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MetaHarness works by generating repo-specific agent harnesses. It wraps the model with task rules, tool policies, scoring, receipts, routing, verifiers, and promotion gates.

Every run creates evidence. Every failure becomes training signal for the harness. Every improvement has to survive tests, cost checks, and safety gates before it gets promoted.

It supports multiple agent hosts. Claude Code, OpenAI Codex, GitHub Copilot, OpenCode, OpenClaw, Hermes, pi.dev, RVM, and GitHub Actions are all execution surfaces.

More than 100k downloads in first two weeks.

NPM: https://www.npmjs.com/package/metaharness

Repo: https://github.com/ruvnet/metaharness

Explainer: https://metaharness-explainer.vercel.app/

CVE-bench is a rigorous benchmark for evaluating AI agents' ability to fix real security vulnerabilities in real open-source software.

It applies the SWE-bench methodology to security: apply-patch → security-test.

Each instance is a real CVE from public open-source repositories, with a conformance firewall that ensures the solver never sees the gold patch or gold security test.

Agents need memory that branches: a per-user personalization layer, a sandbox to test a risky ingest, a checkpoint before a tool call, a thousand parallel experiments off one shared base.

With a normal vector DB each of those is a full copy of the whole index. At 1M vectors that is 496 MB and 67 ms — every time. agenticow makes it 162 bytes and 0.47 ms, flat.

Demo: https://ruvnet.github.io/agenticow/
NPM: https://www.npmjs.com/package/agenticow

The Backstory
On March 12-13, 2026, Chipotle's customer support chatbot "Pepper" went mega-viral after users discovered it could solve LeetCode problems, write Python, reverse linked lists — the works. It's powered by IPsoft Amelia (not Claude, not GPT), and it's still live.

https://github.com/cyberpapiii/chipotlai-max