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Free AI tool to restore old, damaged photos – built solo

Hacker News

Free AI tool to restore old, damaged photos – built solo

Hey HN, I’m a solo developer and over the past few months I’ve built a simple web tool to restore old, faded, or damaged photos using AI. It: - Removes scratches, dust, and tears - Clarifies blurry details (faces, textures) - Optionally colorizes or applies vintage/artistic styles I originally built this for my own family albums — scanning old prints, restoring them in-browser with no Photoshop or plugins. I wanted anyone to be able to revive their memories with a click. There’s a free tier after signup, no hidden fees. I’d love feedback on: - Restoration quality (sharpness, artifacts) - Color/vintage styles (too muted? too saturated?) - Usability (site performance, interface) Thanks for checking it out — I’m happy to answer any questions or open to suggestions. – Fabio

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
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best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: using, open · Missing: mac, agents, macos
41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
26%26% 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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