Over the past two years, AI coding assistants completely transformed how fast software gets written. We can prompt an agent, watch 500 lines of complex boilerplate materialize in seconds, and declare the ticket finished.
But I noticed a deeply uncomfortable pattern: the faster the agent wrote, the less I genuinely understood what was living in my repository. I was shipping database migrations, reactive state machines, and concurrency pipelines that I couldn't explain from first principles. If an interviewer asked, "Why did you use this exact concurrency primitive here?", the honest answer was, "Because Claude Code put it there."
"AI should not be an autopilot that turns engineers into helpless consumers. It should be a tireless staff mentor that levels up your capability with every single commit."
I built idontknow to resolve this contradiction. I didn't want a tool that just generated toy tutorials or lectured me in the abstract. I wanted a skill that would do the real work in my real repo, but refuse to let me sign off on code I cannot defend.
With idontknow, whenever I venture into an unfamiliar corner of computer science—whether that's low-level CSS layout algorithms, Web Workers, OAuth security handshakes, or Kubernetes manifests—the agent builds the solution cleanly, but stops to systematically transfer the mental model into my head.
The result is that every completed pull request doesn't just advance the codebase: it permanently advances my skills as a software engineer.