InterviewTrackr – All-in-one command center for CS job hunts

Hacker News

Hi HN, I’m building InterviewTrackr, a specialized tool for CS students to manage the technical recruiting process. Recruiting is a mess of spreadsheets, resume variants, and LeetCode logs. I built this to consolidate everything: Application Kanban: Track from wishlist to offer. Resume Manager: Match specific resume versions to each application. Interview & DSA Log: Categorize technical questions and behavioral stories. Offer Comparison: Side-by-side compensation breakdown (base, equity, etc.). Launching very soon. I’d love for you to join the waitlist for early access.

Visit product View on Hacker NewsFeb 28, 2026

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos

25%25% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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