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Meteosource – Hyper-local weather API based on improved ML models

Hacker News

Meteosource – Hyper-local weather API based on improved ML models

Hi HN, I am excited to share the new version of my weather API with you. It offers even more accurate forecasts than before, thanks to updates to our proprietary ML model. My weather forecast website initially started as a personal project. Later, it became more professional, and recently, we have evolved into an advanced weather API. Our goal from the beginning has been to offer affordable yet superior services. To achieve high accuracy, we incorporated machine learning into our calculations. Our forecasts pull data from multiple sources and use proprietary numerical weather models. These models also learn from historical data to reduce bias. This applies to hyper-local forecasts as well, where each point is further refined based on surrounding data. In addition, you will find advanced features like a minute-by-minute forecast for the next hour, updated every ten minutes. I would be grateful for all your thoughts and any ideas!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
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best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, new · Missing: agents, macos, agent
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
47%47% 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
14%14% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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