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GPT-4-Vision UX audit for your landing page (relaunch)

Hacker News

GPT-4-Vision UX audit for your landing page (relaunch)

Last week, 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 this API. It's pretty simple to use - just go to https://flawless.is and enter your homepage or landing page URL. I'm using the urlbox API to get a screenshot of your page, then I utilize GPT-4V to analyze the screenshot, find potential usability and conversion issues, suggest fixes, and highlight the relevant areas in the screenshot (although the position is sometimes inaccurate). Last week, I launched it on Hacker News ( https://news.ycombinator.com/item?id=38207892 ), but I wasn't ready for the 20,000 visitors that came next... Unfortunately, GPT-4V has a rate limit of 100 events daily, so almost no one could use the service - EPIC FAIL. To manage demand, I now priced the service at $1.99 (still less than a cup of coffee), which should keep the demand at bay, and also cover the costs of GPT-4V, the screenshot API, and hosting. As a proof of concept, there are some demo audits on the homepage you can click and check out. Please let me know your thoughts and suggestions on how to improve it!

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, 000 · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, openai, using · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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