Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels.
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Updated
Mar 13, 2026 - Python
Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels.
The first distributed AGI system. Thousands of autonomous AI agents collaboratively train models, share experiments via P2P gossip, and push breakthroughs here. Fully peer-to-peer. Join from your browser or CLI.
Claude Autoresearch Skill — Autonomous goal-directed iteration for Claude Code. Inspired by Karpathy's autoresearch. Modify → Verify → Keep/Discard → Repeat forever.
Autoresearch with PhD-level workflows and modular agent skills. Built for the autonomous AI Scientist.
Deterministic runtime for agent evaluation
Autonomous search engine experimentation on WSJ/TREC, with changes accepted only when retrieval quality improves without serious performance regressions.
Apple Silicon dual-backend port of autoresearch (PyTorch MPS + MLX) with full Muon optimizer
Autonomous robotics research with simulation feedback
Collaborative knowledge sharing for autonomous LLM training agents. Fork of karpathy/autoresearch with experiment cards, multi-agent coordination, and multi-platform support (CUDA/MPS/CPU).
Domain-agnostic MCP server for autonomous experimentation with metric-driven keep/rollback decisions and reproducible experiment history.
Train transformers on Apple's Neural Engine. Autonomous hyperparameter search via Karpathy's autoresearch protocol. 43 experiments, 8 verified findings.
What if the AI agent guessed first? 251 experiments. The predicting agent adapted to a 5x compute shift on try one. The reflecting agent took 7. The searching agent never caught up.
Distributed AI research platform. Volunteer compute for autonomous ML experimentation. SETI@home meets autoresearch.
Autonomous experimentation framework for AI agents. Define a problem, launch an agent, walk away. It experiments while you sleep.
Local-first dashboard for monitoring your own autoresearch loop with a tiny Python server and static HTML UI.
Karpathy's autoresearch running on Mac Mini — no NVIDIA GPU required. Auto-detects CUDA, Apple Silicon (MPS), or CPU.
Give AI coding agents (Claude Code, Cursor, Aider, Codex) a structured autonomous loop with guardrails — boundaries, verification gates, self-healing, and autonomous remediation. Zero dependencies. Inspired by Karpathy's autoresearch.
Recursive research intelligence that gets smarter every time you use it. MCP server + CLI + REST API. Prediction-error learning, surprise detection, persistent brain.
A practical playbook for running evidence-driven autoresearch loops.
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