Track and share your technical interview prep progress

Hacker News

A few friends and I just launched a web app to help people prepare for technical SWE/SDE internship interviews! We want to make it easier for people to prepare for interviews, especially for some of the largest tech companies. Core features include easy progress tracking, one-click link sharing, company-specific lists, and topic & difficulty-wise problem filtering. The problem list isn’t exhaustive and we are still looking for more open-source data to make our list more extensive. We want to hear about the bugs you find as this is the beta version. If you have any improvements to suggest, we are all ears! Our code is also completely open-source! Check out the repository here: https://github.com/labs-asterisk/interview-checklist Check out our ProductHunt launch here: https://www.producthunt.com/posts/interview-checklist

Visit product View on Hacker NewsJan 6, 2023

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

63%63% 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 HuntUnlikely to reach the leaderboard · Strong signals: code, open · Missing: mac, agents, macos

50%50% 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

44%44% 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

36%36% 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

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

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

19%19% 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.

Incorrect prediction on native model

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