Rajat here. A few technical details about how this works. Stack: - Next.js - Tailwind - KaTeX for rendering - Supabase storage - deployed on Vercel The pipel

Rajat here. A few technical details about how this works. Stack: - Next.js - Tailwind - KaTeX for rendering - Supabase storage - deployed on Vercel The pipeline is roughly: image → vision model → Markdown + LaTeX → custom renderer The tricky part isn’t OCR itself — it's preserving structure. Examples: • consecutive equations with aligned `=` signs need to become a single `align` block • handwritten tables must be reconstructed from vertical alignment patterns • numbered problems must stay separate instead of merging The system prompt ended up being ~300 lines mostly consisting of *negative constraints* like: - don't simplify math - don't merge derivation steps - don't reorder columns Without those rules the model constantly tries to "improve" the notes. One surprising lesson: prompt engineering for OCR is very different from chat prompts — you want the model to be extremely literal. Still working on better handling for diagrams and messy annotations. Curious if anyone here has worked on *math layout detection or document AI*.