I was preparing for senior engineering interviews and realized every existing tool had a fatal flaw. I tried them all — Anki, ChatGPT, Claude, dedicated interview platforms — and none of them solved the actual problem: testing whether I could explain technical concepts the way an interviewer expects.
Anki is the gold standard for spaced repetition. The algorithm is proven. But it only works for factual recall — flip a card, check your answer against the back. For technical interviews, the question isn't "what is the CAP theorem?" It's "explain the tradeoffs you'd make between consistency and availability for a real-time chat system." Anki can't grade that. You end up self-grading, which means you pass yourself when you shouldn't and build false confidence.
ChatGPT can grade open-ended technical answers surprisingly well. Ask it to evaluate your explanation of database indexing and it'll give you useful feedback. The problem: it has no memory. Every conversation starts fresh. It doesn't know what you got wrong last week, can't schedule reviews, and can't track your progress across topics. You're doing the scheduling manually, which defeats the purpose.
I tried building a workflow with Claude Code — using it as a grading engine while managing spaced repetition state in files. It technically worked, but the overhead was brutal. Starting a session meant loading context, managing state files, dealing with token limits. A 20-minute review session had 10 minutes of setup friction. The tool is incredible for coding, but it's not designed to be a flashcard app.
LeetCode, AlgoExpert, and similar platforms focus on coding problems — which is important but only one dimension of technical interviews. For system design and concept questions, most platforms fall back on multiple choice or video lectures. Multiple choice is actively harmful for interview prep because it teaches recognition, not recall. In an interview, nobody gives you four options to pick from.
What I needed was simple: spaced repetition scheduling (Anki's strength) combined with AI grading of open-ended answers (ChatGPT's strength), with persistent memory of what I know and don't know. Plus follow-up questions that probe weak points, the way a real interviewer digs deeper when your answer is shallow.
Nothing combined all of these. So I built it.
You type your answer in your own words — no multiple choice, no hints. AI grades it on depth, accuracy, and completeness, then asks probing follow-up questions on the parts you got wrong or explained poorly. Spaced repetition schedules the next review based on how well you actually understood the material, not how well you recognized the right option from a list.
The result: you build the kind of deep, explainable knowledge that survives a 45-minute technical interview where the interviewer keeps asking "why?"
Want to see how it compares to using ChatGPT directly? Read AI Interview Coach vs ChatGPT for Interview Prep. Or learn why deep technical knowledge matters more in the age of AI.
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