However, none of the dashboards actually analyzed how the engineers worked with the tools and if there were any areas for improvement! That's why I created https://www.promptster.ai.
Managers get aggregate level view of code quality and how that ties with team workflows (nothing on a per-engineer level). While engineers get personalized coaching on how they can save tokens while keeping output high.
We also have a tool built for individuals to test their local claude code setups. It's open-source and completely local. You can check that out here: https://github.com/pa-arth/cc-audit.
Would love feedback on either and reach out if you're interested in purchasing the teams product!
When PaddleOCR-VL-1.6 dropped, independent benchmarks put it at the top of document parsing models. I had to try it. I needed a provider, but there simply isn't one ready for production that I would trust.
So i set one up myself. I assumed that even after getting it running, serving a vision-language model would be expensive.
It turns out the opposite is true. Once I had it running properly, the cost was absurdly low. At proper GPU utilization, the cost is only around $1 per 1,000 pages.
The nearest competitors are either much lower quality (Azure Read) or absurdly expensive (Extend or Reducto). Even Mistral OCR 4 which is really good and pretty cheap is still 4x more expensive.
So I had to share it. I made the endpoitns public, and vibe-coded a simple dashboard.
Let me know what you think!