CleverMock – An AI voice interviewer that interrupts you

Hacker News

Hi HN, I’ve been working on a project to solve a problem I had with mock interview platforms: they don’t feel real. The biggest factor is that they let you give perfect, rehearsed answers without interruption. Real interviews are dynamic conversations. I created CleverMock, an AI-powered voice interviewer for software engineers. The core differentiator is a feature that simulates a real interviewer's behavior by interrupting the user's answers to ask clarifying questions or redirect the conversation, based on what it's hearing. It uses a pipeline of speech-to-text, LLMs for conversational logic, and text-to-speech to create a fluid, real-time interview session. You can specify the company and role to get tailored system design and behavioral questions. After a 45-minute session, it generates a detailed scorecard on your performance. It’s currently in beta and free for early users. I’m looking for feedback on the core "interruption" mechanic and the quality of the questions/feedback. Link: https://www.clevermock.com

Visit product View on Hacker NewsFeb 27, 2026

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

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

75%75% 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: pipe, io · Missing: https docs, excited, just released

44%44% 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: answers, users · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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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.

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