Zo

ZoneMapzone World Clock

Hacker News

ZoneMapzone World Clock

I built this because I kept needing to answer "when is 3pm EST for my teammate in Singapore" and none of the existing tools felt right for me - they're mostly lists. I wanted a map where time differences are spatially obvious. A few things that were technically interesting to build: The day/night terminator. It's not just a shaded half-sphere. I implemented a simplified USNO solar declination algorithm, computing the subsolar point for a given UTC timestamp, then tracing the great-circle terminator and sampling which side each pixel falls on. It renders pixel-by-pixel onto a Canvas overlay above the SVG map. During drag it bypasses React's render cycle entirely and draws directly on the canvas ref for zero-latency feedback. The map drag = time scrub. Dragging the map horizontally shifts time - one full map width equals 24 hours (1440 minutes). The math is straightforward (pixel delta → minute delta → new Date), but making it feel fluid required some care. City card updates use useDeferredValue so the main thread prioritizes the drag over re-rendering all the cards. URL state. The current city list, base city, and manual time are encoded in the URL and debounced at 500ms so you can share or bookmark a specific view. Restoring from URL has to deal with two city sources: ~306 hardcoded IANA cities and ~33k GeoNames cities that live in localStorage after a search. The GeoNames dataset was 33,334 cities after deduplication-by-timezone. I build a JSON file at deploy time rather than hitting the GeoNames API at runtime. Stack: React 18, TypeScript strict, Vite, Tailwind, Framer Motion, D3-geo Mercator, date-fns-tz, Hono for the city search API. MIT licensed. Source: https://github.com/zzjoey/ZoneMap Live: https://zonemap.live Known rough edges: mobile layout is functional but cramped, and the terminator calculation skips atmospheric refraction so it's off by ~0.5° at the poles.

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, code · Missing: mac, agents, macos
80%80% 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 · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% 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
33%33% 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
11%11% 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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