UI Shots – Speed up your design research process

Hacker News

UI Shots help designers explore 1000+ pages from 100+ SaaS apps. This helps them getting inspiration for their next design. Websites are divided into categories like Finance, CRM, Email, Website Builder, AI, Automation and so on, with a total of 18 categories (more coming sooner) Same goes for pages, they are divided into page types e.g. Landing Page, Pricing Page and so on. Users can also filter by colors and can check the fonts used on a page. Further, they can bookmark their favorite designs to have all the inspiration at one place. Few days ago, I launched it on Product Hunt, 50 users have signed up already, with 1 premium subscription. I am looking for feedback to help improve my app. Thank You. Here is the link: https://www.getuishots.com/

Visit product View on Hacker NewsSep 28, 2023

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Actual performance

3points

4comments

Did not reach leaderboard

Launch Intel predictions

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

79%79% 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, email · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, users · 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, 000, io · Missing: https docs, excited, just released

37%37% 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 · Strong signals: saas, subscription · Missing: arr, mrr, revenue

19%19% 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

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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