Been Ski – Track ski resort visits across 4,500 resorts worldwide

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I built a web app to track ski resort visits. Think "been.coffee" but for skiing. Features: - 4,586 ski resorts with coordinates (scraped from skiresort.info, geocoded via Nominatim) - Interactive map (Leaflet + OpenStreetMap) - Visit logging with dates/notes - Stats dashboard Tech stack: React Native + Expo (web), Firebase Auth/Firestore, Zustand for state, deployed on Vercel. The most interesting part was building the resort database - wrote a Node.js scraper that pulled 6,130 resorts and geocoded ~75% of them successfully. Happy to share the scraping approach if anyone's interested. Feedback welcome!

Visit product View on Hacker NewsJan 27, 2026

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76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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