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Weather API with forecasts based on AI

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Weather API with forecasts based on AI

Meteosource is SaaS platform with a goal to achieve the highest possible weather forecast accuracy. Most weather services around the world prepare their forecasts based on a single global NWP model and then apply a local model such as WRF to the data. Meteosource uses a number of data sources and proprietary numerical weather prediction models to extract the most valuable information from each. It learns from historical data and forecasts from many different models, compares their data from different places and meteorological situations, and creates a single output that should minimise inaccuracies and biases of individual models. API documentation available at https://www.meteosource.com/documentation I greatly appreciate any feedback and thoughts on how to improve this service and make it easier to use!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, single · Missing: mac, agents, macos
79%79% 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
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
59%59% predicted probability of success on TrustMRR, 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
43%43% 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 · Strong signals: platform · Missing: plus, intuitive, reviews
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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