™, a suite of AI and machine learning capabilities.]]>
            </summary>
            <updated>2026-07-29T13:43:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4733</id>
            <title type="text"><![CDATA[Vigil]]></title>
            <link rel="alternate" href="https://vigil.deadbits.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4733"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[⚡ Vigil ⚡ Detect prompt injections, jailbreaks, and other potentially risky Large Language Model (LLM) inputs.

Vigil is a Python library and REST API for assessing Large Language Model prompts and responses against a set of scanners to detect prompt injections, jailbreaks, and other potential risks. 

- [Vigil @ GitHub](https://github.com/deadbits/vigil-llm).
- [Vigil: Open-source LLM security scanner](https://www.helpnetsecurity.com/2023/11/29/vigil-llm-security-scanner/).]]>
            </summary>
            <updated>2025-08-29T05:06:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4776</id>
            <title type="text"><![CDATA[Giskard]]></title>
            <link rel="alternate" href="https://www.giskard.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4776"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open-source Solution for AI Quality.
The testing framework dedicated to ML models, from tabular to LLMs
Scan AI models to detect risks of biases, performance issues and errors. In 4 lines of code. 

- [Giskard @ GitHub](https://github.com/Giskard-AI/giskard).
- [Introduction to Giskard: Open-Source Quality Management for AI Models @ KDnuggets](https://www.kdnuggets.com/2023/11/giskard-introduction-giskard-opensource-quality-management-ai-models).]]>
            </summary>
            <updated>2025-08-29T05:12:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4784</id>
            <title type="text"><![CDATA[MLflow]]></title>
            <link rel="alternate" href="https://www.mlflow.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4784"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A platform for the machine learning lifecycle.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow&amp;#039;s current components are:

- [MLflow @ GitHub](https://github.com/mlflow/mlflow/).
- [Setting up a Development Machine with MLFlow and MinIO @ MinIO Blog](https://blog.min.io/setting-up-a-development-machine-with-mlflow-and-minio/).]]>
            </summary>
            <updated>2025-08-29T05:15:00+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4786</id>
            <title type="text"><![CDATA[Doctor Dignity]]></title>
            <link rel="alternate" href="https://github.com/llSourcell/Doctor-Dignity/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4786"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Doctor Dignity is an LLM that can pass the US Medical Licensing Exam. It works offline, it&amp;#039;s cross-platform, &amp;amp; your health data stays private. 

- [Doctor Dignity – L’IA médicale open-source @ Korben :fr:](https://korben.info/doctor-dignity-intelligence-artificielle-medecine-open-source.html).]]>
            </summary>
            <updated>2025-08-29T05:15:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4794</id>
            <title type="text"><![CDATA[Pezzo]]></title>
            <link rel="alternate" href="https://pezzo.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4794"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Ship AI features in minutes.
Pezzo enables you to build, test, monitor and instantly ship AI all in one platform, while constantly optimizing for cost and performance.

 🕹️ Open-source, developer-first LLMOps platform designed to streamline prompt design, version management, instant delivery, collaboration, troubleshooting, observability and more. 

- [Pezzo @ GitHub](https://github.com/pezzolabs/pezzo).]]>
            </summary>
            <updated>2025-08-29T05:16:00+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4844</id>
            <title type="text"><![CDATA[Otter.ai]]></title>
            <link rel="alternate" href="https://otter.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4844"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Voice Meeting Notes &amp;amp; Real-time Transcription]]>
            </summary>
            <updated>2025-08-29T05:24:06+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4848</id>
            <title type="text"><![CDATA[Zoo]]></title>
            <link rel="alternate" href="https://zoo.replicate.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4848"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A playground for comparing AI image models.

- [Zoo @ GitHub](https://github.com/replicate/zoo).
- [Zoo – Pour jouer avec différents modèles de génération d’images @ Korben (fr)](https://korben.info/zoo-pour-jouer-avec-differents-modeles-de-generation-dimages.html).]]>
            </summary>
            <updated>2025-08-29T05:25:03+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4854</id>
            <title type="text"><![CDATA[roop]]></title>
            <link rel="alternate" href="https://github.com/s0md3v/roop" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4854"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[one-click face swap 

Take a video and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training.

- [Créez des deepfakes faciles avec une simple image ! @ Korben (fr)](https://korben.info/projet-roop-deepfakes-simplifies-ia-visage-remplacement-video.html).]]>
            </summary>
            <updated>2025-08-29T05:27:05+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4873</id>
            <title type="text"><![CDATA[CodeGeeX]]></title>
            <link rel="alternate" href="https://codegeex.cn/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4873"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Multilingual Code Generation Tool.

We introduce CodeGeeX, a large-scale multilingual code generation model with 13 billion parameters, pre-trained on a large code corpus of more than 20 programming languages. As of June 22, 2022, CodeGeeX has been trained on more than 850 billion tokens on a cluster of 1,536 Ascend 910 AI Processors.

- [CodeGeeX2  @ GitHub](https://github.com/THUDM/CodeGeeX2).
- [CodeGeeX @ GitHub](https://github.com/THUDM/CodeGeeX).]]>
            </summary>
            <updated>2025-08-29T05:29:06+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4877</id>
            <title type="text"><![CDATA[The Grand Complete Data Science Guide With Videos And Materials]]></title>
            <link rel="alternate" href="https://github.com/krishnaik06/The-Grand-Complete-Data-Science-Materials" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4877"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Contribute to krishnaik06/The-Grand-Complete-Data-Science-Materials development by creating an account on GitHub.]]>
            </summary>
            <updated>2025-08-29T05:31:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4922</id>
            <title type="text"><![CDATA[FlowGPT]]></title>
            <link rel="alternate" href="https://flowgpt.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4922"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Best ChatGPT Prompts &amp;amp; AI Prompts Community]]>
            </summary>
            <updated>2025-08-29T05:37:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4934</id>
            <title type="text"><![CDATA[Text generation web UI]]></title>
            <link rel="alternate" href="https://github.com/oobabooga/text-generation-webui" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4934"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Gradio web UI for Large Language Models. Supports transformers, GPTQ, llama.cpp (GGUF), Llama models.]]>
            </summary>
            <updated>2025-08-29T05:39:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4975</id>
            <title type="text"><![CDATA[txtai]]></title>
            <link rel="alternate" href="https://neuml.github.io/txtai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4975"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.

[txtai @ GitHub](https://github.com/neuml/txtai).]]>
            </summary>
            <updated>2025-08-29T05:47:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4992</id>
            <title type="text"><![CDATA[promptmap]]></title>
            <link rel="alternate" href="https://github.com/utkusen/promptmap" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4992"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[automatically tests prompt injection attacks on ChatGPT instances.

Prompt injection is a type of security vulnerability that can be exploited to control the behavior of a ChatGPT instance. By injecting malicious prompts into the system, an attacker can force the ChatGPT instance to do unintended actions.]]>
            </summary>
            <updated>2025-08-29T05:49:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4993</id>
            <title type="text"><![CDATA[Anse]]></title>
            <link rel="alternate" href="https://anse.app/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4993"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Supercharged experience for ChatGPT, DALL-E and Stable Diffusion. 

[Anse @ GitHub](https://github.com/anse-app/anse).]]>
            </summary>
            <updated>2025-08-29T05:49:17+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5013</id>
            <title type="text"><![CDATA[LIDA]]></title>
            <link rel="alternate" href="https://microsoft.github.io/lida/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5013"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Automatic Generation of Visualizations and Infographics with LLMs.

LIDA is a library for generating data visualizations and data-faithful infographics. LIDA is grammar agnostic (will work with any programming language and visualization libraries e.g. matplotlib, seaborn, altair, d3 etc) and works with multiple large language model providers (OpenAI, PaLM, Cohere, Huggingface). Details on the components of LIDA are described in the paper here and in this tutorial notebook. See the project page here for updates!.

[LIDA @ GitHub](https://github.com/microsoft/lida).]]>
            </summary>
            <updated>2025-08-29T05:52:18+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5021</id>
            <title type="text"><![CDATA[SQLCoder]]></title>
            <link rel="alternate" href="https://github.com/defog-ai/sqlcoder" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5021"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Defog&amp;#039;s SQLCoder is a state-of-the-art LLM for converting natural language questions to SQL queries.]]>
            </summary>
            <updated>2025-08-29T05:53:19+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5085</id>
            <title type="text"><![CDATA[Learn Prompting]]></title>
            <link rel="alternate" href="https://learnprompting.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5085"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Your Guide to Communicating with Artificial Intelligence.

Learn how to use ChatGPT and other AI tools to accomplish your goals using our free and open source curriculum, designed for all skill levels!

[Learn Prompting @ GitHub](https://github.com/trigaten/Learn_Prompting).]]>
            </summary>
            <updated>2025-08-29T06:04:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5104</id>
            <title type="text"><![CDATA[MITIE]]></title>
            <link rel="alternate" href="https://github.com/mit-nlp/MITIE" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5104"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[library and tools for information extraction.

This project provides free (even for commercial use) state-of-the-art information extraction tools. The current release includes tools for performing named entity extraction and binary relation detection as well as tools for training custom extractors and relation detectors.]]>
            </summary>
            <updated>2025-08-29T06:07:30+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5136</id>
            <title type="text"><![CDATA[Orange Data Mining]]></title>
            <link rel="alternate" href="https://orangedatamining.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5136"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Data Mining, Fruitful and Fun

Open source machine learning and data visualization.

Build data analysis workflows visually, with a large, diverse toolbox.

[Orange Data Mining @ GitHub](https://github.com/biolab/orange3).]]>
            </summary>
            <updated>2025-08-29T06:12:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5145</id>
            <title type="text"><![CDATA[Pigo]]></title>
            <link rel="alternate" href="https://github.com/esimov/pigo" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5145"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Pigo is a pure Go face detection, pupil/eyes localization and facial landmark points detection library based on the Pixel Intensity Comparison-based Object detection paper.]]>
            </summary>
            <updated>2025-08-29T06:15:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5154</id>
            <title type="text"><![CDATA[Skybox AI]]></title>
            <link rel="alternate" href="https://skybox.blockadelabs.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5154"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Skybox AI uses AI to generate full 360 degree panoramic images. 2 available mode tabs below give you full creative control of your skybox.]]>
            </summary>
            <updated>2025-08-29T06:15:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5155</id>
            <title type="text"><![CDATA[Ollama]]></title>
            <link rel="alternate" href="https://ollama.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5155"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Get up and running with large language models, locally.
Run Llama 2 and other models on macOS. Customize and create your own.

- [Ollama @ GitHub](https://github.com/jmorganca/ollama).
- [6 outils de FOU pour les DEVS 🤯 @ YoanDev&amp;#039;s YouTube](https://www.youtube.com/watch?v=x0niOhjzkxw).]]>
            </summary>
            <updated>2025-08-29T06:16:30+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5175</id>
            <title type="text"><![CDATA[ibm-nasa-geospatial (IBM NASA Geospatial) @ Huggin Face]]></title>
            <link rel="alternate" href="https://huggingface.co/ibm-nasa-geospatial" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5175"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[NASA and IBM have teamed up to create an AI Foundation Model for Earth Observations, using large-scale satellite and remote sensing data, including the Harmonized Landsat and Sentinel-2 (HLS) data. By embracing the principles of open AI and open science, both organizations are actively contributing to the global mission of promoting knowledge sharing and accelerating innovations in addressing critical environmental challenges. With Hugging Face&amp;#039;s platform, they simplify geospatial model training and deployment, making it accessible for open science users, startups, and enterprises on multi-cloud AI platforms like watsonx. Additionally, Hugging Face enables easy sharing of the pipelines of the model family, which our team calls Prithvi, within the community, fostering global collaboration and engagement.]]>
            </summary>
            <updated>2025-08-29T06:19:37+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5191</id>
            <title type="text"><![CDATA[face_recognition]]></title>
            <link rel="alternate" href="https://github.com/ageitgey/face_recognition" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5191"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The world&amp;#039;s simplest facial recognition api for Python and the command line.

Recognize and manipulate faces from Python or from the command line with the world&amp;#039;s simplest face recognition library.]]>
            </summary>
            <updated>2025-08-29T06:23:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5198</id>
            <title type="text"><![CDATA[OpenLLM]]></title>
            <link rel="alternate" href="https://github.com/bentoml/OpenLLM" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5198"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Operating LLMs in production.

An open platform for operating large language models (LLMs) in production.
Fine-tune, serve, deploy, and monitor any LLMs with ease.]]>
            </summary>
            <updated>2025-08-29T06:23:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5203</id>
            <title type="text"><![CDATA[Poly: AI Texture Engine]]></title>
            <link rel="alternate" href="https://withpoly.com/browse/textures" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5203"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Search 1000s of free seamless HD PBR textures. Create Textures With Poly.

Generate 3D materials with AI in a free online editor, or search our growing community library.]]>
            </summary>
            <updated>2025-08-29T06:23:42+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5250</id>
            <title type="text"><![CDATA[Clipdrop]]></title>
            <link rel="alternate" href="https://clipdrop.co/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5250"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI image edition toolbox]]>
            </summary>
            <updated>2025-08-29T06:31:43+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5263</id>
            <title type="text"><![CDATA[Vectorizer.AI]]></title>
            <link rel="alternate" href="https://vectorizer.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5263"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Trace Pixels To Vectors in Full Color, Fully Automatically, Using AI.

Convert your JPEG and PNG bitmaps to SVG vectors quickly and easily. Fully Automatically. Using AI.]]>
            </summary>
            <updated>2025-08-29T06:35:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5280</id>
            <title type="text"><![CDATA[AI Transcriptions by Riverside]]></title>
            <link rel="alternate" href="https://riverside.fm/transcription" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5280"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Accurate AI Transcriptions in Minutes.

Web service proposing to transcribe video and/or audio content using AI]]>
            </summary>
            <updated>2025-08-29T06:36:41+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5287</id>
            <title type="text"><![CDATA[LibreTranslate]]></title>
            <link rel="alternate" href="https://libretranslate.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5287"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Free and Open Source Machine Translation API. Self-hosted, offline capable and easy to setup. 

Unlike other APIs, it doesn&amp;#039;t rely on proprietary providers such as Google or Azure to perform translations. Instead, its translation engine is powered by the open source Argos Translate library.

- [LibreTranslate @ GitHub](https://github.com/LibreTranslate/LibreTranslate)]]>
            </summary>
            <updated>2025-08-29T06:37:42+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5295</id>
            <title type="text"><![CDATA[Yachay AI]]></title>
            <link rel="alternate" href="https://www.yachay.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5295"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[open-source geolocation.

Yachay is an open-source Machine Learning community. We have collected decades worth of useful natural language data from traditional media (i.e. New York Times articles), social media (i.e. Twitter &amp;amp; Reddit), messenger channels, tech blogs, GitHub profiles and issues, the dark web, and legal proceedings, as well as the decisions and publications of government regulators and legislators all across the world.

[Yachay @ GitHub](https://github.com/Yachay-AI/byt5-geotagging).]]>
            </summary>
            <updated>2025-08-29T06:39:46+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5314</id>
            <title type="text"><![CDATA[Segment Anything]]></title>
            <link rel="alternate" href="https://segment-anything.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5314"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Segment Anything Model (SAM): a new AI model from Meta AI that can &amp;quot;cut out&amp;quot; any object, in any image, with a single click.

SAM is a promptable segmentation system with zero-shot generalization to unfamiliar objects and images, without the need for additional training.

[Segment Anything @ GitHub](https://github.com/facebookresearch/segment-anything)]]>
            </summary>
            <updated>2025-08-29T06:43:45+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5353</id>
            <title type="text"><![CDATA[LAION-5B]]></title>
            <link rel="alternate" href="https://laion.ai/blog/laion-5b/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5353"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A NEW ERA OF OPEN LARGE-SCALE MULTI-MODAL DATASETS | LAION.

We present a dataset of 5,85 billion CLIP-filtered image-text pairs, 14x bigger than LAION-400M, previously the biggest openly accessible image-text dataset in the world]]>
            </summary>
            <updated>2025-08-29T06:48:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5387</id>
            <title type="text"><![CDATA[Milvus]]></title>
            <link rel="alternate" href="https://milvus.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5387"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.

- [Milvus @ GitHub](https://github.com/milvus-io/milvus).

Related contents:

- [RAG Against The Machine @ Quoi de neuf les devs ? :fr:](https://happytodev.substack.com/p/quoi-de-neuf-les-devs-153-veille?open=false#%C2%A7rag-against-the-machine).]]>
            </summary>
            <updated>2025-10-29T13:14:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5394</id>
            <title type="text"><![CDATA[Polymath]]></title>
            <link rel="alternate" href="https://github.com/samim23/polymath" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5394"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Convert any music library into a music production sample-library with ML.

Polymath uses machine learning to convert any music library (e.g from Hard-Drive or YouTube) into a music production sample-library. The tool automatically separates songs into stems (beats, bass, etc.), quantizes them to the same tempo and beat-grid (e.g. 120bpm), analyzes musical structure (e.g. verse, chorus, etc.), key (e.g C4, E3, etc.) and other infos (timbre, loudness, etc.), and converts audio to midi. The result is a searchable sample library that streamlines the workflow for music producers, DJs, and ML audio developers.]]>
            </summary>
            <updated>2025-08-29T06:56:05+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5400</id>
            <title type="text"><![CDATA[Faraday.dev]]></title>
            <link rel="alternate" href="https://faraday.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5400"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Run open-source LLMs on your computer. Works offline. Zero configuration.]]>
            </summary>
            <updated>2025-08-29T06:56:51+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5403</id>
            <title type="text"><![CDATA[Falcon LLM]]></title>
            <link rel="alternate" href="https://falconllm.tii.ae/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5403"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Falcon LLM is a foundational large language model (LLM) with 40 billion parameters trained on one trillion tokens. TII has now released Falcon LLM – a 40B model.]]>
            </summary>
            <updated>2025-08-29T06:57:50+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5423</id>
            <title type="text"><![CDATA[RedPajama-Data]]></title>
            <link rel="alternate" href="https://github.com/togethercomputer/RedPajama-Data" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5423"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The RedPajama-Data repository contains code for preparing large datasets for training large language models.
RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset.]]>
            </summary>
            <updated>2025-08-29T07:00:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5425</id>
            <title type="text"><![CDATA[Buzz Captions]]></title>
            <link rel="alternate" href="https://buzzcaptions.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5425"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Offline audio transcription and translation.

Transcribe and translate audio offline on your personal computer. Powered by OpenAI&amp;#039;s Whisper.

[Buzz Captions @ GitHub](https://github.com/chidiwilliams/buzz).]]>
            </summary>
            <updated>2025-08-29T07:00:53+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5440</id>
            <title type="text"><![CDATA[ImageBind]]></title>
            <link rel="alternate" href="https://github.com/facebookresearch/ImageBind" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5440"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[ImageBind One Embedding Space to Bind Them All.

PyTorch implementation and pretrained models for ImageBind. For details, see the paper: ImageBind: One Embedding Space To Bind Them All.

ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.]]>
            </summary>
            <updated>2025-08-29T07:03:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5452</id>
            <title type="text"><![CDATA[gpt4all]]></title>
            <link rel="alternate" href="https://github.com/nomic-ai/gpt4all" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5452"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.

Demo, data, and code to train open-source assistant-style large language model based on GPT-J and LLaMa]]>
            </summary>
            <updated>2025-08-29T07:05:55+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5463</id>
            <title type="text"><![CDATA[Bark]]></title>
            <link rel="alternate" href="https://github.com/suno-ai/bark" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5463"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🔊 Text-Prompted Generative Audio Model

Bark is a transformer-based text-to-audio model created by Suno. Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints, which are ready for inference and available for commercial use.]]>
            </summary>
            <updated>2025-08-29T07:07:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5478</id>
            <title type="text"><![CDATA[OpenCommit]]></title>
            <link rel="alternate" href="https://github.com/di-sukharev/opencommit" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5478"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[GPT CLI to auto-generate impressive commits in 1 second 🤯🔫]]>
            </summary>
            <updated>2025-08-29T07:09:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5483</id>
            <title type="text"><![CDATA[Easy Diffusion]]></title>
            <link rel="alternate" href="https://github.com/cmdr2/stable-diffusion-ui" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5483"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Easiest 1-click way to install and use Stable Diffusion on your computer. Provides a browser UI for generating images from text prompts and images. Just enter your text prompt, and see the generated image.]]>
            </summary>
            <updated>2025-08-29T07:11:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5486</id>
            <title type="text"><![CDATA[Semaphore]]></title>
            <link rel="alternate" href="https://github.com/everythingishacked/Semaphore" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5486"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A full-body keyboard using gestures to type through computer vision.

Semaphore uses OpenCV and MediaPipe&amp;#039;s Pose detection to perform real-time detection of body landmarks from video input. From there, relative differences are calculated to determine specific positions and translate those into keys and commands sent via keyboard.]]>
            </summary>
            <updated>2025-08-29T07:12:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5502</id>
            <title type="text"><![CDATA[StableLM:]]></title>
            <link rel="alternate" href="https://github.com/stability-AI/stableLM/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5502"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Stability AI Language Models.

This repository contains Stability AI&amp;#039;s ongoing development of the StableLM series of language models and will be continuously updated with new checkpoints. The following provides an overview of all currently available models. More coming soon.]]>
            </summary>
            <updated>2025-08-29T07:14:00+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5526</id>
            <title type="text"><![CDATA[AI Dungeon]]></title>
            <link rel="alternate" href="https://play.aidungeon.io/main/home" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5526"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Play and create AI-generated adventures with infinite possibilities. Not sure where to start?]]>
            </summary>
            <updated>2025-08-29T07:18:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5535</id>
            <title type="text"><![CDATA[AI Food Generator by Lunchbox]]></title>
            <link rel="alternate" href="https://ai.lunchbox.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5535"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Every image is uniquely generated by artificial intelligence. Food items that add an image see 70% more orders and 65% higher sales compared to restaurants that do not.]]>
            </summary>
            <updated>2025-08-29T07:20:14+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5540</id>
            <title type="text"><![CDATA[Lama Cleaner]]></title>
            <link rel="alternate" href="https://lama-cleaner-docs.vercel.app/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5540"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Lama Cleaner is a free, open-source and fully self-hostable inpainting tool powered by state-of-the-art AI models. You can use it to remove any unwanted object, defect, people from your pictures or erase and replace anything on your pictures.

[Lama Cleaner @ GitHub](https://github.com/Sanster/lama-cleaner)]]>
            </summary>
            <updated>2025-08-29T07:20:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5558</id>
            <title type="text"><![CDATA[k8sgpt]]></title>
            <link rel="alternate" href="https://github.com/k8sgpt-ai" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5558"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[K8sGPT is a tool for scanning your kubernetes clusters, diagnosing and triaging issues in simple english. It has SRE experience codified into it’s analyzers and helps to pull out the most relevant information to enrich it with AI.]]>
            </summary>
            <updated>2025-08-29T07:24:06+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5591</id>
            <title type="text"><![CDATA[Matchering]]></title>
            <link rel="alternate" href="https://github.com/sergree/matchering" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5591"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🎚️ Open Source Audio Matching and Mastering. Matchering 2.0 is a novel Containerized Web Application and Python Library for audio matching and mastering.

It follows a simple idea - you take TWO audio files and feed them into Matchering. Our algorithm matches both of these tracks and provides you the mastered TARGET track with the same RMS, FR, peak amplitude and stereo width as the REFERENCE track has.]]>
            </summary>
            <updated>2025-08-29T07:29:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5606</id>
            <title type="text"><![CDATA[Jema.ai]]></title>
            <link rel="alternate" href="https://jema.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5606"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Jasper alternative open source with ChatGPT.

This project uses ChatGPT API to create almost any text based output for your need - from marketing content to blog post ideas and a lot more. It uses simple template based components to ask ChatGPT for generating results Creating new templates or tasks take about 30 mins. no more, so you can extend it for your needs or wait for new template release :)

[Jema.ai @ GitHub](https://github.com/yuvalsuede/jasper-alternative-gpt)]]>
            </summary>
            <updated>2025-08-29T07:32:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5608</id>
            <title type="text"><![CDATA[AIcyclopedia]]></title>
            <link rel="alternate" href="https://www.aicyclopedia.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5608"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The free AI encyclopedia.
AI tools, podcasts, prompts, newsletter, and movies.]]>
            </summary>
            <updated>2025-08-29T07:32:13+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5616</id>
            <title type="text"><![CDATA[Stable Diffusion web UI]]></title>
            <link rel="alternate" href="https://github.com/AUTOMATIC1111/stable-diffusion-webui" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5616"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A browser interface based on Gradio library for Stable Diffusion.]]>
            </summary>
            <updated>2025-08-29T07:33:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5631</id>
            <title type="text"><![CDATA[Papers With Code]]></title>
            <link rel="alternate" href="https://paperswithcode.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5631"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The latest in Machine Learning]]>
            </summary>
            <updated>2025-08-29T07:36:14+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5635</id>
            <title type="text"><![CDATA[OpenChatKit]]></title>
            <link rel="alternate" href="https://github.com/togethercomputer/OpenChatKit" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5635"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[OpenChatKit provides a powerful, open-source base to create both specialized and general purpose chatbots for various applications. The kit includes an instruction-tuned 20 billion parameter language model, a 6 billion parameter moderation model, and an extensible retrieval system for including up-to-date responses from custom repositories. It was trained on the OIG-43M training dataset, which was a collaboration between Together, LAION, and Ontocord.ai. Much more than a model release, this is the beginning of an open source project. We are releasing a set of tools and processes for ongoing improvement with community contributions.]]>
            </summary>
            <updated>2025-08-29T07:36:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5643</id>
            <title type="text"><![CDATA[Whisper]]></title>
            <link rel="alternate" href="https://openai.com/index/whisper/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5643"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification.

[Whisper @ GitHub](https://github.com/openai/whisper).]]>
            </summary>
            <updated>2025-08-29T07:37:12+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5647</id>
            <title type="text"><![CDATA[writeout.ai – Transcribe and translate any audio file]]></title>
            <link rel="alternate" href="https://writeout.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5647"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Transcribe and translate any audio file.

Free, fast and accurate transcription of audio files. 100% free to use. 

[writeout.ai @ GitHub](https://github.com/beyondcode/writeout.ai)]]>
            </summary>
            <updated>2025-08-29T07:38:12+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5668</id>
            <title type="text"><![CDATA[Enhance Speech from Adobe]]></title>
            <link rel="alternate" href="https://podcast.adobe.com/enhance" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5668"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Free AI filter for cleaning up spoken audio. Enhance voice recordings for free.

Speech enhancement makes voice recordings sound as if they were recorded in a professional studio.]]>
            </summary>
            <updated>2025-08-29T07:41:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5693</id>
            <title type="text"><![CDATA[DalleCli]]></title>
            <link rel="alternate" href="https://dallecli.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5693"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Generate, Edit &amp;amp; Filter images using the DALL-E 2 API

`dallecli` is a command line app designed to provide users with the ability to generate, edit and filter images using the DALL-E 2 API provided by OpenAI.

[DalleCLI @ GitHub](https://github.com/raiyanyahya/dallecli).]]>
            </summary>
            <updated>2025-08-29T07:46:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5722</id>
            <title type="text"><![CDATA[Scribble Diffusion]]></title>
            <link rel="alternate" href="https://scribblediffusion.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5722"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Turn your sketch into a refined image using AI.

[Scribble Diffusion @ GitHub](https://github.com/replicate/scribble-diffusion).]]>
            </summary>
            <updated>2025-08-29T07:50:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5735</id>
            <title type="text"><![CDATA[Coqui STT]]></title>
            <link rel="alternate" href="https://coqui.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5735"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Coqui STT (frogSTT) is a fast, open-source, multi-platform, deep-learning toolkit for training and deploying speech-to-text models. frogSTT is battle tested in both production and research rocket

[Coqui STT @ GitHub](https://github.com/coqui-ai/STT).]]>
            </summary>
            <updated>2025-08-29T07:52:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5749</id>
            <title type="text"><![CDATA[Steampunk Spotter]]></title>
            <link rel="alternate" href="https://steampunk.si/spotter/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5749"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Trustable Automation. Steampunk Spotter provides an Assisted Automation Writing tool that analyzes and offers recommendations for your Ansible Playbooks.]]>
            </summary>
            <updated>2025-08-29T07:56:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5802</id>
            <title type="text"><![CDATA[demucs]]></title>
            <link rel="alternate" href="https://github.com/facebookresearch/demucs" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5802"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Code for the paper Hybrid Spectrogram and Waveform Source Separation.

Demucs is a state-of-the-art music source separation model, currently capable of separating drums, bass, and vocals from the rest of the accompaniment.]]>
            </summary>
            <updated>2025-08-29T08:04:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5849</id>
            <title type="text"><![CDATA[Pose Animator]]></title>
            <link rel="alternate" href="https://github.com/yemount/pose-animator/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5849"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Pose Animator takes a 2D vector illustration and animates its containing curves in real-time based on the recognition result from PoseNet and FaceMesh. It borrows the idea of skeleton-based animation from computer graphics and applies it to vector characters.]]>
            </summary>
            <updated>2025-08-29T08:12:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5858</id>
            <title type="text"><![CDATA[aitools.fyi]]></title>
            <link rel="alternate" href="https://aitools.fyi/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5858"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Find Amazing AI Tools That Make Your Life Easy!]]>
            </summary>
            <updated>2025-08-29T08:13:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5862</id>
            <title type="text"><![CDATA[PhotoPrism]]></title>
            <link rel="alternate" href="https://www.photoprism.app/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5862"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[PhotoPrism® is an AI-Powered Photos App for the Decentralized Web.

It makes use of the latest technologies to tag and find pictures automatically without getting in your way. You can run it at home, on a private server, or in the cloud. 

[PhotoPrism @ GitHub](https://github.com/photoprism/photoprism)]]>
            </summary>
            <updated>2025-08-29T08:14:30+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5895</id>
            <title type="text"><![CDATA[Mycroft]]></title>
            <link rel="alternate" href="https://mycroft.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5895"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Open Source Privacy-Focused Voice Assistant.

Mycroft is the world’s leading open source voice assistant. It is private by default and completely customizable.

[Mycroft @ GitHub](https://github.com/MycroftAI).]]>
            </summary>
            <updated>2025-08-29T08:20:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5896</id>
            <title type="text"><![CDATA[Optuna]]></title>
            <link rel="alternate" href="https://optuna.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5896"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A hyperparameter optimization framework. Optimize Your Optimization.

An open source hyperparameter optimization framework to automate hyperparameter search

[Optuna @ GitHub](https://github.com/optuna/optuna).]]>
            </summary>
            <updated>2025-08-29T08:20:36+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5897</id>
            <title type="text"><![CDATA[fastai]]></title>
            <link rel="alternate" href="https://github.com/fastai/fastai" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5897"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes:]]>
            </summary>
            <updated>2025-08-29T08:20:36+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5910</id>
            <title type="text"><![CDATA[Qdrant - Vector Search Engine]]></title>
            <link rel="alternate" href="https://qdrant.tech/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5910"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Qdrant (read: quadrant ) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.

- [Qdrant @ GitHub](https://github.com/qdrant/qdrant).

Related contents:

- [270 - DB Vectorielle - Noé Achache @ &amp;lt;ifttd&amp;gt; :fr:](https://www.ifttd.io/episodes/db-vectorielle).
- [Episode 641: Qdrant&amp;#039;s Brian O&amp;#039;Grady @ Coder Radio](https://coder.show/641).]]>
            </summary>
            <updated>2026-03-12T19:36:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5982</id>
            <title type="text"><![CDATA[There&amp;#039;s An AI For That | AI Database]]></title>
            <link rel="alternate" href="https://theresanaiforthat.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5982"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI use cases search engine.]]>
            </summary>
            <updated>2025-08-29T08:33:41+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6015</id>
            <title type="text"><![CDATA[Calligrapher.ai]]></title>
            <link rel="alternate" href="https://www.calligrapher.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6015"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Realistic computer-generated handwriting]]>
            </summary>
            <updated>2025-08-29T08:40:45+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6033</id>
            <title type="text"><![CDATA[imaginAIry]]></title>
            <link rel="alternate" href="https://github.com/brycedrennan/imaginAIry" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6033"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI imagined images. Pythonic generation of stable diffusion images.]]>
            </summary>
            <updated>2025-08-29T08:42:44+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6080</id>
            <title type="text"><![CDATA[Lorem Faces]]></title>
            <link rel="alternate" href="https://loremfaces.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6080"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Free UI faces for designers, avatars, dummy faces, AI generated people faces.
UI faces for your web projects and designs AI generated people faces you can use in your works for free]]>
            </summary>
            <updated>2025-08-29T08:50:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6095</id>
            <title type="text"><![CDATA[OpenAI Cookbook]]></title>
            <link rel="alternate" href="https://github.com/openai/openai-cookbook" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6095"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[This repository shares example code and example prompts for accomplishing common tasks with the OpenAI API.
To try these examples yourself, you’ll need an OpenAI account. Create a free account to get started.
Most code examples are written in Python, though the concepts can be applied in any language.
In the same way that a cookbook&amp;#039;s recipes don&amp;#039;t span all possible meals or techniques, these examples don&amp;#039;t span all possible use cases or methods. Use them as starting points upon which to elaborate, discover, and invent.]]>
            </summary>
            <updated>2025-08-29T08:53:05+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6172</id>
            <title type="text"><![CDATA[dbt Learn]]></title>
            <link rel="alternate" href="https://courses.getdbt.com/collections" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6172"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Level up your dbt™ skills on your own time with dbt Fundamentals.Learn Analytics Engineering with dbt.]]>
            </summary>
            <updated>2025-08-29T09:05:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6173</id>
            <title type="text"><![CDATA[NLP Cheat Sheet]]></title>
            <link rel="alternate" href="https://github.com/janlukasschroeder/nlp-cheat-sheet-python" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6173"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Introduction to Natural Language Processing (NLP) tools, frameworks, concepts, resources for Python]]>
            </summary>
            <updated>2025-08-29T09:05:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6179</id>
            <title type="text"><![CDATA[YOLOv5]]></title>
            <link rel="alternate" href="https://github.com/ultralytics/yolov5" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6179"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[YOLOv5 in PyTorch &amp;gt; ONNX &amp;gt; CoreML &amp;gt; TFLite.
YOLOv5 is the world&amp;#039;s most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.]]>
            </summary>
            <updated>2025-08-29T09:06:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6182</id>
            <title type="text"><![CDATA[Advanced NLP with spaCy · A free online course]]></title>
            <link rel="alternate" href="https://course.spacy.io/en" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6182"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[spaCy is a modern Python library for industrial-strength Natural Language Processing. In this free and interactive online course, you&amp;#039;ll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.]]>
            </summary>
            <updated>2025-08-29T09:07:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6193</id>
            <title type="text"><![CDATA[Diffusion Bee]]></title>
            <link rel="alternate" href="https://github.com/divamgupta/diffusionbee-stable-diffusion-ui" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6193"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Stable Diffusion GUI App for MacOS.
Diffusion Bee is the easiest way to run Stable Diffusion locally on your Intel / M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.]]>
            </summary>
            <updated>2025-08-29T09:09:03+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6198</id>
            <title type="text"><![CDATA[Gauss]]></title>
            <link rel="alternate" href="https://github.com/justjake/Gauss" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6198"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Stable Diffusion app for macOS built with SwiftUI and Apple&amp;#039;s ml-stable-diffusion CoreML models.]]>
            </summary>
            <updated>2025-08-29T09:10:02+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6205</id>
            <title type="text"><![CDATA[Machine Learning Glossary]]></title>
            <link rel="alternate" href="https://ml-cheatsheet.readthedocs.io/en/latest/index.html" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6205"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.]]>
            </summary>
            <updated>2025-08-29T09:11:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6262</id>
            <title type="text"><![CDATA[chaiNNer]]></title>
            <link rel="alternate" href="https://chainner.app/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6262"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A node-based image processing and AI upscaling GUI that makes it easy to chain together complex processing tasks.
A flowchart/node-based image processing GUI aimed at making chaining image processing tasks (especially upscaling done by neural networks) easy, intuitive, and customizable.
No existing upscaling GUI gives you the level of customization of your image processing workflow that chaiNNer does. Not only do you have full control over your processing pipeline, you can do incredibly complex tasks just by connecting a few nodes together.

- [chaiNNer @ GitHub](https://github.com/chaiNNer-org/chaiNNer).]]>
            </summary>
            <updated>2025-08-29T09:21:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6277</id>
            <title type="text"><![CDATA[Ansible Ai]]></title>
            <link rel="alternate" href="https://ansible.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6277"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI for Ansible Content Development.
Easily Generate, Customize, &amp;amp; Use!
Save time and get unstuck. Tell ansible.ai what you’re thinking to automate in your IT infrastructure and it will generate syntactically correct playbook to help you get there.]]>
            </summary>
            <updated>2025-08-29T09:23:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6284</id>
            <title type="text"><![CDATA[PimEyes]]></title>
            <link rel="alternate" href="https://pimeyes.com/en" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6284"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Face Recognition Search Engine and Reverse Image Search.
Upload photo and find out where images are published.]]>
            </summary>
            <updated>2025-08-29T09:25:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6289</id>
            <title type="text"><![CDATA[Basic Pitch]]></title>
            <link rel="alternate" href="https://basicpitch.spotify.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6289"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Basic Pitch, a free audio-to-MIDI converter with pitch bend detection, built by Spotify.
Basic Pitch is a Python library for Automatic Music Transcription (AMT), using lightweight neural network developed by Spotify&amp;#039;s Audio Intelligence Lab.

[Basic Pitch @ GitHub](https://github.com/spotify/basic-pitch)]]>
            </summary>
            <updated>2025-08-29T09:25:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6360</id>
            <title type="text"><![CDATA[VToonify]]></title>
            <link rel="alternate" href="https://github.com/williamyang1991/VToonify" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6360"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[VToonify: Controllable High-Resolution Portrait Video Style Transfer.
[SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer.
This repository provides the official PyTorch implementation for the following paper:]]>
            </summary>
            <updated>2025-08-29T09:37:18+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6507</id>
            <title type="text"><![CDATA[This X Does Not Exist]]></title>
            <link rel="alternate" href="https://thisxdoesnotexist.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6507"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Using generative adversarial networks (GAN), we can learn how to create realistic-looking fake versions of almost anything, as shown by this collection of sites that have sprung up in the past month.]]>
            </summary>
            <updated>2025-08-29T10:01:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6511</id>
            <title type="text"><![CDATA[Cleanup.pictures]]></title>
            <link rel="alternate" href="https://cleanup.pictures/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6511"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Remove objects, people, text and defects from any picture for free]]>
            </summary>
            <updated>2025-08-29T10:02:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6533</id>
            <title type="text"><![CDATA[MLflow]]></title>
            <link rel="alternate" href="https://mlflow.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6533"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An open source platform for the machine learning lifecycle.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud).

- [MLflow @ GitHub](https://github.com/mlflow/mlflow).]]>
            </summary>
            <updated>2025-08-29T10:05:36+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6541</id>
            <title type="text"><![CDATA[ClipDrop - Relight]]></title>
            <link rel="alternate" href="https://clipdrop.co/relight" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6541"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Relight your photos &amp;amp; drawings in seconds]]>
            </summary>
            <updated>2025-08-29T10:07:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6542</id>
            <title type="text"><![CDATA[PromptHero]]></title>
            <link rel="alternate" href="https://prompthero.com/?ref=CtrlAltCC" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6542"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Search prompts for Stable Diffusion, DALL-E &amp;amp; Midjourney.
Search millions of art images by AI models like DALL-E, Stable Diffusion, Midjourney...]]>
            </summary>
            <updated>2025-08-29T10:07:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6565</id>
            <title type="text"><![CDATA[Runway]]></title>
            <link rel="alternate" href="https://runwayml.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6565"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Next-generation creation suite.
Everything you need to make content, fast.
Magical AI tools, realtime collaboration, precision editing, and more. Your next-generation content creation suite.]]>
            </summary>
            <updated>2025-08-29T10:11:32+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6594</id>
            <title type="text"><![CDATA[gistlib]]></title>
            <link rel="alternate" href="https://gistlib.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6594"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[find how to do things in code.
ask questions, get answers.
Gistlib uses artificial intelligence to help you find answers to your programming questions.]]>
            </summary>
            <updated>2025-08-29T10:17:36+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6600</id>
            <title type="text"><![CDATA[Open-Capture]]></title>
            <link rel="alternate" href="https://github.com/edissyum/opencapture" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6600"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open-Capture is the one and only 100% Open Source intelligent capture managment.]]>
            </summary>
            <updated>2025-08-29T10:17:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6610</id>
            <title type="text"><![CDATA[Gnod - The Global Network Of Discovery]]></title>
            <link rel="alternate" href="https://www.gnod.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6610"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An AI recommendation engine to discover new music, movies, art and more.]]>
            </summary>
            <updated>2025-08-29T10:18:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6627</id>
            <title type="text"><![CDATA[FauxPilot]]></title>
            <link rel="alternate" href="https://github.com/moyix/fauxpilot" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6627"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[an open-source GitHub Copilot server.
This is an attempt to build a locally hosted version of GitHub Copilot. It uses the SalesForce CodeGen models inside of NVIDIA&amp;#039;s Triton Inference Server with the FasterTransformer backend.]]>
            </summary>
            <updated>2025-08-29T10:21:43+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6628</id>
            <title type="text"><![CDATA[YOLOv7]]></title>
            <link rel="alternate" href="https://github.com/WongKinYiu/yolov7" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6628"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.]]>
            </summary>
            <updated>2025-08-29T10:21:44+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6629</id>
            <title type="text"><![CDATA[DeepFaceLab]]></title>
            <link rel="alternate" href="https://github.com/iperov/DeepFaceLab" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6629"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[DeepFaceLab is the leading software for creating deepfakes.]]>
            </summary>
            <updated>2025-08-29T10:21:45+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6630</id>
            <title type="text"><![CDATA[PaddleNLP]]></title>
            <link rel="alternate" href="https://github.com/PaddlePaddle/PaddleNLP" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6630"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[PaddleNLP is an easy-to-use and powerful natural language processing development library. Aggregates high-quality pre-trained models in the industry and provides an out -of-the-box development experience. The model library covering multiple scenarios of NLP and industrial practice examples can meet the needs of developers for flexible customization .]]>
            </summary>
            <updated>2025-08-29T10:22:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6631</id>
            <title type="text"><![CDATA[MindsDB]]></title>
            <link rel="alternate" href="https://github.com/mindsdb/mindsdb" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6631"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[In-Database Machine Learning.
MindsDB ML-SQL Server enables machine learning workflows for the most powerful databases and data warehouses using SQL.]]>
            </summary>
            <updated>2025-08-29T10:22:40+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6632</id>
            <title type="text"><![CDATA[Image Super-Resolution (ISR)]]></title>
            <link rel="alternate" href="https://github.com/idealo/image-super-resolution" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6632"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
The goal of this project is to upscale and improve the quality of low resolution images.
This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components.]]>
            </summary>
            <updated>2025-08-29T10:22:41+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6633</id>
            <title type="text"><![CDATA[DeepPavlov]]></title>
            <link rel="alternate" href="https://deeppavlov.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6633"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An open source conversational AI framework.
DeepPavlov makes it easy for beginners and experts to create dialogue systems.

- [DeepPavlov @ GitHub](https://github.com/deeppavlov/DeepPavlov).
- [DeepPavlov Agent @ GitHub](https://github.com/deeppavlov/dp-agent).

Related contents:

- [DeepPavlov Agent: Open-source Framework for Multiskill Conversational AI - ML Conf EU 2020 @ GitNation](https://gitnation.com/contents/deeppavlov-agent-open-source-framework-for-multiskill-conversational-ai).]]>
            </summary>
            <updated>2025-08-29T10:22:41+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6634</id>
            <title type="text"><![CDATA[OpenCV]]></title>
            <link rel="alternate" href="https://opencv.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6634"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open Computer Vision. Open source machine learning library for computer vision.

- [OpenCV @ GitHub](https://github.com/opencv/opencv).
- [OpenCV on Wheels @ GitHub](https://github.com/opencv/opencv-python).

Related contents:

- [OpenCV Course - Full Tutorial with Python @ freeCodeCamp.org&amp;#039;s YouTube](https://www.youtube.com/watch?v=oXlwWbU8l2o).]]>
            </summary>
            <updated>2025-08-29T10:22:41+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6637</id>
            <title type="text"><![CDATA[Bumble&amp;#039;s Private Detector]]></title>
            <link rel="alternate" href="https://github.com/bumble-tech/private-detector" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6637"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[a pretrained model for detecting lewd images.
This is the repo for Bumble&amp;#039;s Private Detector™ model - an image classifier that can detect lewd images.

The internal repo has been heavily refactored and released as a fully open-source project to allow for the wider community to use and finetune a Private Detector model of their own. You can download the pretrained SavedModel and checkpoint here]]>
            </summary>
            <updated>2025-08-29T10:23:40+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6649</id>
            <title type="text"><![CDATA[Palette - Colorize Photos]]></title>
            <link rel="alternate" href="https://palette.fm/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6649"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Colorize Black&amp;amp;White Pictures with machine learning.
Automatic, no sign-up, and Free!]]>
            </summary>
            <updated>2025-08-29T10:25:43+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6652</id>
            <title type="text"><![CDATA[Neo4j]]></title>
            <link rel="alternate" href="https://neo4j.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6652"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Graph Database Management System.
Neo4j Graph Data Platform. Blazing-Fast Graph, Petabyte Scale.
With proven trillion+ entity performance, developers, data scientists, and enterprises rely on Neo4j as the top choice for high-performance, scalable analytics, intelligent app development, and advanced AI/ML pipelines.

- [Neo4j @ GitHub](https://github.com/neo4j/neo4j).
 
Related contents:

- [Episode #18: NoSQL Smackdown! @ Changelog Interviews](https://changelog.com/podcast/18).]]>
            </summary>
            <updated>2025-12-09T09:25:32+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6663</id>
            <title type="text"><![CDATA[Magic Eraser]]></title>
            <link rel="alternate" href="https://www.magiceraser.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6663"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Remove unwanted things from images in seconds.
Upload an image, mark the bit you need removed, download the fixed up image.]]>
            </summary>
            <updated>2025-08-29T10:27:43+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6748</id>
            <title type="text"><![CDATA[Vowpal Wabbit]]></title>
            <link rel="alternate" href="https://vowpalwabbit.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6748"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Vowpal Wabbit provides fast, efficient, and flexible online machine learning techniques for reinforcement learning, supervised learning, and more. It is influenced by an ecosystem of community contributions, academic research, and proven algorithms. Microsoft Research is a major contributor to Vowpal Wabbit.]]>
            </summary>
            <updated>2025-08-29T10:41:53+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6754</id>
            <title type="text"><![CDATA[Datafari Enterprise Search]]></title>
            <link rel="alternate" href="https://www.datafari.com/en/index.html" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6754"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Datafari Enterprise Search finds your data wherever they are, whatever they are. Available as Open source, Datafari comes with many connectors, a big data architecture, advanced use of Articifial Intelligence and Machine Learning to optimise Relevancy.]]>
            </summary>
            <updated>2025-08-29T10:42:51+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6759</id>
            <title type="text"><![CDATA[Upscayl]]></title>
            <link rel="alternate" href="https://upscayl.github.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6759"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Free and Open Source AI Image Upscaler for Linux, MacOS and Windows built with Linux-First philosophy.

[Upscayl @ GitHub](https://github.com/upscayl/upscayl).]]>
            </summary>
            <updated>2025-08-29T10:43:50+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6770</id>
            <title type="text"><![CDATA[Kubeflow]]></title>
            <link rel="alternate" href="https://www.kubeflow.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6770"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Machine Learning Toolkit for Kubernetes
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow.

- [Kubeflow @ GitHub](https://github.com/kubeflow/).

Related contents:

- [CNCF Announces Kubeflow Graduation @ DevOps digest](https://www.devopsdigest.com/cncf-announces-kubeflow-graduation).]]>
            </summary>
            <updated>2026-08-21T12:13:45+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6782</id>
            <title type="text"><![CDATA[Scraft]]></title>
            <link rel="alternate" href="https://www.scraft.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6782"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI-powered writing tool that helps you organize ideas and craft better arguments by asking mind-stimulating questions like Socrates]]>
            </summary>
            <updated>2025-08-29T10:47:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6783</id>
            <title type="text"><![CDATA[Practical Deep Learning for Coders]]></title>
            <link rel="alternate" href="https://course.fast.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6783"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.]]>
            </summary>
            <updated>2025-08-29T10:47:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6803</id>
            <title type="text"><![CDATA[Whisper]]></title>
            <link rel="alternate" href="https://github.com/openai/whisper" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6803"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.]]>
            </summary>
            <updated>2025-08-29T10:50:56+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6821</id>
            <title type="text"><![CDATA[Open Neural Network Exchange]]></title>
            <link rel="alternate" href="https://onnx.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6821"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers.]]>
            </summary>
            <updated>2025-08-29T10:54:02+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6853</id>
            <title type="text"><![CDATA[CARS]]></title>
            <link rel="alternate" href="https://github.com/CNES/cars" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6853"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[CARS is an open source 3D tool dedicated to produce Digital Surface Models from satellite imaging by photogrammetry. This Multiview Stereo framework is intended for massive DSM production with a robust, performant and modular design.]]>
            </summary>
            <updated>2025-08-29T10:59:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6911</id>
            <title type="text"><![CDATA[✍️ Mintlify Writer]]></title>
            <link rel="alternate" href="https://github.com/mintlify/writer" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6911"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI powered documentation writer.

Writing documentation sucks. Let Mintlify take care of it. Just hightlight code and see the magic.]]>
            </summary>
            <updated>2025-08-29T11:10:05+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6958</id>
            <title type="text"><![CDATA[Photo restoration with GFP-GAN]]></title>
            <link rel="alternate" href="https://app.baseten.co/apps/QPp4nPE/operator_views/RqgOnqV" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6958"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Restore old photos using our photo restoration application, built with Baseten and powered by the GFP-GAN model.]]>
            </summary>
            <updated>2025-08-29T11:18:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6959</id>
            <title type="text"><![CDATA[GFPGAN]]></title>
            <link rel="alternate" href="https://github.com/TencentARC/GFPGAN" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6959"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration.
It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration.]]>
            </summary>
            <updated>2025-08-29T11:18:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6973</id>
            <title type="text"><![CDATA[DALL·E 2]]></title>
            <link rel="alternate" href="https://openai.com/dall-e-2/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6973"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language.]]>
            </summary>
            <updated>2025-08-29T11:19:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7141</id>
            <title type="text"><![CDATA[Sieve]]></title>
            <link rel="alternate" href="https://sievedata.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7141"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Transform raw video into high quality datasets in minutes.
Find the most important images within petabytes of raw video, in just a few lines of code.]]>
            </summary>
            <updated>2025-08-29T11:47:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7220</id>
            <title type="text"><![CDATA[Bagage minimal de statistiques pour le Machine Learning - datacorner par Benoit Cayla]]></title>
            <link rel="alternate" href="https://www.datacorner.fr/statistiques-min/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7220"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Bagage minimal de statistiques pour le Machine Learning]]>
            </summary>
            <updated>2025-08-29T12:00:30+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7221</id>
            <title type="text"><![CDATA[Types of Data &amp;amp; the Scales of Measurement | UNSW Online]]></title>
            <link rel="alternate" href="https://studyonline.unsw.edu.au/blog/types-of-data" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7221"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Learn what data is and discover how understanding the types of data will enable you to inform business strategies and effect change.]]>
            </summary>
            <updated>2025-08-29T12:00:32+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7227</id>
            <title type="text"><![CDATA[Brain.js]]></title>
            <link rel="alternate" href="https://brain.js.org/#/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7227"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[GPU accelerated Neural networks in JavaScript for Browsers and Node.js 

- [Brain.js @ GitHub](https://github.com/BrainJS/brain.js).]]>
            </summary>
            <updated>2025-08-29T12:02:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7228</id>
            <title type="text"><![CDATA[TensorFlow.js | Machine Learning for JavaScript Developers]]></title>
            <link rel="alternate" href="https://www.tensorflow.org/js" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7228"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Develop ML models in JavaScript, and use ML directly in the browser or in Node.js.]]>
            </summary>
            <updated>2025-08-29T12:02:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7229</id>
            <title type="text"><![CDATA[mind.js]]></title>
            <link rel="alternate" href="https://github.com/stevenmiller888/mind" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7229"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A flexible neural network library for Node.js and the browser]]>
            </summary>
            <updated>2025-08-29T12:02:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7230</id>
            <title type="text"><![CDATA[neuro.js]]></title>
            <link rel="alternate" href="https://neuro.js.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7230"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Neuro.js is machine learning framework for building AI assistants and chat-bots.]]>
            </summary>
            <updated>2025-08-29T12:02:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7231</id>
            <title type="text"><![CDATA[ConvNetJS: Deep Learning in your browser]]></title>
            <link rel="alternate" href="https://cs.stanford.edu/people/karpathy/convnetjs/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7231"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you&amp;#039;re training. No software requirements, no compilers, no installations, no GPUs, no sweat.]]>
            </summary>
            <updated>2025-08-29T12:02:36+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7232</id>
            <title type="text"><![CDATA[ml5js·Friendly Machine Learning For The Web]]></title>
            <link rel="alternate" href="https://ml5js.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7232"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.]]>
            </summary>
            <updated>2025-08-29T12:02:37+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7233</id>
            <title type="text"><![CDATA[TensorFlow]]></title>
            <link rel="alternate" href="https://www.tensorflow.org/?hl=en" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7233"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.]]>
            </summary>
            <updated>2025-08-29T12:02:37+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7247</id>
            <title type="text"><![CDATA[Kaggle]]></title>
            <link rel="alternate" href="https://www.kaggle.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7247"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Your Machine Learning and Data Science Community.

Inside Kaggle you’ll find all the code &amp;amp; data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.]]>
            </summary>
            <updated>2025-08-29T12:06:32+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7313</id>
            <title type="text"><![CDATA[PHP-ML]]></title>
            <link rel="alternate" href="https://github.com/php-ai/php-ml" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7313"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library.]]>
            </summary>
            <updated>2025-08-29T12:16:43+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7387</id>
            <title type="text"><![CDATA[NNabla]]></title>
            <link rel="alternate" href="https://nnabla.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7387"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Neural Network Libraries NNabla is a deep learning framework that is intended to be used for research, development and production. We aim it running everywhere like desktop PCs, HPC clusters, embedded devices and production servers.

- [NNabla @ GitHub](https://github.com/sony/nnabla).]]>
            </summary>
            <updated>2025-08-29T12:28:47+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7446</id>
            <title type="text"><![CDATA[openpilot]]></title>
            <link rel="alternate" href="https://comma.ai/openpilot" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7446"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[openpilot is an open source driving agent.

openpilot is an operating system for robotics. Currently, it upgrades the driver assistance system on 275+ supported cars. 

- [openpilot @ GitHub](https://github.com/commaai/openpilot).

Related contents:

- [Les constructeurs européens sont en train de rater le train de la conduite autonome et ça fait chier @ Korben :fr:](https://korben.info/xiaomi-mise-millions-clips-battre-tesla.html).
- [Tomb Raider - La nouvelle arme de Comma pour dominer la conduite autonome @ Korben :fr:](https://korben.info/openpilot-010-tomb-raider.html).
- [Comma 4 + openpilot 0.11 - La conduite assistée open source passe un cap @ Korben :fr:](https://korben.info/comma-ai-openpilot-v011-comma-4.html).]]>
            </summary>
            <updated>2026-03-23T16:11:41+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7472</id>
            <title type="text"><![CDATA[Torch]]></title>
            <link rel="alternate" href="http://torch.ch/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7472"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Scientific computing for LuaJIT.
Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.]]>
            </summary>
            <updated>2025-08-29T12:43:00+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7655</id>
            <title type="text"><![CDATA[Smile - Statistical Machine Intelligence and Learning Engine]]></title>
            <link rel="alternate" href="http://haifengl.github.io/smile" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7655"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Smile is a fast and comprehensive machine learning system.]]>
            </summary>
            <updated>2025-08-29T13:13:13+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7696</id>
            <title type="text"><![CDATA[Leaf]]></title>
            <link rel="alternate" href="https://github.com/autumnai/leaf" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7696"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Hacker&amp;#039;s Machine Learning Engine.
Leaf is a Machine Intelligence Framework engineered by software developers, not scientists. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf is lean and tries to introduce minimal technical debt to your stack.]]>
            </summary>
            <updated>2025-08-29T13:20:15+00:00</updated>
        </entry>
    </feed>
