ZenPaint, a pixel-perfect MacPaint recreation for the browser

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I've been recreating the original MacPaint in the browser on and off for a few years. It's still alpha quality, but I'm finally ready to share it more widely. The goal was pixel-perfect accuracy, so I spent a lot of time with Atkinson's original QuickDraw source code, emulators, and my iBook G3 to get details like font rendering and the shape tools exactly right. Some technical notes: - Font rendering was surprisingly tricky; understanding the original pipeline's quirks took lots of experimentation, and avoiding canvas smoothing/aliasing required careful handling. - Written declaratively with React; performance is kept reasonable with a buffer pool and copy-on-write semantics. - You can share links to artwork from within the UI. E.g.: https://zenpaint.org/#p=KQumBQ5x - Mobile support was not considered here (for obvious reasons). It might still be usable on a larger phone or tablet but I have not tested this at all. There's something magical about making art within MacPaint's constraints: the 1-bit graphics, the limited resolution, the peculiar set of tools that still feel surprisingly expressive. Still some rough edges and missing features, but I'd love feedback from anyone who remembers the original.

Visit product View on Hacker NewsNov 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, notes, code · Missing: agents, macos, agent

81%81% 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.

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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% 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

35%35% 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

13%13% 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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