AI engineer at Tigerless. I build tools, agents, and experiments across the AI development stack. Most of it starts as something I needed for my own sessions, then gets cleaned up and shipped in the open.
Based in New York. Creator of Lara.
The ones people use most. I build these under Tigerless Labs.
🧠 autoharness (2.9k stars) - An agent that gets better by working, not by being rebuilt. A self-learning skill layer for Claude Code: distills skills from your real sessions, updates them as you work, and prunes the ones that stop getting used. No daemon, no benchmark
💸 cost-xray (1.3k stars) - See what Claude Code and Codex actually send to the API — and what each part costs. Usage logs tell you what a turn cost; Cost X-ray tells you why
📚 agent-memory (612 stars) - Long-term memory that gives any agent a retrieval engine's ranking over a filesystem it can just read
Skills I build to put the agent to work on everything around the code — design, research, distribution.
📣 auto-gtm (343 stars) - Drafts your X and Reddit posts from your merged PRs and the day's hot threads. Drafts only — it never posts for you
🎛️ design-harness (217 stars) - Feed your agent papers and half-formed ideas; it links them into a system design you can defend. Markdown keeps the record, a visual canvas makes it readable
🔎 influencer-discovery (193 stars) - Finds creators who bring their own audience across 15 channels, and appends their public contact info to a Google Sheet
📡 paper-radar (190 stars) - Finds the AI papers 28 labs put on arXiv over any date range, and separates the ones a company led from the ones it only appears on. No ML, no state, stdlib only
🚀 seo-ops (78 stars) - Give it a URL, get a crawler's-eye pass/fail report on the site's SEO foundation — 26 structural checks, zero LLM, deterministic
More in the works — built in the open, shipped fast, all under @tigerless-labs.