emergency-poncho VS AI-basketball-analysis

I've created a tool for replaying HTTP archives:

https://github.com/Tade0/emergency-poncho

There are many like it, but this one is mine.

Useful when you're a front-end developer and the backend app is not easily deployable locally and the test environment is down.

Also with it you can make a blazing-fast, browsable snapshot of JIRA.

https://github.com/Tade0/emergency-poncho

This is a HTTP mock server for reproducing issues on the front-end using a recorded HTTP archive(.har file).

The gimmick is that for a given endpoint it stores all the responses and serves them in a round-robin sequence.

This way you can simulate situations where e.g. a request has been retried after the JWT expired, or something special happens when an item is added to a list, and the list is refreshed afterwards - basically every case in which you need backend state.

It works... sometimes. Each new project I'm in uncovers new issues.

That being said with the test team giving me both videos and .har files of the bug reproduction I was able to solve a few long standing bugs in one legacy system.

Source code: https://github.com/chonyy/AI-basketball-analysis

I built an app to visualize and analyze basketball shots and shooting pose with machine learning.

https://github.com/chonyy/AI-basketball-analysis

The result is pretty nice. However, the only problem is the slow inference speed. I'm now refactoring the project structure and changing the model to a much faster YOLO model.

Thanks for asking! This is not a noob question.

I would say that the similar workflow could be applied to any ball-related sports. The object detection and the tracking algorithm is basically the same. Then, you could add any sport-specific feature!

For example, I have used a similar method to build AI Basketball Analysis.

https://github.com/chonyy/AI-basketball-analysis