GPT-4V audit for your landing page

Hacker News

I got seriously fascinated by the new GPT-4 Vision API introduced on OpenAI DevDay, so I decided to build a UX Audit tool with it. It's pretty simple to use - just go to https://uxaudit.vercel.app and enter your homepage or landing page URL. I'm using urlbox to get a screenshot of your page, then I utilize GPT-4V to analyze the screenshot and find potential usability and conversion issues. It also suggests solutions and highlights the relevant problem areas in the screenshot (although the position is sometimes inaccurate). Please let me know your thoughts and suggestions on how to improve it.

Visit product View on Hacker NewsNov 9, 2023

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

71%71% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, 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

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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