Linus wrote Git in about 10 days, so I asked my local LLM to build something similar with one promptRich Edmonds writes that he tasked a local LLM with building a Git-like version control system from scratch to test the capabilities of home-based models. Using a 27B parameter Qwen model on a modest 20 GB VRAM setup, the system generated a working Python script named tinyvcs that handles core repository functions like initialization, commits, and history inspection. While the model identified and fixed several bugs on its own, it eventually required manual intervention to correct schema validation and symlink issues, producing a functional but basic proof of concept that lacks features like branching and remote management.
- The hardware utilized for this project is being used for a non-intended purpose, as consumer-grade GPUs are generally designed for gaming and graphics rendering rather than the heavy lifting required for local model inference.
- The LLM was strictly constrained to the Python standard library and had no internet access or ability to call external APIs.
- The resulting tinyvcs tool is comparable to an early prototype of Git, featuring content-addressed objects and SHA-based IDs but missing standard features like tags, remotes, and merging.
- The hardware utilized for this project is being used for a non-intended purpose, as consumer-grade GPUs are generally designed for gaming and graphics rendering rather than the heavy lifting required for local model inference.
- The LLM was strictly constrained to the Python standard library and had no internet access or ability to call external APIs.
- The resulting tinyvcs tool is comparable to an early prototype of Git, featuring content-addressed objects and SHA-based IDs but missing standard features like tags, remotes, and merging.
autoharness is a self-learning skill layer for Claude Code that distills reusable skills from a user's real sessions, merges near-duplicates, updates them in use, and prunes those that stop getting used — all without a daemon or an external benchmark. It fires on tool-call count rather than turns, keeps only the skills it authored, and validates a skill's worth by adherence in later turns rather than a held-out score.
- Skills are stored as plain native SKILL.md files in `.claude/skills/`; the plugin's own recall index is injected on top of the host's native mechanism
- Three distinct lifecycle signals are tracked: load (model invoked the skill), view (session read into the skill's directory), and patch (promoter landed an improvement)
- The `/learn` command allows on-demand distillation of the current session through the same proposal-and-validation chain
- Skills are stored as plain native SKILL.md files in `.claude/skills/`; the plugin's own recall index is injected on top of the host's native mechanism
- Three distinct lifecycle signals are tracked: load (model invoked the skill), view (session read into the skill's directory), and patch (promoter landed an improvement)
- The `/learn` command allows on-demand distillation of the current session through the same proposal-and-validation chain
Shweta Sharma writes that Unsloth Studio, an AI-model-training tool in beta, contained a vulnerability where selecting a model could trigger arbitrary Python code execution on a user's machine. The issue stemmed from the application automatically enabling Hugging Face's `trust_remote_code` option during routine metadata checks, allowing specially crafted models to execute malicious code without downloading full weights or requiring inference.
- Pillar Security researcher Ariel Fogel discovered that reading only the `config.json` file was sufficient to trigger the exploit.
- A fix was released in version 2026.6.9 which prevents arbitrary model loading from Hugging Face and disables the automatic trust of remote code for local files.
- Pillar Security researcher Ariel Fogel discovered that reading only the `config.json` file was sufficient to trigger the exploit.
- A fix was released in version 2026.6.9 which prevents arbitrary model loading from Hugging Face and disables the automatic trust of remote code for local files.
This repository provides access to Apple's built-in large language models via Node.js and Python packages, requiring only macOS 26+ on Apple Silicon and the Xcode Command Line Tools. It offers two tiers of interaction: an on-device tier using a small sparse model (AFM 3 Core Advanced) that ensures privacy and guaranteed JSON output through constrained decoding, and a cloud tier via Apple's Private Cloud Compute which provides more powerful reasoning capabilities but sends prompts off the machine.
- The library includes both npm (`apple-llm`) and PyPI (`apple-llm`) packages.
- On-device models are noted to be poor at code generation and long reasoning tasks.
- Structured JSON output is guaranteed on-device via a specific "GenerationSchema" dialect used by Apple's decoder.
- The cloud tier uses Shortcuts as an intermediary because the direct Private Cloud Compute API requires restricted developer entitlements.
- Users can utilize built-in tools like OCR, barcode reading, and Spotlight semantic search for local RAG (macOS 27+).
- The library includes both npm (`apple-llm`) and PyPI (`apple-llm`) packages.
- On-device models are noted to be poor at code generation and long reasoning tasks.
- Structured JSON output is guaranteed on-device via a specific "GenerationSchema" dialect used by Apple's decoder.
- The cloud tier uses Shortcuts as an intermediary because the direct Private Cloud Compute API requires restricted developer entitlements.
- Users can utilize built-in tools like OCR, barcode reading, and Spotlight semantic search for local RAG (macOS 27+).
Ivan Maradzhiyski writes about innomd, a command-line tool designed for Linux and macOS that renders LaTeX math formulas as clean Unicode directly in the terminal. Built on top of the `rich` library, it serves scientists, engineers, and students by providing human-readable mathematical notation (such as Greek letters, operators, and fractions) instead of raw LaTeX source code. The tool also features support for rendering Mermaid and PlantUML diagrams into ASCII/Unicode representations and includes a live reload mode for real-time Markdown previewing.
- Renders common subsets of Mermaid and PlantUML (including flowchart, sequence, class, Gantt, C4 architecture, and activity diagrams).
- Supports Jupyter notebook (`.ipynb`) files by rendering cells as Markdown with syntax highlighting.
- Developed in pure Python using `rich` and `grandalf`, requiring no external binaries like Graphviz or Node.js for diagram layout.
- Features nine built-in color themes, including Nord, Dracula, and Solarized.
- Uses Unicode approximation rather than pixel-perfect rendering, making it ideal for terminal use but not as a substitute for PDF/LaTeX compilation.
- Renders common subsets of Mermaid and PlantUML (including flowchart, sequence, class, Gantt, C4 architecture, and activity diagrams).
- Supports Jupyter notebook (`.ipynb`) files by rendering cells as Markdown with syntax highlighting.
- Developed in pure Python using `rich` and `grandalf`, requiring no external binaries like Graphviz or Node.js for diagram layout.
- Features nine built-in color themes, including Nord, Dracula, and Solarized.
- Uses Unicode approximation rather than pixel-perfect rendering, making it ideal for terminal use but not as a substitute for PDF/LaTeX compilation.
Python 0.9.1 is the historical first public beta release of the Python programming language. It was uploaded and distributed by its creator, Guido van Rossum, to the Usenet newsgroup alt.sources on February 19, 1991
Strands Agents Tools is a community-driven Python package that hands LLM-based agents a ready-made set of capabilities—file operations, shell integration, web search, Python execution, persistent memory, and multi-agent coordination—so developers building on the Strands Agents SDK don't have to write each integration from scratch.
- Memory backends include Mem0, Amazon Bedrock Knowledge Bases, Elasticsearch, and MongoDB Atlas
- Multi-agent primitives (swarm intelligence, agent-as-tool with model switching, multi-agent graphs) live in the same package as basic file tools, reducing glue code
- Python execution requires user confirmation as a first-class safety measure
- Modular design: pull in only the tools you need without dragging in video processing, cron scheduling, or Slack
- Memory backends include Mem0, Amazon Bedrock Knowledge Bases, Elasticsearch, and MongoDB Atlas
- Multi-agent primitives (swarm intelligence, agent-as-tool with model switching, multi-agent graphs) live in the same package as basic file tools, reducing glue code
- Python execution requires user confirmation as a first-class safety measure
- Modular design: pull in only the tools you need without dragging in video processing, cron scheduling, or Slack
Becquerel is a Python package for analyzing nuclear spectroscopic measurements, offering tools to read/write spectrum files, fit spectral features, perform detector calibrations, and interpret results. It relies on numpy, scipy, matplotlib, and pandas for data analysis and visualization. The package is designed for both educational and research use, supporting a wide range of users from undergraduates to advanced researchers.
- Developed by Lawrence Berkeley National Laboratory
- Supports spectrum file formats like N42, CHN, and CSV
- Includes tools for radiation spectrum plotting and nuclear data access
- Developed by Lawrence Berkeley National Laboratory
- Supports spectrum file formats like N42, CHN, and CSV
- Includes tools for radiation spectrum plotting and nuclear data access
Tyler August writes that CircuitPython has been enhanced with precompiled functions to boost performance. This new feature, developed by Mikey Sklar with help from Anthropic's Claude LLM, allows users to compile critical sections of code for faster execution, up to 70x speed improvements. The update enables 'native' mode for a 3X speed boost and 'viper' mode for significantly higher performance, similar to MicroPython's Viper compiler. While CircuitPython still lacks inline assembly, it's closer to the capabilities of MicroPython now. The article also notes the versatility of Python in microcontroller projects, ranging from e-bikes to music players.
- Enhances performance by allowing precompiled code execution
- Developed by Mikey Sklar with assistance from Anthropic's Claude LLM
- 'Native' mode offers 3X speed boost, 'Viper' mode up to 70X
- Still lacks inline assembly, but closer to MicroPython capabilities
- Python's versatility in microcontroller projects highlighted
- Enhances performance by allowing precompiled code execution
- Developed by Mikey Sklar with assistance from Anthropic's Claude LLM
- 'Native' mode offers 3X speed boost, 'Viper' mode up to 70X
- Still lacks inline assembly, but closer to MicroPython capabilities
- Python's versatility in microcontroller projects highlighted
This repository provides an open-source face recognition software development kit (SDK) for Windows and Linux systems, developed by Faceplugin. It uaes deep learning models to offer on-premise processing of facial data, ensuring privacy as no information leaves the user's device. The toolkit supports various functions including face detection, landmark detection, feature embedding generation, and similarity comparison via Python APIs.
- Supports JPG, PNG, BMP, and TIFF image formats
- Compatible with both CPU and GPU acceleration
- Requires Python 3.9 or higher and Anaconda is recommended for setup
- Includes capabilities for bounding box extraction and facial landmark detection
- Supports JPG, PNG, BMP, and TIFF image formats
- Compatible with both CPU and GPU acceleration
- Requires Python 3.9 or higher and Anaconda is recommended for setup
- Includes capabilities for bounding box extraction and facial landmark detection
