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I built a free worldwide holiday and disruption tracker

Hacker News

I built a free worldwide holiday and disruption tracker

Hi HN, I’m the creator of Alarms.Global, a free tool I built while working in logistics. I was constantly juggling scattered holiday calendars and scanning local news to figure out why shipments were delayed. What it does: Tracks public holidays in 190+ countries (so you know when offices, ports, and customs are closed) Aggregates real-time transport disruptions (strikes, port congestion, weather issues, etc.) Shows everything on a filterable world map Works in 14 languages The idea was to create a single open platform to see what might stop goods from moving today. Tech: It’s built with F#/.NET on the backend, Qdrant for vector search, GDELT + custom feeds for disruptions, and Leaflet.js/MapTiler for the map UI. I kept it lightweight to be fast and browser-friendly. It’s still early some features are in progress but the core is live. I’d love feedback: What disruption sources would you want added? Anything confusing in the interface? Try it here: https://alarms.global

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, single, using · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, friendly, interface · Missing: plus, intuitive, reviews
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
41%41% 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 · Missing: mobile apps, ios, personal
26%26% 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
12%12% 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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