AI-Mirror - Analytics engine for modern web applications.

Hacker News

I built AIMirror because I got tired of analytics tools showing me what users did, but never why they struggled. AIMirror is a lightweight UI/UX analysis toolkit for web apps that detects frustration, hesitation, dead clicks, rage clicks, and confusing user flows in real time — then explains what’s likely going wrong so you can actually fix the experience for the users instead of guessing. I am a creative technologist with a design degree majoring in digital design, with a heavy programming background I found combining the two exctremely complementary yet somewhat confusing for a lot of people who only either do the UX/UI side, or the back end, systems side. UIMirror helps people who want to help with that middle ground guess work that stops an app from being useful, or accessible.

Visit product View on Hacker NewsMay 22, 2026

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

85%85% 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, using · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, users · Missing: mobile apps, ios, personal

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

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

53%53% 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

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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto

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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