Hello everyone - this community is in need of a few new mods, and you can use the comments on this post to let us know why you’d like to be a mod here.

Priority is given to redditors who have past activity in this community or other communities with related topics. It’s okay if you don’t have previous mod experience. Our goal, when possible, is to add a group of moderators so you can work together to build the community.

Please use at least 3 sentences to explain why you’d like to be a mod and share what moderation experience you have (if any).

If you are interested in learning more about being a moderator on Reddit, please visit redditforcommunity.com. This guide to joining a mod team is a helpful resource.

Comments from those making repeated asks to adopt communities or that are off topic will be removed.

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/

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