Memory Snapshots for CPython (Python Language Summit 2026)

Show HN: STE80 Humanizer – de-AI text by writing 80% of the way to ASD-STE100

STE80 Humanizer: agent skill that removes AI tells and rewrites text 80% of the way to ASD-STE100 (Simplified Technical English), the method Andrej Karpathy shared. - Dodaios/ste80-humanizer

Locating the source of a mysterious beep with Claude

Simon Willison used Claude Code to build Beep Locator, a system that triangulates the source of an intermittent beeping sound using multiple devices as listeners. The Python web server coordinates phones, iPads and laptops over WebSockets, using Fast Fourier Transform beep detection and a chirp to align recorded clips, and Willison names the 2007 BeepBeep acoustic ranging paper as the research Claude found. The tool located the beep to within about 40cm, which turned out to be an ancient carbon monoxide detector that wanted its batteries replaced. He started with Claude Code Cloud running Opus 5.5 on his iPhone, then continued on his laptop after Fable 5.2 was released, iterating live by pushing new JavaScript to listening devices so they did not need to be manually restarted.

Issue #147 - Reinforcement Learning Explained with a Practical Python Example

How agents learn from rewards, why exploration matters, and how to build your first Q-learning agent.

Wasmtime 48 Denies Sockets by Default: What Breaks and Fixes

Wasmtime 48 denies TCP/UDP sockets by default. See what breaks, how to restore it on the CLI, and how to opt in when embedding Wasmtime.

Richard Geldreich's Blog: Basis Universal v2.6 update

Basis Universal v2.6 will expose its real-time analytical encoders through new APIs and tooling, with a new command line tool called basisu_pack shipping in January 2027. These encoders don't require SIMD, yet many are faster than other older search-based SIMD encoder designs. The update also plans official Python wheels for several platforms, transcoding performance increases, and large compression ratio improvements for XUASTC. CLI tools ship as both native binaries and secure WASM WASI executables.

Distributed LLM Inference on Kubernetes with llm-d, Wasm, & TornadoVM

Edoardo Vacchi covers the evolving landscape of WebAssembly on the backend, running Java workloads on GPUs using TornadoVM, and optimizing distributed AI inference using llm-d and vLLM on Kubernetes.

The Web Is More Than Enough, by Paul Ford

Web browsers enable incredibly rich app development—and that’s radically easier than it used to be.

Build Your Own Reasoning Model: A Full Video Course

Khóa học video kéo dài 11 giờ tập trung vào việc xây dựng model reasoning với Python và PyTorch. Nội dung bao gồm các kỹ thuật advanced như evaluation, inference-time scaling, reinforcement learning và distillation. Qua khóa học, bạn sẽ học cách tối ưu hiệu suất của các reasoning model hiện đại. Các bài thực hành code chi tiết giúp hiểu sâu về quy trình training và fine-tuning. Giá trị lớn nhất nằm ở việc kết hợp lý thuyết với thực hành để xây dựng được hệ thống AI có khả năng reasoning mạnh mẽ.

Lập trình viên muốn nâng cấp kiến thức về mô hình học máy từ cơ bản đến ứng dụng thực tế—đặc biệt là trong scaling mô hình, reinforcement learning, và distillation—để áp dụng ngay trong dự án AI của mình với Python và PyTorch.