Facefork – A tree interface to AI edit photos for fun

Hacker News

Hello HN. I made this as a fun project with AI. No accounts, sign-ups, or any strings attached. The idea: Prompt your way into having AI modifying your photos in a tree-like interface. Every edit/prompt creates a leaf node. Share the whole tree or any single picture. Uses Openrouter as a singular way to access AI image models. Bring your own key. The whole project is served as a privacy-respecting static web app. No tracking. Your photos or API key never reach the web host or any third party other than Openrouter (obviously for the AI call). Code is here: https://github.com/aravindhsampath/facefork Ask your LLM to clone, review and run it locally if you’d rather not trust/use the hosted version. Fun PRs welcome.

Visit product View on Hacker NewsSep 17, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, single · Missing: mac, agents, macos

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

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, 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

25%25% 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

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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