Timezone App – Visual meeting scheduler for distributed teams

Hacker News

Scheduling meetings across multiple time zones has always been painful for me, especially across daylight saving time transitions. So I built a visual timeline that makes it easy to find overlapping availability. Add your locations, drag to select a time range, and share a link. Recipients see the proposed times in their local time zone automatically. A few things that might be interesting: * Location search over GeoNames with fuzzy matching using weighted edit distance, so typos and partial names still resolve correctly. * Shareable links encode the selected time range and locations in a base62 payload to keep URLs short and stateless — no database lookup needed. * Handles the annoying edge cases: DST transitions use the IANA timezone database, and 15/30-minute UTC offsets (Nepal, India, Newfoundland) work correctly. * Google Calendar and Outlook integration, but all calendar data is fetched and processed entirely in the browser. Events are never fetched or stored on the server. Would love feedback on what's useful, not useful, or could be improved!

Visit product View on Hacker NewsMar 29, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, visual · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

42%42% 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

41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

23%23% 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.

Incorrect prediction on native model

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