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After 37 failed interviews, I built the prep tool I wish I had

Hacker News

After 37 failed interviews, I built the prep tool I wish I had

Hey HN, I’m Ilyas. For 18 months, I was stuck in a loop: apply, interview, reject. Repeat. I sent over 1,000 applications and failed 37 interviews. The frustrating part? I wasn't failing complex algorithm challenges. I was failing basic questions like “What are React portals?” or “Explain GET in HTTP”. I knew the answers, but under pressure, my mind went blank. I realized my problem wasn't understanding—it was remembering. At first I used ChatGPT to help me create flashcards. But with time storage and tracking complexity grew. Plus sometimes AI was wrong. That's why I've built the tool to scratch my own itch. It’s a flashcard-based system designed to move web dev knowledge from "I kinda know this" to "I can explain this instantly under pressure". How it works (The Technical Bit): Instead of passive reading, the system uses spaced repetition and active recall to fight the Ebbinghaus forgetting curve. It covers 24 categories (React, Node, SQL, etc.) with 4,900+ questions. The goal is to build strong neural pathways through testing rather than just re-watching tutorials. The Outcome: After using this system for a few weeks, I stopped panicking in interviews. I eventually scored 95% on a technical test and landed a dream offer with paid relocation. Why I’m sharing it: I felt sick from the job search process and I know many others do too. I wanted to build something practical that helps to increase chances of passing dev interview. It isn't a "magic button," but a practical system for people who are tired of failing because they "kinda" know the material. It can help you, or at least helped me to get a dream job with paid relocation. I’d love to get your feedback on what helped to do a breakthrough on successfully passing dev interviews?! p.s. just in case you would like to take a look at the tool, URL is below: https://99cards.dev/ Thanks, Ilyas

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, chatgpt, using · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, answers, way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, paid · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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