yeag123

The average Nebraska farm is 989 acers[1] as of 2022 and uses roughly ~1,200 acre-feet of water (or roughly 390 million gallons) per year[2]. So a single average Nebraska farm uses roughly 30 times the water the Lincoln data center uses per year. There are 44,479 farms/ranches covering 44 million acres, which accounts for over 90% of the state's total land area.

Until I looked this up, I didn't appreciate the scale of water usage in farming (even in a state like Nebraska that gets a large amount of rainfall compared to Central or Southern California). I hope that the current data center environmental focus continues to shed light on our industrial water and electrical usage.

I wanted an intelligent document processing SaaS (Document AI, Form Recognizer, the various PDF-to-JSON tools) that you could run on your own hardware.

The interesting bits:

- Three-tier extraction: PyMuPDF for digital PDFs (~50ms), Docling layout-only for scanned-but-readable, Docling+OCR for the rough stuff. Auto-fallback based on extracted character count. - Smart templates use vector similarity (Qdrant) to classify docs, then LLM extraction for fields — no regex, so layout drift doesn't break templates. - Local Ollama or Azure OpenAI, switchable per-user.

Built on top of Cole Medin's local-ai-packaged. Apache 2.0.

The workflow: Upload doc → LLM extracts structured data → Generate new doc from template.

It’s API-first, includes webhooks, and is built to be self-hosted/self-provisioned for privacy. Still very much a WIP, but looking for feedback on the feature set and the extraction accuracy.

I worked with manufacturing companies, and the amount of manual document extraction and manipulation, particularly from accounting documents, was always a large burden.

The goal is upload a document → extract structured fields via LLM → generate new documents from templates. Has a dashboard, with an API, along with a webhook, very much a WIP.