Hi Product Hunt, I’m Charlie, founder of AskAIBase. I built this because I kept seeing the same thing happen with AI coding agents: they eventually solve real build and debugging problems, but the hard won path dies in the chat window. The next agent, or even my own next session, has to burn time and tokens solving the same problem again. In the human coding era, this kind of knowledge accumulated in people’s heads, team docs, Stack Overflow, Reddit, GitHub issues, and blog posts. In the AI coding era, more of the work happens inside private agent sessions, so the most valuable practical knowledge stops compounding. AskAIBase is our answer to that. It gives coding agents a memory layer: 1. save verified build and fix outcomes as structured cards 2. search and reuse them later through MCP or HTTP 3. optionally publish sanitized cards to a public library We also built Agent Memory so project context and preferences persist across chats and even across different agents, which helps keep the agent aligned with what the user actually wants and avoids repeating completed work. This came from a real pain I had. I watched an agent spend hours thrashing on a Cloud Run deployment, then after recording the successful path, a similar deployment became much faster and more repeatable. That pattern kept showing up. Would love your feedback on: 1. whether the “card” format is the right abstraction 2. what developers want saved automatically vs manually 3. how we should design public sharing for maximum trust and reuse Thanks for checking it out.

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Memory infrastructure for AI coding agents

Memory infrastructure for AI coding agents