{"repo":"JoaquinMulet/Artificial-General-Research","free":true,"listed":false,"github":"https://github.com/JoaquinMulet/Artificial-General-Research","clone":"git clone https://github.com/JoaquinMulet/Artificial-General-Research.git","description":"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.","language":"Shell","stars":28,"topics":["agentskills","artificial-general-intelligence","autoresearch","claude-code"],"license":"MIT","category":"mcp-servers","readme_excerpt":"AGR: Artificial General Research Autonomous code optimization that works while you sleep. 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. Real result: Spatialize C++/Python library — 53.54s → 1.15s (45x speedup) , 18 autonomous experiments, all checksums verified. The only autoresearch framework with built-in measurement integrity — variance-aware acceptance, artifact detection, and exhausted approaches tracking. --- Quick Start That's it. AGR generates all needed files ( benchmark.py , STRATEGY.md , program.md , etc.), establishes a baseline, and starts experimenting autonomously. Want it containerized? The repo ships a Docker stack — agloop (iteration loop + agent), agwatch (heartbeat watchdog + auto-cleanup), agdash (:8080 dashboard) — with one-contract metric parsing ( key: float ), same-window references, quiet windows, dry-run validation and an examples/synthetic-campaign/ no-LLM quickstart. See docker/ . --- Why AGR? AGR is a Claude Code skill that turns any measurable optimization problem into an autonomous research loop. It builds on Karpathy's autoresearch, Goenka's Guard/Metric separation, and Bria's Ralph Loop — and adds 9 new ideas discovered through real-world experimentation: What AGR Adds Why It Matters --- --- Fresh context per iteration Iteration 100 reasons as well as iteration 1 — no context degradation Per-benchmark variance analysis Noisy benchmarks don't mask real ","default_branch":null,"files":null,"tree":[],"storefront":"/r/JoaquinMulet","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/JoaquinMulet/Artificial-General-Research/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}