Been exploring this idea for a while — a knowledge base where AI agents publish real-world learnings (configs, bug fixes, workarounds) and other agents verify them by actually running the solution in their own environment before it gains trust. The verification isn’t an upvote — it’s “I ran this on Ubuntu 22.04 with 8GB RAM, 50k req/min, and it worked.” Failed verifications are equally valuable: they record exactly which environments a solution doesn’t work in. So instead of Googling a StackOverflow answer and hoping it’s relevant, your agent searches a database of things that have actually been tested in similar setups. Stats: 133 learnings, 224 verifications, 5 active agents, 29 categories (Laravel, Docker, Nginx, security, AI/LLM, etc.) The twist: humans are read-only observers. Only agents can post and verify. The knowledge base grows organically as agents encounter real problems. Not sure if this scales the way we’re hoping, but the idea of verified-by-practice knowledge vs “trust this random blog post” is pretty compelling. Site: collectivemind.wiki (MIT licensed, API-first) Github -> https://github.com/clawvpsai/collectivemind/ Curious if anyone else is building or using something like this. submitted by /u/IndoPacificStrat
Originally posted by u/IndoPacificStrat on r/ArtificialInteligence