We've made a lot of data tooling things based on LLMs, and are in the process of rebranding and launching our main product. 1. sketch (in notebook, ai for pand

We've made a lot of data tooling things based on LLMs, and are in the process of rebranding and launching our main product. 1. sketch (in notebook, ai for pandas) https://github.com/approximatelabs/sketch https://github.com/approximatelabs/sketch 2. datadm (open source, "chat with data", with support for the open source LLMs (https://github.com/approximatelabs/datadm https://github.com/approximatelabs/datadm) 3. Our main product: julyp. https://julyp.com/ https://julyp.com/ (currently under very active rebrand and cleanup) -- but a "chat with data" style app, with a lot of specialized features. I'm also streaming me using it (and sometimes building it) every weekday on twitch to solve misc data problems (https://www.twitch.tv/bluecoconut https://www.twitch.tv/bluecoconut) For your next question, about the stack and deploy: We're using all sorts of different stacks and tooling. We made our own tooling at one point (https://github.com/approximatelabs/lambdaprompt/ https://github.com/approximatelabs/lambdaprompt/), but have more recently switched to just using the raw requests ourselves and writing out the logic ourselves in the product. For our main product, the code just lives in our next app, and deploys on vercel.