#python
ChatDev 2.0 (DevAll) is a free, zero-code platform to build and run teams of AI agents for any task—like software coding, data charts, 3D models, games, or deep research—using simple drag-and-drop workflows, no programming needed. Install with Python/uv and Node.js, then launch via web console or Python SDK for quick results. This saves you hours of manual work, cuts costs, and lets you create complex projects fast and easily, even without tech skills.
https://github.com/OpenBMB/ChatDev
#python
AlpaSim is an open-source simulator that lets me test full autonomous driving systems in realistic, closed-loop virtual environments. It gives me high-quality camera and sensor data, realistic vehicle physics, and complex traffic scenarios, all configurable for my experiments. Because it is modular, Python-based, and built on microservices, I can easily plug in my own algorithms, scale across machines, and debug tricky behaviors. Built-in support for advanced driving policies, rich documentation, and sample datasets helps me quickly validate, compare, and improve my models while reducing the cost and risk of real-world testing.
https://github.com/NVlabs/alpasim
#python
This GitHub repo runs a 6-month experiment where ChatGPT manages a real $100 micro-cap stock portfolio daily, using trading data, strict 10% stop-losses, and weekly research to pick trades—outperforming the S&P 500 by 25% (+29% vs. +4%) in the first two months. It offers Python scripts, prompts, CSVs, performance charts, and a starter guide to run your own. You benefit by testing AI stock picking transparently with low risk, learning from logs to boost your trading skills or generate real returns.
https://github.com/LuckyOne7777/ChatGPT-Micro-Cap-Experiment
#python
Python 3.15.0 alpha 3 is an early test version you can build from source on Unix/Linux/macOS with simple steps: `./configure`, `make`, `make test`, `sudo make install`. Use `--enable-optimizations` for faster performance via PGO, run `make test` to check it works, and `make altinstall` for multiple versions side-by-side. Access docs at docs.python.org/3.15, source at github.com/python/cpython, and contribute via the dev guide. This helps you test new features early, optimize your code, and prepare projects ahead of the stable release for better speed and reliability.
https://github.com/python/cpython
#python
Polymarket Agents is a free, open-source Python framework to build AI agents that trade autonomously on Polymarket prediction markets. It integrates the Polymarket API, pulls data from news and bets, uses RAG for smart info retrieval, and offers LLM tools for strategies like spotting cheap YES/NO shares when their average cost dips below $1 for guaranteed profit. You benefit by automating emotion-free trades, capturing market inefficiencies instantly, and boosting returns without constant monitoring.
https://github.com/Polymarket/agents
#python
Bloom is a free, open-source tool that automates testing AI models for bad behaviors like bias or sycophancy. You define the behavior in a simple config file, add example chats if you want, and it runs four steps: understanding it, creating varied test scenarios, simulating talks with your target model (like Claude or GPT via APIs), and scoring results with metrics like how often the issue appears. View interactive transcripts easily. This saves you hours of manual work, lets you quickly compare models on fresh tests to avoid overfitting, and gives reliable, reproducible insights into AI safety—perfect for researchers building trustworthy systems.
https://github.com/safety-research/bloom
#python
This repository offers Anthropic's Claude Skills—folders with instructions, scripts, and resources that dynamically teach Claude specialized tasks like branded documents, data analysis, or workflows. Examples cover creative, technical, and enterprise uses; install via Claude Code, .ai, or API, or create your own with a simple SKILL.md template. You benefit by automating repetitive work, boosting productivity, ensuring consistent results, and capturing your team's knowledge for reliable, scalable AI performance.
https://github.com/anthropics/skills
#python
**Reachy Mini** is an open-source desktop robot, 11 inches tall and 3.3 lbs, with a 6-DoF expressive head, 360° body rotation, animated antennas, wide-angle camera, microphones, speaker, and Hugging Face AI integration for 1.7M+ models. Assemble in 2-3 hours as a kit; choose Lite (USB-powered) or Wireless (Raspberry Pi, battery). Use simple Python SDK for quick control, apps like conversation or hand-tracking, and simulation. **You benefit** by easily building, testing, and sharing AI robots at home or work, speeding up embodied AI experiments affordably.
https://github.com/pollen-robotics/reachy_mini
#python
Mini-SGLang is a compact, easy-to-read inference framework (~5,000 Python lines) that runs and serves large language models with high speed using optimizations like radix cache, chunked prefill, overlap scheduling, tensor parallelism, and FlashAttention/FlashInfer kernels. It’s CUDA-dependent, quick to install from source, and can launch an OpenAI-compatible API or interactive shell for single- or multi‑GPU serving, letting you test or deploy models (e.g., Qwen, Llama) with low latency and scalable throughput. Benefit: you get a transparent, modifiable engine to deploy fast, efficient LLM inference for development, benchmarking, or production use.
https://github.com/sgl-project/mini-sglang
#python
**ty** is a super-fast Python type checker and language server built in Rust by Astral (makers of uv and Ruff). It's 10-100x faster than mypy or Pyright, with rich error messages, IDE features like auto-complete and hover help, and support for big projects or partial typing. Try it via `uvx ty check`. This helps you catch bugs early, code faster with real-time feedback, and boost productivity in editors like VS Code.
https://github.com/astral-sh/ty
#python
You can access a free, detailed global dataset called the Global Building Atlas, which includes 2D building shapes, heights, and simple 3D models (LoD1) for 2.75 billion buildings worldwide, including areas often missing in other maps like Africa and South America. This data is very accurate, with a fine 3x3 meter resolution, and can be used in GIS software or downloaded fully. It helps with urban planning, disaster risk assessment, climate adaptation, and monitoring sustainable development goals by showing where people live and how cities grow. The dataset and related code are openly available for research and practical use.
https://github.com/zhu-xlab/GlobalBuildingAtlas
#python
cuTile Python is a new programming tool from NVIDIA that lets you write GPU programs in Python more easily and efficiently. It uses a tile-based model, where you work with chunks of data called tiles, making your code portable across different NVIDIA GPUs without needing to rewrite it for each hardware generation. cuTile automatically uses advanced GPU features like tensor cores and memory accelerators, so you get high performance without complex coding. You can install it via pip, and it requires CUDA Toolkit 13.1+ and Python 3.10+. This helps you develop faster, future-proof GPU applications with less effort.
https://github.com/NVIDIA/cutile-python