Mocked – AI-powered mock interview tool for PMs

Hacker News

Hi HN, I launched an AI tool a few months back to help with product management interviews. It's Leetcode-style prep, but you can verbally respond to questions and receive feedback. I originally started it as a side project, but it blew up on Linkedin and we're nearing 5-figure ARR. I would be grateful for some honest feedback and advice on both the growth and engineering side. I'm particularly interested in: - What's one feature you feel is glaringly missing? - How did similar educational consumer apps (7sage, Leetcode) scale beyond just ads? - Should I implement a landing page? I wanted to give users the least friction when signing up, but every other consumer app has one. Any other thoughts would be appreciated, thanks!

Visit product View on Hacker NewsSep 28, 2025

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

80%80% 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: apps, user, code · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, month, users · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr, growth · Missing: mrr, revenue, profit

30%30% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

26%26% 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 · Strong signals: users · Missing: plus, platform, intuitive

25%25% predicted probability of success on AppSumo, 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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