Completely agree, most failures I’ve seen come from poor context management and unclear data flow, not the model itself. State handling also becomes a major issue when workflows scale, especially with multiple tools and agents interacting. In my experience, debugging improves a lot once you treat it as a system design problem rather than just an AI model issue.

Hmm, I think AI tools are actually pretty helpful, but you still have to double-check everything — they’re not perfect 🙂

What looks like a simple “success response” usually hides a lot of work—validation, auth checks, data fetching, retries, and transformations—all happening before the API replies, and understanding that is key to debugging performance and reliability.

Spaced repetition for Leetcode is one of those obvious-in-hindsight ideas — I've watched candidates grind 400 problems and still blank on a tree traversal in week 2 because they never revisited the early ones. Curious about the scheduling: are you using straight SM-2 or did you tune the intervals around problem difficulty tiers (easy vs hard take different memory windows)?

Very clean work. I really enjoyed learning this .

Yeah, when I was freelancing a lot of time was spent understanding the existing code in the projects I was being asked to update.