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I build an AI-Powered app for practicing interviews

Hacker News

I build an AI-Powered app for practicing interviews

While practicing for interviews, my first approach was to stand in front of a mirror, quizzing myself with questions and trying to give my best answers. When I answer some of my questions, I think I gave a better answer than before but when I try to recall it, I cannot answer how I did it earlier. So, I built Peptalk: Practice Interviews app to actively prepare for interviews which will help in recording answers to the interview questions. 1. Practice interview recording - Practice and capture recordings in the background and evaluate yourself. 2. AI-Generated Content: - Generate interview questions based on user-preferred jobs and descriptions and also answer evaluations by AI 3. Share / Send for Review: - Share the recordings for more constructive feedback.

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Actual performance

2points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% 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: user, recordings · Missing: mac, agents, macos
71%71% 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: lua, io · Missing: https docs, excited, just released
47%47% 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 · Strong signals: answers · Missing: mobile apps, ios, personal
45%45% 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
35%35% predicted probability of success on AppSumo, 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 · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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