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Prepair – fun mock interview challenge for software engineers

Hacker News

Prepair – fun mock interview challenge for software engineers

Hi everyone, Amidst the bad job market and hiring freezes in tech, my friends and I developed Prepair, a gamified mock interview tool for software engineers inspired by arcades. Would appreciate your feedback! Here are the problems we aimed to tackle with this project: 1. Mock interviews with real professionals for software jobs can be very expensive, often costing $100-200 for 30-minute sessions. We tried using AI to make a much more affordable but quality alternative. 2. Based on our experiences, a lot of companies do NOT conduct leetcode-style DSA interviews, but most interview prep services are focused on this niche. Prepair will present varied style of questions that may be more practical for you. 3. Interviews are ultimately competitions, but it's challenging to gauge how you measure up quickly. Use Prepair's leaderboard to see how high you can score against potential candidates as well as your past self! TLDR: just think of it as a Duolingo-vibe mock interview tool for developers. Give it a try and share your thoughts :) Thanks.

Visit product View on Hacker NewsMay 29, 2024

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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best fitHighest predicted score across all platforms for this description.

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

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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 · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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