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CipherAI – Technical interview prep platform with LLM to assist you

Hacker News

CipherAI – Technical interview prep platform with LLM to assist you

Hi HN, I'm working on a technical interview prep platform that uses AI to help you. As of now, the platform is missing a lot of features and only has a few problems, but I wanted some feedback on the idea. Currently, Cipher is basically just leetcode but with a chatbot to help you answer questions. However, I was wondering if I should shift in a different direction, which would be more helpful to have an AI assistant (i.e., AI mock interviewer), or if optimizing the current idea is best. Also, any suggestions on either a) features to add and b) feedback in general would be greatly appreciated! Thank you in advance!

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Actual performance

4points
1comments
Did not reach leaderboard

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Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
30%30% predicted probability of success on AppSumo, 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
20%20% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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