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I built a browser-based image comparison tool for subtle differences

Hacker News

I built a browser-based image comparison tool for subtle differences

Hi HN, I built this after getting frustrated trying to compare small differences in AI-generated images and game graphics settings. A lot of tools I tried either felt bloated or required uploading files somewhere. So I made a simple image comparison tool that runs entirely in the browser. Everything stays local — no uploads. It supports slider, split view, fade mode, pan & zoom, and a few other viewing options like grayscale and basic color frequency info to help spot subtle changes. The goal was to keep it lightweight, precise, and easy to use. Curious what you think. https://picdiff.dev/

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1points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
74%74% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% 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
33%33% 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
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.

Correct prediction on native model

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