Created this simple website where you can group existing GitHub repos and track their health based on their public data. The idea came from working as a Sr SRE/DevOps on mostly Kubernetes/Cloud environments with tons of CNCF open source products, and usually there are many competing alternatives for the same task, so I started to create static markdown docs about these GitHub groups with basic health data (how old the tool is, how many stars it has, language it was written in), so I can compare them and have a mental map of their quality, lifecycle and where's what.
Over time whenever I hear about a new tool I can use for my job, I update my markdown docs. I found this categorization/grouping useful for mapping the tool landscape, comparing tools in the same category and see trends as certain projects are getting abandoned while others catching attention.
The challenge I had that the doc I created was static and the data I recorded were point in time manual snapshots, so I thought I'll create an automated, dynamic version of this tool which keeps the health stats up to date. This tool became www.gitfitcheck.com. Later I realized that I can have further aspects as well, not just comparison within the same category, for example I have a group for my core Python packages that I bootstrap all of my Django projects. Using this tool I can see when a project is getting less love lately and can search for an alternative, maybe a fork or a completely new project. Also, all groups we/you create are public, so whenever we search for a topic/repo, we'll see how others grouped them as well, which can help discoverability too.
I found this process useful in the frontend and ML space as well, as both are depending on open source GitHub projects a lot.
Feedback are welcome, thank you for taking the time reading this and giving a try!
Created this simple website where you can group existing GitHub repos and track their health based on their public data. The idea came from working as a Sr SRE/DevOps on mostly Kubernetes/Cloud environments with tons of CNCF open source products, and usually there are many competing alternatives for the same task, so I started to create static markdown docs about these GitHub groups with basic health data (how old the tool is, how many stars it has, language it was written in), so I can compare them and have a mental map of their quality, lifecycle and where's what.
Over time whenever I hear about a new tool I can use for my job, I update my markdown docs. I found this categorization/grouping useful for mapping the tool landscape, comparing tools in the same category and see trends as certain projects are getting abandoned while others catching attention.
The challenge I had that the doc I created was static and the data I recorded were point in time manual snapshots, so I thought I'll create an automated, dynamic version of this tool which keeps the health stats up to date. This tool became www.gitfitcheck.com. Later I realized that I can have further aspects as well, not just comparison within the same category, for example I have a group for my core Python packages that I bootstrap all of my Django projects. Using this tool I can see when a project is getting less love lately and can search for an alternative, maybe a fork or a completely new project. Also, all groups we/you create are public, so whenever we search for a topic/repo, we'll see how others grouped them as well, which can help discoverability too.
I found this process useful in the frontend and ML space as well, as both are depending on open source GitHub projects a lot.
Feedback are welcome, thank you for taking the time reading this and giving a try!
Thank you, sendai