We

We've built graphics comparison tool

Hacker News

We've built graphics comparison tool

Hi, I wanted to share with You our project ( https://comparere.io ). We have built a website for comparing graphics and designs. The concept is simple, you have to just drag'n drop two designs you are having trouble to pick from. The idea has emarged when we stumbled across many different facebook groups for graphic designers, where they used to sometimes ask the community for help to decide which design is better. I had similar problem too. Hopefully this tool will help graphic/ux designers build/create projects that will appeal to more audiences. We are looking forward to read about your impressions.

Visit product View on Hacker NewsOct 29, 2019

Actual performance

23points

8comments

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

84%84% 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 · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, 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

56%56% 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 · Missing: plus, platform, intuitive

46%46% 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

33%33% 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

10%10% 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.

Correct prediction on native model

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