I build AI agent systems — autonomous agents with real guardrails, and tooling that makes Claude Code project-specific instead of templated.
I build agent systems with Claude Code daily — not just write about them. Self-taught, based in Israel, learning in public. Right now that's Verdoc, a documentation business run by an autonomous agent inside real guardrails, and claude-harness-bootstrap, a plugin that builds a project-specific Claude Code harness from a one-paragraph description.
Before that: Gymion, a production fitness PWA with real-time sync, offline support, and full multi-language RTL/LTR support — still live today. Learning by building, in public.
A Claude Code plugin that builds a complete, project-specific Claude Code harness — agents, guard hooks, and commands — from a one-paragraph project description. No Claude Code experience required. Agents and hooks are derived from the project's actual traits, not a fixed template: a UI gets a framework reviewer, persistence gets a data-layer guard, auth gets a security reviewer, a published library gets a semver reviewer. Every harness gets a security baseline: it protects its own files, treats outside text as data, not instructions, and turns on Claude Code's sandbox where available.
Tested two ways: evals run per model (9 of 12 on Haiku and Sonnet), and a 13-case attack suite, where its harnesses went from catching 2 cases to all 13.
A documentation business run entirely by an autonomous AI agent, governed by a constitution the agent can't edit itself. Four independent guardrail layers — immutable constitution, pre-commit hook, integrity checker, approval queue — deliberately red-teamed: four attacks attempted, four stopped. Payment runs on live agent-to-agent infrastructure (x402, on Base mainnet).
A production fitness-tracking PWA — real-time cloud sync, offline support, full RTL/LTR multi-language support, Supabase auth, an AI workout generator, and a USDA nutrition API integration. Built from scratch, live today.
A community-driven restaurant rating app built with React Native + Expo. Users find restaurants globally, rate them across 7 categories, tag dining companions, and compete on a real-time leaderboard. Restaurant search runs on OpenStreetMap — no API key required. Full English/Hebrew i18n.
Open to AI engineering roles and freelance work on agent systems. Also happy to just talk shop about agents — I read everything.