Experiment Loops

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

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

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

Find Reddit threads where people are genuinely asking for what you offer, then draft short, genuinely helpful replies — disclosing your affiliation honestly and naming your product only when it truly answers the question. Two moves: discovery — scan the right subreddits for real needs (recommendation asks, expressed pain, competitor mentions) and rank the few threads where you can actually help; drafting — write from real experience, respect each community's self-promo rules, and keep a human in the loop to review and post. This markets on Reddit the honest way: contribute value, disclose who you are, never astroturf. Distilled from the playbook behind doany.ai's Reddit agent. Triggers on "find reddit opportunities", "reddit marketing", "reddit automation", "reply on reddit for my product", "reddit community engagement", "reddit lead gen", or any ask to turn Reddit threads into genuine, disclosed marketing replies.

ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.

The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.

The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.