Sam Fleder

Here's another example where our specialized document models are leading frontier VLMs on document AI. If you've ever tried to get an agent to fill a real form, you have likely experience this failure. It reads the page fine, then misses half the fields because it can't put a tight box on them. So our crack ML team @nutrientdocs built a Form Field Detection VLM and put it on a public benchmark. On typed-field F1 at IoU 0.5 over 100 real form pages and 701 fields - our two models take #1 and #2 on a 16-model board. Nutrient Hybrid 0.477, Nutrient VLM 0.406. Best frontier VLM (GPT-5.6 Sol) 0.357, and the rest of the frontier trails from there. The point isn't "we beat GPT." General VLMs are incredible. They're just not built for precise form-field localization like a specialized model is from a company with years of expertise in the document space. That's the whole bet. Everything's open. The model card, dataset, demo, code. Run it yourself: huggingface.co/nutrientdocs/… and give it a like if you find it useful.

Everyone says Claude Cowork already does PDFs. It doesn't it, it just installs some open-source library to read extracted text yet stays blind to the actual document. So it can't show you where on the page anything is. We shipped the @nutrientdocs PDF Editor for Claude Cowork. You can use it now for free. The full viewer renders inline in the chat panel, so you and Claude are looking at the same page. 16 tools: open, annotate, fill forms, redact, search. Real native PDF objects, not overlays: they travel with the file and save straight back to the original on disk. Drive it yourself, ask Claude, or both in one session. Files never leave your machine. No signup, no license key, connector is MIT. Runs in Cowork, inside @claudeai Desktop.

Launching today: Nutrient Data Extraction API. Parse PDFs, scans, images, and Office files into spatial JSON or Markdown — with coordinates, confidence, and page context. Built for AI agents, RAG, search, automation, and human review. nutrient.io/api/data-extract…

Update on this: we just hit #1 across the board. 0.89 overall accuracy (was 0.88, now ahead of docling) 0.93 reading order (best in class) 0.007s per page — 87x faster than the next most accurate parser New in this release: --enable-image-export for vision LLMs Still free for 1,000 docs/month. Still runs locally. npm i -g @pspdfkit/pdf-to-markdown

Shipping Day! We’re all proud and excited @nutrientdocs to officially release our PDF to Markdown converter. On our 200 document hand annotated markdown based benchmark it beats out every other major parser for speed and reading order while matching accuracy. We are shipping it as a closed source, free to use for up to 1,000 documents per month, no license key required, and your documents stay completely local. We’ve had early beta testers give us phenomenal feedback but please reach out and share publicly your experience with it. Our future roadmap: - adding our OCR Vision capabilities to it. - supporting Windows x64 - speed increases (yeah, make it even faster) - support for DOCX to md - more based on feedback. If you find it useful, please star the repo and tell your friends!

We've been cooking @nutrientdocs on a PDF, DOCX, and other common document file types converter to Markdown packaged as a simple CLI library. On all our benchmarks we've done so far it's the fastest available by a long shot, most token efficient, and on par with accuracy of the top libraries. - 2x faster than liteparse that was just released. - 5x faster than pymupdf - 4x faster than markit who @badlogicgames just praised for its speed. - 3x faster than Microsoft's markitdown. We'll be releasing the converter as freemium closed sourced here soon. Stay tuned. Let me know if your interested in early access.