Simulated (AI Based) User Testing [Open Source]

Hacker News

Hi all, I've created a little tool that allows you to get feedback from a "simulated (AI-based) user" on your designs. You can select preset questions for the AI user to answer or input your own questions. I hope this tool can help you refine your designs before showcasing them to real users. I'd love to hear your thoughts and feedback! I'm considering adding personas for the AI to role-play and making several other enhancements. There's still lot’s to do, but I wanted to share this early version with you.

Visit product View on Hacker NewsJul 25, 2024

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

86%86% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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

14%14% predicted probability of success on Acquire.com, 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.

Incorrect prediction on native model

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