Te

Technical interviews are hard, so we built a voice agent for practice

Hacker News

Technical interviews are hard, so we built a voice agent for practice

Hey HN! I’m Andrew, and I built Code Coach with my good friends, Akshay and Chetan. Code Coach is an AI-powered interview coach that you talk to. It plugs right into Leetcode and turns any problem into a mock interview – it grills you the same way a real interviewer would and provides in-depth feedback on where you can improve. I built Code Coach to solve some of my own problems when practicing for technical interviews. I knew I had to practice communicating my reasoning while coding, but found it intimidating to schedule mock interviews with friends. More often than not, I ended up talking to a wall to replicate a more ‘real’ interview environment! Code Coach allows me to simulate unlimited mock interviews and master more challenging problems with live guidance. Here’s the link - https://www.trycodecoach.com (try out our demo!). With latency, transcription, and speech synthesis improving in the Voice AI space, the timing is ripe for more personalized, live coaching products. Thanks for the support, excited to hear your feedback!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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: agent, new, coding · Missing: mac, agents, macos
70%70% 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: excited, ide, io · Missing: https docs, just released, exist
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
35%35% 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
22%22% 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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