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Weather2Geo – Geolocate screenshots from weather widgets

Hacker News

Weather2Geo – Geolocate screenshots from weather widgets

Hi HN, I built an OSINT tool called Weather2Geo that helps locate where a screenshot was taken based on the weather widget data visible (e.g. temp, condition, and local time in the taskbar). People often post these without realizing how specific those values can be in combination. It works by comparing the given weather condition, temperature, and time against current data from thousands of cities. It’s timezone-aware, supports fuzzy matching, and groups results geographically to reduce noise. It’s most effective on recent screenshots, ideally taken within the last hour—since weather and daylight conditions change quickly. The tool helps pinpoint likely locations when screenshots lack EXIF data or other traditional OSINT clues. It’s open source here: https://github.com/elliott-diy/Weather2Geo Would love feedback, bug reports, or to hear if you think this has other use cases. - Elliott

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
74%74% 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: open · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: widgets · Missing: mobile apps, ios, personal
56%56% 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
38%38% predicted probability of success on AppSumo, 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.

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