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Sotto – AI interview assistant for Mac

Hacker News

Sotto – AI interview assistant for Mac

Being a software engineer myself I understand how grueling and tiresome software engineering interviews can be. Sometimes I found myself botching technical interviews due to nerves, only to realize a few minutes after the zoom call that I knew the answer all along. Many factors can cause skilled engineers to freeze up during interviews - and let's be honest, most of the skills tested during interviews are irrelevant to the actual day to day job. I created a Mac desktop app at https://getsotto.ai/ . It's made for engineers who have studied leetcode patterns but are in need of hints to guide them in the right direction during interview time. If you're truly stuck, it will also offer a correct solution but it will not help engineers who have no data structures and algorithms experience. I welcome any feedback and please join the discord server!

Visit product View on Hacker NewsMay 27, 2026

Actual performance

1points

Did not reach leaderboard

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best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, code · Missing: agents, macos, agent

59%59% 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: ide, io · Missing: https docs, excited, just released

49%49% 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

39%39% 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

13%13% 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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