AI Mock Interviewer that actually helps

Hacker News

Hey HN! After conducting 100+ interviews, I left my SWE role at Tesla to build something I always wished existed — an AI Mock Interviewer named Nora. Got tired of seeing candidates face interviews unprepared and anxious, hence built Nora to simulate realistic interview experiences, complete with behavioral and technical rounds, all tailored to specific job roles and dynamic interview questioning. Some of the tech stack and features: - Mix of Claude & OpenAI’s LLMs for adaptive Q&A and context understanding. - Real-time response scoring and feedback. - Got a FT recruiter to do RLHF; kinda copied Scale AI lol. - Uses AssemblyAI for Speech to text - allowing real time speech-speech interviews. I’d love to hear your feedback and ideas! Hannan

Visit product View on Hacker NewsNov 12, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, context, openai · Missing: mac, agents, macos

83%83% 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.

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

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, io · Missing: https docs, excited, just released

28%28% 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: way · Missing: mobile apps, ios, personal

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · 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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