XRain – Explore rainfall statistics around the world

Hacker News

Last year I launched a website that allow people to see rainfall statistics that are based on satellite data. Historical rainfall information usually comes from rain gauges, and while these are fantastic there are many parts of the world that don't have many long term gauges, or where that data is hard to access. Satellite data can be an invaluable source of information for those data-scarce areas. The business model is to sell "extreme precipitation" data that can be used for flood modelling. Unfortunately, after a year I still haven't made a single sale. I've tried various ways of advertising, mainly via messaging people on LinkedIn who would actually have a use for it. I'm still proud of what I've built, even if it's a flop! The tech stack is SolidJS with a Django API backend. Fun feature: jump to a completely random part of the world by clicking the "Random" button. I'd love feedback on anything, such as how to improve the UI/UX of the mobile view of the map page.

Visit product View on Hacker NewsSep 27, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, single · Missing: mac, agents, macos

91%91% 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

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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

37%37% 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.

Incorrect prediction on native model

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