Jev for OCR, documents and classification

Jerry Liu

@jerryjliu0

DocJev is the fastest way to classify and split complex document packets ⚡️. (and the default is fully free and OSS!) I made a sick teaser video below. TY Opus 5.5 🙏 check it out: github.com/jerryjliu/docj…

Hassan

@nutlope

I used Jev to classify 1,018 AI research papers. The result: $0.08 total cost and 256ms median end-to-end latency per paper. The pipeline was: 1. Summarize each paper with DeepSeek V4 Flash 2. Send the title + summary + 24 possible topics to Jev 3. Use Jev to classify each paper 4. Visualize everything on http://1kpapers.com The summaries cost $3.99 on @togethercompute. The classifications cost $0.08 on @typesafeai. So for just over $4 of inference, I ended up with a pretty useful way to explore the top AI research papers from the past year. I think this is where things are heading: different models for different parts of the workflow, instead of using one model for everything. I’m running evals on the Jev classifications before replacing the current ones, but the site is already live: http://1kpapers.com

Milind S

@milindlabs

Okay so Jev can actually do computer use really well Without any screenshots, or LLMs and no Pixels leave my mac I dont even read the Dom elements A local CoreML model segments every button and UI element on screen. On-device OCR reads the labels. That text is all Jev gets. It returns a probability across those elements and tells me the best one to click. Then it clicks, re-runs detection, and decides again. In a loop until the goal is done. ~90ms per decision. Faster than any LLM computer use I've tried. Blazing fast computer use, without any latency @typesafeai is building something really interesting

iagolast

@iagolast

Caso de uso de @typesafeai: Revisar todas las facturas de la empresa y clasificarlas contablemente en segundos 🫠

Fayaz Ahmed

@fayazara

Made myself a little image classifier with OCR + Jev It was able to categorise ~900 images in 40 seconds Pretty cool

Hamilton Ulmer

@hamiltonulmer

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!

Hamed Valigholizadeh

@hametgholizadeh

JEV IS INSANE. I gave it 80 real exam questions and 297 practice ones. In 80 seconds, it told me which ones are most likely to appear on the real exam and which ones aren’t. All for $0.0256. Can't stop playing with @typesafeai 😁

Trinay Hari

@hari_trinay

Built a construction plan-set classifier with Jev. Proq turns civil and building plan sets into bills of materials using an LLM pipeline we built on GPT-4.1. Jev classified an entire 26-sheet plan set in 2.9 seconds for $0.0052. It matched GPT-4.1 and GPT-6 Astra on 100% of sheet-level classifications while running 17–21x cheaper and 5x faster than our production pipeline.

Ateeq

@TPateeq

I used Jev to solve a problem every agent eventually runs into: reading logs. 22.8M lines, and running an LLM on every one would've cost $1,120. Tocsin groups them into 11,812 repeating patterns, then asks Jev about each pattern once. 6 minutes, 64 cents, 123 patterns that actually needed to be looked at. http://github.com/TPAteeq/tocsin The paging policy is just a prompt. You tell it what should wake someone up at 3 am and what's just another log line.

Sabrina

@sabrinaesaquino

Jev is now live on the Venice API. Watch it classify 24,000 Hacker News posts into 12 categories in about 2 minutes

Kenny Chen｜AI 实战

@KennyChinaTech

Jev 这个案例很适合小团队：2,300 篇 AI 论文，约 83 秒，成本 $0.14。 但这个数字不能直接当成 Jev 的单模型成本。旧标签先由 DeepSeek V4 Flash 跑过，真正该测的是整条分类链路：预处理、Jev 决策和人工抽检加起来还剩多少。 x.com/omarsar0/statu…

Frit🅾️ Pendej🅾️

@vwapster

Grok bot + Jev + Laya. Self improving and auto calibrating Kalshi Solana trading bot. Still on paper. Looks promising.

Stanislav Sorokin

@stas_sorokin_

1,000 AI papers sorted into 24 topics for $0.0585. Then Opus 5 graded the labels. @nutlope's Jev paper map went viral, but the pipeline never shipped and the eval was "still running". So I rebuilt both and opened them. The first judge run came back empty: Opus spent its whole budget thinking and answered nothing. Reasoning off, second run: it agreed with Jev on 85 of 100 papers, at 153x the cost and 1.9s against 57ms per paper. The 15 misses are not random. One number Jev already returns tells you which labels to recheck. Cheap models sort. Expensive models audit only what the cheap one flags. Repost if you classify anything at scale, because the eval rows are public and anyone can rerun them with their own judge in one command. Code in the reply.

Richard Meng

@richard_meng_01

Nitpicky, an AI generated photo detector powered by jev AI generated photos can be told from nits. That's why we build something to zoom into every detail: faces, fingers, characters, numbers, poses, where common senses fall apart, judged by jev

Artificial Intelligence Papers

@SciFi

Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents Dongming Jiang, Yi Li, Bingzhe Li arxiv.org/abs/2609.23986 [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙻𝙶]

Spikez 99.9%

@0xSpikez

Jev Founder, Diogo Amogo, just released 12-page PDF on building a Jev Harness for coding agents this is a 10-step blueprint on how to make your coding agents 200× faster and 400× cheaper: step 1 → meet Jev: the LLM writes, the harness executes, Jev decides what each turn sees, where it routes and whether it runs step 2 → ask the question that breaks every agent: how would you design one if LLMs had no KV cache? step 3 → stop routing blind: Opus → Sonnet → Opus costs 6.19 vs 4.15 for pure Opus, because handing back reprocesses the whole context step 4 → follow the tokens: reading and searching take 56.2% of tool turns and 46.5% of tokens. Writing code is under 10% step 5 → score every chunk per query: hide, short summary, long summary or full. Compress after the question, not before step 6 → disclose tools in tiers: one-line snippets for 100s of tools, schema on demand, docs for one-off queries. Batteries stop costing context step 7 → load instructions by condition: touching *.tsx loads the style guide, billing/ loads its gotchas file, and compaction can't erase either step 8 → route by trust, not just difficulty: secrets and infra stay on first-party frontier models, public docs go to the cheapest one step 9 → share one retrieval pass: cross-model review, eval generation, ELI5 explainers and live progress pages all run read-only in the background step 10 → gate every command: programmable allow / ask / deny policies that read a script before it runs, not just its name Send this PDF and the article below to your Claude Code or Codex instance and start shipping 200× faster.