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Onsites.fyi - Curated Big Tech Interview Experiences

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Onsites.fyi - Curated Big Tech Interview Experiences

Hi HN! While Glassdoor and other employment discussion boards offer valuable interview experience data, navigating it to prepare for interviews can be difficult. Onsites.fyi curates interview experiences and insights from Big Tech hiring across various positions and levels. Our collection currently includes interview experiences from top-tier companies like Apple, Google, Meta, Microsoft, and Amazon. Reviewing real interview experiences (rounds, questions, format) of others could be an invaluable preparation tool for interviews. Try it out and please share any feedback!

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Actual performance

187points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
86%86% 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: apple, google · Missing: mac, agents, macos
53%53% 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
38%38% 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: google, para · Missing: mobile apps, ios, personal
35%35% 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
25%25% 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
12%12% 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.

Incorrect prediction on native model

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