AI agents frequently suggest outdated code or dependencies because their training data is stale, leading to repeated manual corrections by developers.
CQ is a proposed system that allows AI agents to share "knowledge units" to avoid repeating common mistakes and stay up-to-date.
A practical demonstration shows an AI agent initially using an old GitHub Action version, which is then corrected and saved as a knowledge unit by CQ for future use.
CQ operates with a local SQLite database for individual use and can connect to a team server for shared knowledge, with a future vision for a public commons.
A key challenge for CQ is implementing guardrails and human-in-the-loop verification to prevent malicious or incorrect information from poisoning the shared knowledge.
The project has received positive initial feedback, and potential future applications include improving performance, security, and reducing dependency bloat.
Mozilla AI aims to democratize AI and keep it open, balancing innovation with community concerns about integrating AI into products like Firefox.
Developers are encouraged to try CQ locally and provide feedback, while also maintaining a balanced and critical perspective on when and how to use AI development tools.
Jobs that call for the skills explored in this talk.