Taste-Bench
Taste-Bench measures the taste of an LLM agent: its ability to choose the better direction at a real decision fork in a long-horizon task.
https://github.com/wbopan/tastebench
Taste-Bench measures the taste of an LLM agent: its ability to choose the better direction at a real decision fork in a long-horizon task.
https://github.com/wbopan/tastebench
GitHub
GitHub - wbopan/tastebench: Taste-Bench: measuring the long-horizon judgment of LLM agents at real decision forks
Taste-Bench: measuring the long-horizon judgment of LLM agents at real decision forks - wbopan/tastebench
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type_enforced
Fast runtime type enforcement for Python 3.11+ type annotations. Zero dependencies and uncompromising performance.
https://github.com/connor-makowski/type_enforced
Fast runtime type enforcement for Python 3.11+ type annotations. Zero dependencies and uncompromising performance.
https://github.com/connor-makowski/type_enforced
GitHub
GitHub - connor-makowski/type_enforced: Fast runtime type enforcement for Python 3.11+ type annotations. Zero dependencies and…
Fast runtime type enforcement for Python 3.11+ type annotations. Zero dependencies and uncompromising performance. - connor-makowski/type_enforced
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Lemma
The open-source workspace where humans and AI agents work as one team.
https://github.com/lemma-work/lemma-platform
The open-source workspace where humans and AI agents work as one team.
https://github.com/lemma-work/lemma-platform
GitHub
GitHub - lemma-work/lemma-platform: Multiplayer harness that shapes around your work. Agents, apps and workflows your team shares…
Multiplayer harness that shapes around your work. Agents, apps and workflows your team shares, used from Slack, Telegram, WhatsApp, Teams, Email, Web or your own AI agent. - lemma-work/lemma-platform
Making np.searchsorted up to 25× Faster in NumPy 2.5
The article speeds up binary search by batching independent searches with NumPy’s vectorized operations, then reformulates the algorithm to use only O(1) extra memory. After porting it to C++, the approach achieves up to a 25× speedup over NumPy 2.4’s implementation.
https://blog.scientific-python.org/numpy/searchsorted/
The article speeds up binary search by batching independent searches with NumPy’s vectorized operations, then reformulates the algorithm to use only O(1) extra memory. After porting it to C++, the approach achieves up to a 25× speedup over NumPy 2.4’s implementation.
https://blog.scientific-python.org/numpy/searchsorted/
blog.scientific-python.org
Making np.searchsorted up to 25× Faster in NumPy 2.5
How vectorizing independent binary searches and reducing per-query state can make np.searchsorted up to 25× faster.
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Building a Faster (Rust-Based) Python Driver for ScyllaDB
ScyllaDB is building a new Python driver on top of its Rust driver using PyO3, with most serialization, deserialization, request execution, and concurrency handled in Rust for better performance and maintainability. Benchmarks show especially large gains under concurrent workloads, while the project still needs production hardening, broader feature coverage, and a compatibility layer for...
https://www.scylladb.com/2026/09/28/building-a-faster-rust-based-python-driver/
ScyllaDB is building a new Python driver on top of its Rust driver using PyO3, with most serialization, deserialization, request execution, and concurrency handled in Rust for better performance and maintainability. Benchmarks show especially large gains under concurrent workloads, while the project still needs production hardening, broader feature coverage, and a compatibility layer for...
https://www.scylladb.com/2026/09/28/building-a-faster-rust-based-python-driver/
ScyllaDB
Building a Faster (Rust-Based) Python Driver for ScyllaDB
Binding Rust to Python with PyO3, zero-copy deserialization with yoke
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AI Development on Windows: from PyTorch and llama.cpp to Windows ML
Microsoft is expanding local AI development on Windows with experimental GGUF support in Windows ML via llama.cpp, new native inference APIs, and an OpenAI-compatible endpoint for running models locally. The broader stack also adds native PyTorch and Triton support on Windows Arm64, giving developers a path to train and optimize models with familiar tools and deploy them through Windows ...
https://devblogs.microsoft.com/foundry-on-windows/build-on-winml-oct-7-26/
Microsoft is expanding local AI development on Windows with experimental GGUF support in Windows ML via llama.cpp, new native inference APIs, and an OpenAI-compatible endpoint for running models locally. The broader stack also adds native PyTorch and Triton support on Windows Arm64, giving developers a path to train and optimize models with familiar tools and deploy them through Windows ...
https://devblogs.microsoft.com/foundry-on-windows/build-on-winml-oct-7-26/
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ldraw-nova
Agent tooling for generative LEGO models building, built with Astra and Opus 5.5, powered by Jev
https://github.com/anteloc/ldraw-nova
Agent tooling for generative LEGO models building, built with Astra and Opus 5.5, powered by Jev
https://github.com/anteloc/ldraw-nova
GitHub
GitHub - anteloc/ldraw-nova: Agent tooling for generative LEGO models building, built with Astra and Opus 5.5, powered by Jev
Agent tooling for generative LEGO models building, built with Astra and Opus 5.5, powered by Jev - anteloc/ldraw-nova
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Orchestrating Disposable Browser
Glazer replaced race-prone parallel startup with readiness signals between Python and Docker containers, allowing components to start concurrently without assuming that running means ready. That design cut cold starts from 8–10 seconds to about 4.5 seconds, and caching the Tor data directory brought startup down to a consistent 1.9–2.1 seconds.
https://blog.glazer.ee/posts/building-glazer-disposable-browser/
Glazer replaced race-prone parallel startup with readiness signals between Python and Docker containers, allowing components to start concurrently without assuming that running means ready. That design cut cold starts from 8–10 seconds to about 4.5 seconds, and caching the Tor data directory brought startup down to a consistent 1.9–2.1 seconds.
https://blog.glazer.ee/posts/building-glazer-disposable-browser/
Glazer Blog
Orchestrating Disposable Browser
Glazer Blog: Get latest insights and best practises on cybercrime investigations, due-diligence workflows, intelligence collection and building with Glazer Technologies. Glazer transforms data into mission-critical intelligence faster, safer, and at scale.
The things you own end up owning you
NumPy is being ported toward Python’s Limited API so it could eventually ship abi3 wheels that work across multiple Python versions, reducing the large per-version wheel matrix and easing support for new Python releases. The same work is also pushing NumPy toward subinterpreter support by replacing static types and global state with heap types and per-module state, though multiarray rema...
https://labs.quansight.org/blog/limited_api_numpy
NumPy is being ported toward Python’s Limited API so it could eventually ship abi3 wheels that work across multiple Python versions, reducing the large per-version wheel matrix and easing support for new Python releases. The same work is also pushing NumPy toward subinterpreter support by replacing static types and global state with heap types and per-module state, though multiarray rema...
https://labs.quansight.org/blog/limited_api_numpy
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Build & Train a GLM-5.3-Flash Model From Scratch with Python
This tutorial demonstrates how to build and train a compact 25-million parameter multimodal language model from scratch using modern techniques like mixture-of-experts and sparse attention. It further explores the AI researcher's role by guiding viewers through pre-training, reinforcement learning experiments, and the critical importance of designing effective data-driven environments.
https://www.youtube.com/watch?v=-gfgQfw2g_E
This tutorial demonstrates how to build and train a compact 25-million parameter multimodal language model from scratch using modern techniques like mixture-of-experts and sparse attention. It further explores the AI researcher's role by guiding viewers through pre-training, reinforcement learning experiments, and the critical importance of designing effective data-driven environments.
https://www.youtube.com/watch?v=-gfgQfw2g_E
YouTube
Build & Train a GLM-5.3-Flash Model From Scratch with Python
Learn how to design, build, and train an advanced multimodal language model from scratch using modern techniques like mixture-of-experts, sparse attention, and reinforcement learning. This hands-on guide walks you through the entire lifecycle from tokenization…
rGPU
Keep Python on your laptop. Run PyTorch operations and hold tensors on a remote GPU, including from a Mac with no CUDA installation.
https://github.com/ymcrcat/rgpu
Keep Python on your laptop. Run PyTorch operations and hold tensors on a remote GPU, including from a Mac with no CUDA installation.
https://github.com/ymcrcat/rgpu
GitHub
GitHub - ymcrcat/rgpu: Keep Python on your laptop. Run PyTorch operations and hold tensors on a remote GPU, including from a Mac…
Keep Python on your laptop. Run PyTorch operations and hold tensors on a remote GPU, including from a Mac with no CUDA installation. - ymcrcat/rgpu
Pyronaut
Pyronaut is a high-performance polyglot application platform that brings Micronaut features like HTTP routing, dependency injection, configuration, validation, and Java library access to Python via GraalPy.
https://github.com/micronaut-projects/pyronaut
Pyronaut is a high-performance polyglot application platform that brings Micronaut features like HTTP routing, dependency injection, configuration, validation, and Java library access to Python via GraalPy.
https://github.com/micronaut-projects/pyronaut
GitHub
GitHub - micronaut-projects/pyronaut: High Performance Python Application Platform
High Performance Python Application Platform. Contribute to micronaut-projects/pyronaut development by creating an account on GitHub.
Can Jev save my inbox from the Democrats?
The author outlines an end-to-end ML project evaluating whether TypeSafe’s Jev, accessed through OpenRouter, and smaller local models can effectively filter political fundraising emails from a personal inbox. He ultimately builds a 23.4 MB quantized ONNX SetFit model using mdbr-leaf-mt and confidence thresholding, creating a fast, CPU-friendly local classifier while exposing the remainin...
https://huggingface.co/blog/stephen-solka/use-jev-to-delete-fundraising-emails
The author outlines an end-to-end ML project evaluating whether TypeSafe’s Jev, accessed through OpenRouter, and smaller local models can effectively filter political fundraising emails from a personal inbox. He ultimately builds a 23.4 MB quantized ONNX SetFit model using mdbr-leaf-mt and confidence thresholding, creating a fast, CPU-friendly local classifier while exposing the remainin...
https://huggingface.co/blog/stephen-solka/use-jev-to-delete-fundraising-emails
huggingface.co
Can Jev save my inbox from the Democrats?
A Blog post by Stephen Solka on Hugging Face
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How we built an async-aware Python profiler
Learn how Datadog made Python profiling async-aware while reducing profiler overhead and preserving context across asyncio tasks.
https://www.datadoghq.com/blog/engineering/async-python-profiler/
Learn how Datadog made Python profiling async-aware while reducing profiler overhead and preserving context across asyncio tasks.
https://www.datadoghq.com/blog/engineering/async-python-profiler/
Datadog
How we built an async-aware Python profiler | Datadog
Learn how we made Python profiling async-aware while reducing profiler overhead and preserving context across asyncio tasks.
8 Python 3.15 Features You Need to Know
Python 3.15 introduces practical enhancements such as lazy imports, a built-in immutable dictionary, and streamlined unpacking in comprehensions. These updates, alongside a new sampling profiler and default UTF-8 encoding, aim to improve developer productivity and application performance.
https://www.youtube.com/watch?v=bgeqL4Btou0
Python 3.15 introduces practical enhancements such as lazy imports, a built-in immutable dictionary, and streamlined unpacking in comprehensions. These updates, alongside a new sampling profiler and default UTF-8 encoding, aim to improve developer productivity and application performance.
https://www.youtube.com/watch?v=bgeqL4Btou0
YouTube
Python 3.15 Is a Bigger Update Than I Expected
🧱 Build software that lasts. Join the Software Design Mastery program → https://arjan.codes/mastery.
Python 3.15 brings some surprisingly useful changes, from lazy imports and a built-in immutable dictionary to a standard sentinel and a new way to flatten…
Python 3.15 brings some surprisingly useful changes, from lazy imports and a built-in immutable dictionary to a standard sentinel and a new way to flatten…
Jev for Python engineers
Vercel’s Python AI SDK adds support for Jev, a fast universal classifier that answers structured choice, scoring, and probability questions without task-specific training. The post tests Jev on classifying Python vs. English and generating Python ASTs, showing that it works well for narrow decisions but still struggles when pushed into general code generation.
https://vercel.com/blog/jev-for-python-engineers
Vercel’s Python AI SDK adds support for Jev, a fast universal classifier that answers structured choice, scoring, and probability questions without task-specific training. The post tests Jev on classifying Python vs. English and generating Python ASTs, showing that it works well for narrow decisions but still struggles when pushed into general code generation.
https://vercel.com/blog/jev-for-python-engineers
Vercel
Jev for Python engineers
Explore Jev with Vercel's Python AI SDK. Learn how its fast, structured decisions work, try the experimental evaluate() API, and follow two experiments in text classification and Python code generation.
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openTPU
An open-source AI accelerator, developed by AI: RTL, ISA, simulator, compiler and profiler in one repo. Runs Qwen3, LFM2.5 and Qwen3.5 on a Kintex-7 PCIe card.
https://github.com/FeSens/openTPU
An open-source AI accelerator, developed by AI: RTL, ISA, simulator, compiler and profiler in one repo. Runs Qwen3, LFM2.5 and Qwen3.5 on a Kintex-7 PCIe card.
https://github.com/FeSens/openTPU
GitHub
GitHub - FeSens/openTPU: An open-source AI accelerator, developed by AI: RTL, ISA, simulator, compiler and profiler in one repo.…
An open-source AI accelerator, developed by AI: RTL, ISA, simulator, compiler and profiler in one repo. Runs Qwen3, LFM2.5 and Qwen3.5 on a Kintex-7 PCIe card. - FeSens/openTPU
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