Mi

Mini tool helped me to get a dream dev job (can help you too)

Hacker News

Mini tool helped me to get a dream dev job (can help you too)

Hi HN! I'm Ilyas - a web dev. In this short post I would like to share my mini tool on getting prepared for any dev interview: - HR - Tech - Behavioral - Live coding - Algo - etc. In the last five years through all kinds of interviews and summarized preparation in a bite size checklists to quickly go thru before the interview. This definitely adds at least extra 5% to confidence. Checklist is free to use. In general, "checklist" is proven tool that is used by pilots and doctors. This method helps you to make sure that all necessary steps are checked by you. Unloads your memory. If interested, check it out here: https://www.99cards.dev/checklists p.s. again, it's free. Enjoy! Ilyas

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, 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
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.
Product HuntUnlikely to reach the leaderboard · Strong signals: coding · Missing: mac, agents, macos
38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: 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
31%31% 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
16%16% 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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