pytkml VS ultra-weather

Code: https://github.com/rbitr/pytkml

I didn't explain it well; this is an area that's becoming increasingly important

I worked on (wouldn't say I completed) a testing framework for ML models, where you can specify a series of tests for a model to pass. The unique part is emphasizing support from the training data for the inferences you expect the model to make, i.e. checking that similar training data exists for some test cases and is influential in predicting them. It's pretty niche, and I don't explain it very well, but I remain convinced that with the right framing it represents a more rigorous way of making sure machine learning models are built and used "responsibly".

https://github.com/rbitr/pytkml

I tried sharing a couple of my web apps:

- HN the way I want to read it: https://hw.leftium.com/

- Source code: https://github.com/Leftium/hckrweb

- Weather forecast compared to last two days' weather: https://github.com/Leftium/ultra-weather#readme

One of the reasons I built https://uw.leftium.com/ was so I could see the previous two days' weather.

It uses the DarkSky API, but also supports other weather API's: https://github.com/Leftium/ultra-weather#readme

UltraWeather uses the openweather geocoding API[1]:

https://openweathermap.org/api/geocoding-api

Pretty generous free tier, and paid options seem pretty reasonable.

[1]: https://github.com/Leftium/ultra-weather#specify-a-location