<?xml version="1.0" encoding="UTF-8"?>
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    <title>format</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/28/feed"/>
    <updated>2026-11-03T21:39:07+00:00</updated>
    <id>https://links.biapy.com/guest/tags/28/feed</id>
            <entry>
            <id>https://links.biapy.com/links/13164</id>
            <title type="text"><![CDATA[Open Knowledge Format (OKF)]]></title>
            <link rel="alternate" href="https://github.com/GoogleCloudPlatform/knowledge-catalog/tree/main/okf" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13164"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[OKF is a universal, vendor-neutral format for representing knowledge as plain markdown files with YAML frontmatter. It is not tied to any particular agent, framework, model provider, or serving system.

Related contents:

- [Introducing the Open Knowledge Format @ Google Cloud Blog](https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing).
- [LLM Wiki @ Andrej karpathy&amp;#039;s GitHub Gist](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f).]]>
            </summary>
            <updated>2026-06-30T13:39:11+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12768</id>
            <title type="text"><![CDATA[Lance]]></title>
            <link rel="alternate" href="https://lance.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12768"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Open Lakehouse Format for Multimodal AI.

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 @ GitHub](https://github.com/lance-format/lance).

Related contents:

- [S3 is the perfect place to store data, until you try to search it @ Gordon Murray](https://gordonmurray.ie/data/2026/05/02/s3-is-the-perfect-place-to-store-data-until-you-try-to-search-it.html).]]>
            </summary>
            <updated>2026-05-15T13:58:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12736</id>
            <title type="text"><![CDATA[IGVM]]></title>
            <link rel="alternate" href="https://github.com/microsoft/igvm" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12736"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[This project is the home of the Independent Guest Virtual Machine (IGVM) file format. The format specification can be found in the igvm_defs crate, with a Rust implementation of the binary format in the igvm crate.

The IGVM file format is designed to encapsulate all information required to launch a virtual machine on any given virtualization stack, with support for different isolation technologies such as AMD SEV-SNP and Intel TDX.

Related contents:

- [The IGVM File Format @ Quarkslab&amp;#039;s Blog](https://blog.quarkslab.com/the-igvm-file-format.html).]]>
            </summary>
            <updated>2026-05-14T21:28:49+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11775</id>
            <title type="text"><![CDATA[Parquet]]></title>
            <link rel="alternate" href="https://parquet.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11775"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Apache Parquet is an open source, column-oriented data file format designed for efficient data storage and retrieval. It provides high performance compression and encoding schemes to handle complex data in bulk and is supported in many programming languages and analytics tools.

- [Apache Parquet Format @ GitHub](https://github.com/apache/parquet-format).

Related contents:

- [Building Your Modern Data Analytics Stack with Python, Parquet, and DuckDB @ KD nuggets](https://www.kdnuggets.com/building-your-modern-data-analytics-stack-with-python-parquet-and-duckdb).]]>
            </summary>
            <updated>2026-02-11T10:19:02+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11582</id>
            <title type="text"><![CDATA[Bound Book Format (.bbf)]]></title>
            <link rel="alternate" href="https://github.com/ef1500/libbbf" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11582"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A high-performance, DirectStorage-native container format for comics and manga. 

Bound Book Format (.bbf) is a high-performance binary container designed specifically for digital comic books and manga. Unlike CBR/CBZ, BBF is built for DirectStorage/mmap, easy integrity checks, and mixed-codec containerization.

Related contents:

- [I got into an argument on Discord about how inefficient CBR/CBZ is, so I wrote a new file format. It&amp;#039;s 100x faster than CBZ. @ r/selfhosted](https://old.reddit.com/r/selfhosted/comments/1qi64pr/i_got_into_an_argument_on_discord_about_how/).]]>
            </summary>
            <updated>2026-01-23T13:15:50+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11278</id>
            <title type="text"><![CDATA[TRON]]></title>
            <link rel="alternate" href="https://github.com/fastserial/lite3" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11278"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Tree Root Object Notation.

A JSON-Compatible Zero-Copy Serialization Format.]]>
            </summary>
            <updated>2025-12-22T13:17:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11104</id>
            <title type="text"><![CDATA[Apache TsFile]]></title>
            <link rel="alternate" href="https://tsfile.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11104"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[File Format for Internet of Things

TsFile is a columnar storage file format designed for time series data, which supports efficient compression, high throughput of read and write, and compatibility with various frameworks, such as Spark and Flink. It is easy to integrate TsFile into IoT big data processing frameworks.

- [Apache TsFile @ GitHub](https://github.com/apache/tsfile).]]>
            </summary>
            <updated>2025-11-26T12:41:09+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/7</id>
            <title type="text"><![CDATA[AGENTS.md]]></title>
            <link rel="alternate" href="https://agents.md/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A simple, open format for guiding coding agents,
used by over 20k open-source projects.

Think of AGENTS.md as a README for agents: a dedicated, predictable place to provide the context and instructions to help AI coding agents work on your project.

- [AGENTS.md @ GitHub](https://github.com/openai/agents.md).

Related contents:

- [Improve your AI code output with AGENTS.md (+ my best tips) @ builder.io](https://www.builder.io/blog/agents-md).
- [\#118 - Les news sur le développement web et l&amp;#039;IA pour septembre 2025 RC1 @ Double Slash :fr:](https://double-slash.dev/podcasts/sept25-rc1/).
- [Optimizing repos for AI @ Tom Bedor&amp;#039;s Blog](https://tombedor.dev/optimizing-repos-for-ai/).
- [How to write a great agents.md: Lessons from over 2,500 repositories @ GitHub Blog](https://github.blog/ai-and-ml/github-copilot/how-to-write-a-great-agents-md-lessons-from-over-2500-repositories/).
- [Writing a good CLAUDE.md @ humanlayer](https://www.humanlayer.dev/blog/writing-a-good-claude-md).
- [Streamlining my user-level CLAUDE.md @ chris dzombak](https://www.dzombak.com/blog/2025/12/streamlining-my-user-level-claude-md/).
- [The Complete Guide to CLAUDE.md @ builder.io](https://www.builder.io/blog/claude-md-guide).
- [How to build self-improving coding agents - Part 1 @ Eric J Ma&amp;#039;s Website](https://ericmjl.github.io/blog/2026/1/17/how-to-build-self-improving-coding-agents-part-1/).
- [AGENTS.md outperforms skills in our agent evals @ Vercel](https://vercel.com/blog/agents-md-outperforms-skills-in-our-agent-evals).
- [Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents? @ arXiv](https://arxiv.org/abs/2602.11988).
- [Knowledge Priming @ martinFowler.com](https://martinfowler.com/articles/reduce-friction-ai/knowledge-priming.html).
- [J&amp;#039;ai mis deux IA en code review l&amp;#039;une contre l&amp;#039;autre — voici ce que ça donne @ Maxence Maireaux :fr:](https://maxence.maireaux.fr/fr/blog/mon-workflow-ia-claude-code-codex/).
- [Filesystems are having a moment @ madalitso.me](https://madalitso.me/notes/why-everyone-is-talking-about-filesystems/).
- [Your Docs Directory Is Doomed @ Jim Yagmin&amp;#039;s Blog](https://yagmin.com/blog/your-docs-directory-is-doomed/).
- [Stop Wasting Hours Writing Unit Tests: Use GitHub Copilot to Explode Code Coverage Fast @ Build5Nines](https://build5nines.com/stop-wasting-hours-writing-unit-tests-use-github-copilot-to-explode-code-coverage-fast/).]]>
            </summary>
            <updated>2026-06-04T07:03:14+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/661</id>
            <title type="text"><![CDATA[DuckLake]]></title>
            <link rel="alternate" href="https://ducklake.select/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/661"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[DuckLake is an integrated data lake and catalog format

DuckLake delivers advanced data lake features without traditional lakehouse complexity by using Parquet files and your SQL database. It&amp;#039;s an open, standalone format from the DuckDB team.

DuckLake is an open Lakehouse format that is built on SQL and Parquet. DuckLake stores metadata in a catalog database, and stores data in Parquet files. The DuckLake extension allows DuckDB to directly read and write data from DuckLake.

- [Ducklake @ GitHub](https://github.com/duckdb/ducklake).]]>
            </summary>
            <updated>2025-08-28T17:48:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2586</id>
            <title type="text"><![CDATA[Apache Arrow]]></title>
            <link rel="alternate" href="https://arrow.apache.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2586"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics.

Apache Arrow defines a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead.

- [Apache Arrow @ GitHub](https://github.com/apache/arrow/).
- [arrow-rs @ GitHub](https://github.com/apache/arrow-rs).

Related contents:

- [Fast columnar JSON decoding with arrow-rs @ arroyo](https://www.arroyo.dev/blog/fast-arrow-json-decoding).
- [I spent 6 hours learning Apache Arrow: Overview @ Data Engineer Things&amp;#039;s Medium](https://blog.det.life/i-spent-6-hours-learning-apache-arrow-overview-e7f3b8ee85b2).]]>
            </summary>
            <updated>2025-08-28T23:07:06+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/7694</id>
            <title type="text"><![CDATA[FLIF - Free Lossless Image Format]]></title>
            <link rel="alternate" href="http://flif.info/index.html" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7694"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[FLIF is a novel lossless image format which outperforms PNG, lossless WebP, lossless BPG, lossless JPEG2000, and lossless JPEG XR in terms of compression ratio.]]>
            </summary>
            <updated>2025-08-29T13:19:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/7928</id>
            <title type="text"><![CDATA[FLIF - Free Lossless Image Format]]></title>
            <link rel="alternate" href="http://flif.info/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/7928"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[FLIF is a novel lossless image format which outperforms PNG, lossless WebP, lossless BPG and lossless JPEG2000 in terms of compression ratio.]]>
            </summary>
            <updated>2025-08-29T13:59:40+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/9953</id>
            <title type="text"><![CDATA[JSON]]></title>
            <link rel="alternate" href="http://json.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/9953"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[]]>
            </summary>
            <updated>2025-08-29T19:36:41+00:00</updated>
        </entry>
    </feed>
