Al

Algochurn - Ace your next Technical Interview Round

Hacker News

Algochurn - Ace your next Technical Interview Round

Hey HN! I built Algochurn - A place for front-end developers to practice most commonly asked front-end interview questions. Of all the available resources out there, I wanted to have a place where I could practice coding questions myself (and to mimic a coding interview setup) and solve the problems with a time based approach. After 2 months of questions curation, I've come up with the first set of questions that are being asked in Big product based companies that assess candidates on their React skills. Use Algochurn to speed up your interview preparation. Save and Bookmark questions, Mark question as completed and use a time bound approach to solve problems. Link: https://algochurn.com To all the developers out there looking for jobs, I wish you all the best. Let Algochurn help you in landing that dream job. :)

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
80%80% 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: coding · 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: 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 · Strong signals: month, para · Missing: mobile apps, ios, personal
38%38% 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
27%27% 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
11%11% 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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