Ticking Away, a Dark Side of the Moon Watchface Written in Zig

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I built a watchface inspired by Pink Floyd's "Dark Side of the Moon". A prism that refracts light into a rainbow to show the time. The minute hand is a light ray, the rainbow points at the hour. It's written in Zig, which lets me target the browser (WASM), a Raspberry Pi Zero 2 W driving a 13.3" e-ink display as a wall clock, a Pico 2 for bare-metal battery operation, and eventually the Pebble Round 2 smartwatch. I built it with Claude Code (Opus) as an experiment in AI-assisted coding while keeping real ownership of the code. Claude does the fast iteration, I make the architecture decisions and refactor by hand. Every line in lib/ is reviewed and curated. The rendering went through a few iterations. Started with physical dispersion using Cauchy's equation, but tuning the coefficients for all 720 time combinations was a nightmare and some just looked bad. Then I tried bouncing light inside the prism, which was better but had ugly corner cases. In the end I dropped the physics and went with a simple traced path that always looks good.

Visit product View on Hacker NewsMar 29, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, physical, using · Missing: mac, agents, macos

89%89% 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.

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

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% 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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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