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I built an AI to mock technical interview me after I shut down my YC co

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I built an AI to mock technical interview me after I shut down my YC co

Hey HN, I’m one of two people working on Hyperscreen AI, an AI that does mock technical interviews with you so you can pass more tech screens. I shut my YC startup down a year ago and had to look for a job afterward. Even though I had interned at and landed multiple FAANG offers out of college, I spent almost 6 months looking for a job after my company failed. I was fortunate enough to get technical screens due to my network, but I failed many of them because I didn’t have anybody to practice with. All of my friends either already had jobs and weren’t looking, or were still working on their startups. It was incredibly lonely to grind leetcode in my parents’ house for 8+ hours a day, 6 days a week, for months. I eventually got fed up with not having anyone to practice interviewing with, so I built a hacky voice bot on top of the latest LLMs to practice with. I was finally able to land a SWE job at a great late-stage startup. If someone with my background struggled this much to land a job, I can’t imagine how difficult it must be for many of the people who aren’t as privileged and have fewer interview opportunities to begin with. I went to a Tier 3 college when the market was still good, and, in my best year, I was only able to get phone screens with 4 companies. With so few chances, you need to be perfect and every edge you can get while prepping helps. My friend and I went to a hackathon a few months ago to build a more fleshed-out version of the tool I built for myself. After a few rounds of iteration with initial customers, it’s finally ready. Now for what you’ve all been waiting for, the juicy technical details. We’re using a simple React front end and Flask backend, with a series of model vendors provided to us through Vapi to handle the model audio interaction (OpenAI TTS, Deepgram STT). We’re using OpenAI for the feedback. The model gets access to your code via us sending it any time you speak. Others are building similar products, but none of them have gained substantial brand awareness, and none were (in my opinion) as meticulously designed or thought through as ours is. We’ve gotten feedback that this is just another AI wrapper, but we don’t think that’s true. You can’t effectively run a technical interview for free using just ChatGPT — we’ve tried it. It gives away answers, overexplains, and struggles to guide you. Not to mention the poorer UX, since you can’t easily sync your code while speaking to it. The way to get around this would be building a similar product to us, but then you’d have to pay for the audio streaming service yourself (either OpenAI real time, which is still in beta and very expensive, or something like Vapi which you’d still have to pay for). In other words, you need to pay if you want to run a mock technical interview. We’re still experimenting with our business model (as most new businesses tend to), but for now, we make money by charging a subscription for a fixed amount of time that our customers get each month. Currently, that’s $9.75 for an hour of interview time per month. My co-founder’s mom has paid upwards of $150 for 45-minute interviews of varying quality. Meanwhile, we currently charge more than 10x less for the same amount of time with a more consistent experience and detailed feedback. You can find them cheaper than this, but we have yet to see a service where you can interview with a human for cheaper than $9.75/hour. If you have friends who can interview you for free, that’s great! To be honest, writing this post is pretty surreal. I never did a Show HN for my last startup and held myself back from even working on this project or with a cofounder for a long time because I was so traumatized by my last experience. In retrospect, I’m glad I did it, and I’m thrilled to share what we’ve built so far. We’re really excited to hear all of your feedback, whether or not you decide to try it!

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Actual performance

5points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
99%99% 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: model, new, chatgpt · Missing: mac, agents, macos
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
64%64% 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, answers, way · 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
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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 · Strong signals: chat, make money, real time · Missing: web3, crypto, cryptocurrency
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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