Caffenol, a darkroom simulation photo editing toy

Hacker News

Caffenol is a darkroom toy, built on film development and darkroom craft. You load a photo and develop it into a negative in a simulated tank. Then you print that negative under a timed enlarger exposure. You shape the light by hand with cut cards that you wave over the paper. Under the hood, it's a WebGPU-powered film and paper sim, with chaos from each run producing a unique, organic result. It's built to encourage experimentation and there are few guardrails. I built this because darkroom printing made me fall in love with editing photography in a way Lightroom or Capture One couldn't. But it's expensive, uses a lot of resources, and isn't accessible. So, Caffenol brings the process and makes a few opinionated turns so it's fun and enjoyable. Ultimately, I hope it helps people make cool art. I'm tuning the film and paper sims with B&W processes now, with color coming shortly (read: when the color process can be made as fun as B&W is). No signups and everything is stored locally. If you find yourself wanting to learn more about how to bend and break it, the manual might help: https://caffenol.app/manual/ Hope it can make a print worth saving.

Visit product View on Hacker NewsSep 11, 2026

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

76%76% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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 · Strong signals: way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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.

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