We built a multimodal AI interviewer for mock system design interviews

Hacker News

Hey HN! We’re Jared, Shreyas, and Varun the creators of TechInterviewer. We’re building a product for software engineers to go through an entirely simulated systems design interview. Our AI interviewer, Steve, gives you a prompt and you talk out loud and draw on a whiteboard while Steve guides you through the interview and gives real-time feedback. Check out our demo: https://app.techinterviewer.ai Every software engineer today has to prepare for systems design interviews and have two awful options: pay hundreds of dollars for a single session with a FAANG engineer or follow silently alongside a YouTube playlist. Because there is no instant feedback while practicing, engineers often learn about their most important knowledge gaps during the course of the interview loop. Jared and Shreyas are both senior engineers who have spent 1000s of hours preparing for and administering systems design interviews. Shreyas was an early engineer at Deepgram and spent many years tracking developments in the TTS (text to speech) space. He realized that voice interviews had potential to change the candidate experience when he starting using chatGPT to prepare for interviewing founding engineer candidates at his startup. We’re hoping that having easy access to interview feedback will level the playing field of software engineers at different skill levels. We’re really excited to share this with you all and we’d love any thoughts, feedback, and comments

Visit product View on Hacker NewsJan 19, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: chatgpt, single, using · Missing: mac, agents, macos

84%84% 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

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist

55%55% 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

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

24%24% 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

20%20% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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