Claude Autoresearch Skill — Autonomous goal-directed iteration for Claude Code. Inspired by Karpathy's autoresearch. Modify → Verify → Keep/Discard → Repeat forever.

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.

Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels.

a recursive self-improving harness designed to help your agents (and future iterations of those agents) succeed on any task

A curated list of autonomous improvement loops, research agents, and autoresearch-style systems inspired by Karpathy's autoresearch.

🦞+🔬: NanoResearch: The Autonomous AI Research Assistant

Fully Autonomous AI Research System with Self-Evolution, built natively on Claude Code

A self-improving loop for voice AI agents. Uses karpathy's autoresearch as foundation.

One file. Your AI coding agent becomes a scientist. Autonomous experimentation skill for Claude Code, Codex, or any other agent.

multi-agent evolution organization for autoresearch and more

Autoresearch with PhD-level workflows and modular agent skills. Built for the autonomous AI Scientist.

Autonomous code optimization that works while you sleep (Autoresearch with Claude Code). Define a metric, point it at your code, go to bed. Wake up to a faster, smaller, better system — with correctness verified at every step.

Can LLMs beat classical HPO? A benchmark comparing classical, LLM-based, and hybrid methods on Karpathy's autoresearch.

Apple Silicon dual-backend port of autoresearch (PyTorch MPS + MLX) with full Muon optimizer

Deterministic runtime for agent evaluation

Give AI coding agents (Claude Code, Cursor, Aider, Codex) a structured autonomous loop with guardrails — boundaries, 5 verification gates, 3-layer self-reflection, and autonomous remediation. pip install ouro-loop. Zero dependencies.

Autonomous AI skill improvement through iterative experimentation — inspired by Karpathy's autoresearch. An agent mutates skill instructions, evaluates against objective metrics, keeps improvements, reverts regressions. No human in the loop.

Autonomous robotics research with simulation feedback

Companion tools for Karpathy's autoresearch - smarter evaluation, guided steering, and multi-agent competitions for GPT pretraining