#Autoresearch
Showing 13 of 13 repositories tagged #autoresearch, ranked by stars
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.
Codex Autoresearch Skill — A self-directed iterative system for Codex that continuously cycles through: modify, verify, retain or discard, and repeat indefinitely. Inspired by Karpathy’s autoresearch concept.
🦞+🔬 NanoResearch: The Autonomous AI Research Assistant
AIDE: AI-Driven Exploration in the Space of Code. The machine Learning engineering agent that automates AI R&D.
Curated list of AutoResearch use cases with optimization traces and open source implementations
One file. Your AI coding agent becomes a scientist. 30+ experiments while you sleep.
Loop until it's better — drop-in agentic loops (autoresearch, scientific writing, data analysis, code/SQL/prompt optimization, red-teaming) as open-standard Agent Skills. Verification-gated; native on Claude Code, portable across Codex, Cursor & other Skills hosts.
Alignment-research scaffold (autoresearch-style) for LLM guardrails: search over a single policy.md surface
Don't trust an autoresearch paper at face value. Reviewer-side integrity forensics (self-consistency + fabrication), deterministic verdict. 61 signals: 46 integrity hack-patterns (families A–H, verdict-bearing) + 13 zero-weight AI writing-style impressions (AIS) + 2 advisory. Not an opaque AI-text classifier. The dual of ARIS.
Production-Grade Autoresearch. Ideal for GPU kernels, ML model development, feature engineering, prompt engineering, and other optimizable code.
AutoResearch + PromptFoo = AutoPrompter. Run it with Neo AI Engineer
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.