User repository on GitHub
A very fast document classifier/splitter using Jev
- GitHub stars
- 468
- Forks
- 32
- Primary language
- Python
- License
- apache-2.0
- Last pushed
- Updated Sep 2026
Repo stats from the GitHub API, cached Sep 2026.
CommunityTools & CLIs
DocJev
Classifies and splits PDFs, DOCX, and PPTX with local page text from LiteParse and category decisions from Jev.
- Category
- Tools & CLIs
- Format
- —
- Published by
- Community
- Author
- jerryjliu
- Added
- 2026-09-28
- Last verified
- 2026-09-28
Highlights
- Classify returns one category, probabilities, and review flags. Split returns ordered categories and page ranges.
- On a 40-document pilot both engines classified 40 of 40 originals correctly. Jev split 7 of 8 packets exactly.
- A 15-page public-finance packet keeps a ten-page BEA release together and separates two adjacent Treasury results.
- DOCX and PPTX need LibreOffice. Native PDFs do not.
- Local OCR does not make the Jev call offline.
Quickstart
bash
uv sync
uv run docjev classify examples/real/originals/r04.pdf \
--rules examples/real/classify/rules.yamlWatch out
Apache-2.0. Needs Python 3.11+ and TYPESAFE_API_KEY. Independent of LlamaIndex hosted classify or split. The next 40-document set is a protocol, not a measured result.
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From the community
Posts from builders shipping with Jev right now.
Browser Use Ultrafast, powered by Jev
Breaking: Browser Use + Jev = Ultrafast ⚡ Findings flights took 7s and cost only $0.0039 🤯 > new action space every step > DOM state space > small LLM fallback to type (this video is at 1x speed btw) Built a tiny open source browser agent. try it below ↓
A really smart switch statement
hype-free explanation of jev: jev does not replace gpt / claude jev is just a *really* smart switch statement like if 2016 ml classifiers got 2026 levels of intelligence it's a new* type of tool that will make a lot of workloads insanely fast, cheap, and accurate * = and by Show more
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
When a designer gets Jev
when a designer gets access to Jev
Full Jev video tutorial
Full Jev Tutorial What it is, how you can build with it and what new applications it can unlock → 0:00 Intro → 0:34 Jev explained → 4:06 API setup → 5:59 Demo 1: Voice-controlled browser → 11:33 Demo 2: AI memory → 17:27 Demo 3: YouTube predictor
The case against Jev-scored compaction
This is a terrible compaction strategy that fundamentally doesn't understand how compaction and context management work. Seems like a lot of people are confused so let's break this down. 1. Compaction isn't a filter The role of compaction is to clean up history to keep the Show more
found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant
Classifying rows in DuckDB
I made a DuckDB extension where you can use @typesafeai 's Jev to do quick classification of rows in any csv/parquet file or duckdb table about 10sec for 1k rows ~ better than using an LLM, way more ergonomic than a classifier game-changing for data analysis!
