As someone who looks at GitHub accounts as a resume, I can tell you that improvement over time is amazing to see. Assuming it’s clear what’s most interesting or recent.
I think that by definition this is a tradeoff. Most times you talk to data scientists that want a fully automated end-2-end solution, that doesn't require they change anything about their current workflow, and that any future modifications to their workflow would be supported as well.
That is magical thinking. I prefer best of breed solutions that integrate nicely with other best of breed solutions every day. That way if a tool doesn't suit you tomorrow, you can relatively easily swap it out for something better
Congrats on the launch. To me this feels like DVC but with a slightly more convenient python API, and without the pipeline which might be really great. How are you organizing experiments though? Without Git it seems too easy to have experiments become unusable. And filtering according to accuracy is not really a solution..