Chisle
The coding-agent plugin that bills 52% of a bare model across 20 live tasks. Terse persona + tool-output compressor + context diet.
Open-Source ☀️ Weather API with free access for non-commercial use. No API Key required ✅. Accurate weather forecasts for any location. Open-Meteo provides high-resolution open data ranging from 1 to
Translation pending
Developer toolsGitHub @open-meteoOnlineOwner unconfirmed
Have you tried it? One tap is enough. No sign-in needed.
Repository information has not been collected yet.
No updates detected yet. GitHub releases will appear here.
Free Weather Forecast API for non-commercial use
Translation pending
Loading the full README.
Read the full README ↗Open-Meteo is an open-source weather API that offers free access for non-commercial use. No API key is required, so you can start using it immediately.
Explore the API documentation or follow the Open-Meteo blog for project updates.
curl "https://api.open-meteo.com/v1/forecast?latitude=52.52&longitude=13.41&hourly=temperature_2m"
Open-Meteo uses open weather forecasts published by national weather services. These numerical weather predictions are free to download, but working with them requires expertise in binary file formats, grid systems, map projections, and weather forecasting.
Open-Meteo combines high-resolution regional and global weather models. More than 5 TB of data is downloaded and processed daily from multiple national weather services. The processed data is stored in a custom file format optimized for compressed time-series access, such as retrieving a multi-day temperature forecast for one location.
Open-Meteo provides complete access to its source code and openly lists its data sources, with credit to the national weather services that produce them. You can launch your own weather API with Docker or the prebuilt Ubuntu package, inspect every stage of data processing, and adapt the service to your needs. Contributions are welcome.
The API is available for non-commercial use at no cost. It combines frequently updated regional and global models to select suitable forecast data for locations worldwide.
Apps:
This is the beginning of the README. Read the rest at the original. Read the full README ↗