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    <title>numpy</title>
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    <updated>2026-06-17T17:16:32+00:00</updated>
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            <entry>
            <id>https://links.biapy.com/links/11952</id>
            <title type="text"><![CDATA[numpy-ts]]></title>
            <link rel="alternate" href="https://numpyts.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11952"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Full NumPy, in TypeScript/JavaScript (94% coverage). 

The most comprehensive NumPy implementation for TypeScript and JavaScript. Write numerical computing code with the same API you already know from Python — fully type-safe, tree-shakeable, and validated against NumPy itself.

- [numpy-ts @ GitHub](https://github.com/dupontcyborg/numpy-ts).]]>
            </summary>
            <updated>2026-02-27T12:46:48+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10771</id>
            <title type="text"><![CDATA[JAX]]></title>
            <link rel="alternate" href="https://docs.jax.dev/en/latest/" />
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            <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 provides a familiar NumPy-style API for ease of adoption by researchers and engineers.

- [JAX @ GitHub](https://github.com/jax-ml/jax).

Related contents:

- [How the jax.jit() JIT compiler works in jax-js @ eric makes software](https://ekzhang.substack.com/p/how-the-jaxjit-jit-compiler-works).
- [192 Weeks @ Eric Zhang](https://notes.ekzhang.com/reflections/192-weeks).]]>
            </summary>
            <updated>2025-10-27T13:18:36+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/2450</id>
            <title type="text"><![CDATA[picklescan]]></title>
            <link rel="alternate" href="https://github.com/mmaitre314/picklescan" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2450"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Security scanner detecting Python Pickle files performing suspicious actions]]>
            </summary>
            <updated>2025-08-28T22:45:03+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2612</id>
            <title type="text"><![CDATA[KlongPy]]></title>
            <link rel="alternate" href="https://github.com/briangu/klongpy" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2612"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[High-Performance Klong array language in Python.

KlongPy is a Python adaptation of the Klong array language, known for its high-performance vectorized operations that leverage the power of NumPy. Embracing a &amp;quot;batteries included&amp;quot; philosophy, KlongPy combines built-in modules with Python&amp;#039;s expansive ecosystem, facilitating rapid application development with Klong&amp;#039;s succinct syntax.]]>
            </summary>
            <updated>2025-08-28T23:13:07+00:00</updated>
        </entry>
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