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Doc-OCR router using Jev TypeSafe AI A Jev-powered router that looks at a PDF page by page, decides which pages actually need OCR, extracts the rest locally. Result: save cost on # OCR pages + speed

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Misbah Syed

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27,028 görüntüleme • 1 ay önce •via X (Twitter)

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Built a model router with Jev by TypeSafe AI. Jev decides what model fits your request best and the request is sent to that model.
0:14

Built a model router with Jev by TypeSafe AI. Jev decides what model fits your request best and the request is sent to that model.

Duncan

120,065 görüntüleme • 1 ay önce

China open-sourced a peanut-sized OCR that parses entire 100-page PDFs in one shot.. It's called Unlimited-OCR. Only 3B params. Runs locally. Every other OCR tool chops your doc into pages and loses the thread. this one reads the whole thing in a single pass. → One-shot "long-horizon" parsing (32K context window) → Multilingual, out of the box → 93% on the standard parsing benchmark (+6 over baseline) → <0.11 error rate past 40 pages → Runs 100% locally on your own hardware → Works with Transformers, vLLM, SGLang, Docker, Ollama, llama.cpp Traditional cloud OCR (Textract, Google Vision, Azure Doc Intelligence) costs $1.50–$15 per 1,000 pages. This runs on your machine. For free. Forever. Baidu built it explicitly to push DeepSeek-OCR one step further. Already at 1.9M downloads on Hugging Face and most people have no idea it exists yet. 100% open source.
0:13

China open-sourced a peanut-sized OCR that parses entire 100-page PDFs in one shot.. It's called Unlimited-OCR. Only 3B params. Runs locally. Every other OCR tool chops your doc into pages and loses the thread. this one reads the whole thing in a single pass. → One-shot "long-horizon" parsing (32K context window) → Multilingual, out of the box → 93% on the standard parsing benchmark (+6 over baseline) → <0.11 error rate past 40 pages → Runs 100% locally on your own hardware → Works with Transformers, vLLM, SGLang, Docker, Ollama, llama.cpp Traditional cloud OCR (Textract, Google Vision, Azure Doc Intelligence) costs $1.50–$15 per 1,000 pages. This runs on your machine. For free. Forever. Baidu built it explicitly to push DeepSeek-OCR one step further. Already at 1.9M downloads on Hugging Face and most people have no idea it exists yet. 100% open source.

Superman

1,113,701 görüntüleme • 3 ay önce

You can now classify documents with open-weight VLMs at ultra-low cost with our TypeSafe AI Jev-compatible API in the VLM Run gateway! $1 buys you 6700+ pages, and the full 1,651-page OmniDocBench run cost 25 cents. It reads the page as pixels (no OCR->LLM hacks) and hands back one typed, calibrated decision per page - ~180ms p50 e2e. Jev really opened developers' eyes to re-thinking how we process data at the cost-pareto front. A week later we're bringing that to our APIs with vision as a first-class citizen. Try our gateway today - free to sign-up and try!
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You can now classify documents with open-weight VLMs at ultra-low cost with our TypeSafe AI Jev-compatible API in the VLM Run gateway! $1 buys you 6700+ pages, and the full 1,651-page OmniDocBench run cost 25 cents. It reads the page as pixels (no OCR->LLM hacks) and hands back one typed, calibrated decision per page - ~180ms p50 e2e. Jev really opened developers' eyes to re-thinking how we process data at the cost-pareto front. A week later we're bringing that to our APIs with vision as a first-class citizen. Try our gateway today - free to sign-up and try!

Sudeep Pillai

20,816 görüntüleme • 1 ay önce

VLMs can be surprisingly accurate and token-efficient on the right tasks. In a bake-off between VLM-based document classification vs. OCR-based classification using our new TypeSafe AI-compatible SystemOne API, we're seeing over ~7x token-efficiency! VLM: 372 tokens/page, fixed. OCR->Text: 2,002 median, 19,159 on the worst page.
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VLMs can be surprisingly accurate and token-efficient on the right tasks. In a bake-off between VLM-based document classification vs. OCR-based classification using our new TypeSafe AI-compatible SystemOne API, we're seeing over ~7x token-efficiency! VLM: 372 tokens/page, fixed. OCR->Text: 2,002 median, 19,159 on the worst page.

Sudeep Pillai

10,887 görüntüleme • 1 ay önce

🚨 CHINA JUST OPEN SOURCED AN OCR THAT EATS 100 PAGE PDFS IN ONE PASS It’s called Unlimited OCR. Only 3B params. Runs locally. Every other OCR tool chops your doc into pages and loses the thread. This one reads the whole thing in one continuous pass. → One shot full context parsing (32K context window) → Multilingual out of the box → 93% on the standard parsing benchmark (6 points over baseline) → Under 0.11 error rate past 40 pages → Runs 100% locally on your own hardware → Works with Transformers, vLLM, SGLang, Docker, Ollama, llama.cpp Traditional cloud OCR (Textract, Google Vision, Azure Doc Intelligence) costs $1.50 to $15 per 1,000 pages. This runs on your machine. For free. Forever. Baidu built it explicitly to push DeepSeek OCR one step further. Already at 1.9M downloads on Hugging Face and most people have no idea it exists yet. 100% open source.
0:13

🚨 CHINA JUST OPEN SOURCED AN OCR THAT EATS 100 PAGE PDFS IN ONE PASS It’s called Unlimited OCR. Only 3B params. Runs locally. Every other OCR tool chops your doc into pages and loses the thread. This one reads the whole thing in one continuous pass. → One shot full context parsing (32K context window) → Multilingual out of the box → 93% on the standard parsing benchmark (6 points over baseline) → Under 0.11 error rate past 40 pages → Runs 100% locally on your own hardware → Works with Transformers, vLLM, SGLang, Docker, Ollama, llama.cpp Traditional cloud OCR (Textract, Google Vision, Azure Doc Intelligence) costs $1.50 to $15 per 1,000 pages. This runs on your machine. For free. Forever. Baidu built it explicitly to push DeepSeek OCR one step further. Already at 1.9M downloads on Hugging Face and most people have no idea it exists yet. 100% open source.

Brooks Whale X 🐋

499,741 görüntüleme • 1 ay önce

Unlimited-OCR is a 3B parameter model that parses entire 100-page PDFs in one shot — no page chunking, no lost context. +⁠32K context window, reads the whole document in a single pass +⁠93% on standard parsing benchmarks, +6 over baseline +⁠Under 0.11 error rate past page 40 — where every other OCR tool falls apart ⁠+Multilingual out of the box, runs locally via Transformers, Ollama, llama.cpp, Docker +⁠Textract, Google Vision, and Azure Doc Intelligence charge $1.50–$15 per 1,000 pages 1.9M downloads on HuggingFace and most people have never heard of it. This runs on your machine. For free. Forever. Model link:
0:13

Unlimited-OCR is a 3B parameter model that parses entire 100-page PDFs in one shot — no page chunking, no lost context. +⁠32K context window, reads the whole document in a single pass +⁠93% on standard parsing benchmarks, +6 over baseline +⁠Under 0.11 error rate past page 40 — where every other OCR tool falls apart ⁠+Multilingual out of the box, runs locally via Transformers, Ollama, llama.cpp, Docker +⁠Textract, Google Vision, and Azure Doc Intelligence charge $1.50–$15 per 1,000 pages 1.9M downloads on HuggingFace and most people have never heard of it. This runs on your machine. For free. Forever. Model link:

0xMarioNawfal

84,661 görüntüleme • 3 ay önce

last week we open sourced: anydoc: parse 14 document formats, ~5ms per page pdf-inspector: parses + classifies pdfs, no waiting on OCR 14k stars each, built in rust. now powering Firecrawl /parse, which adds OCR for the hard ones what should we open source next?
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last week we open sourced: anydoc: parse 14 document formats, ~5ms per page pdf-inspector: parses + classifies pdfs, no waiting on OCR 14k stars each, built in rust. now powering Firecrawl /parse, which adds OCR for the hard ones what should we open source next?

Nicolas Camara

101,602 görüntüleme • 3 ay önce

Introducing Respan Router, powered by Span-01. Automatically routes every request using task context, model capabilities, and cache-aware cost estimates behind 1 model id: 𝐬𝐩𝐚𝐧-𝐫𝐨𝐮𝐭𝐞𝐫 - 13% lower latency than jev-router on τ-bench airline - 37% cheaper than the most accurate single model, at matching accuracy - 0% processing fee on Respan Gateway
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Introducing Respan Router, powered by Span-01. Automatically routes every request using task context, model capabilities, and cache-aware cost estimates behind 1 model id: 𝐬𝐩𝐚𝐧-𝐫𝐨𝐮𝐭𝐞𝐫 - 13% lower latency than jev-router on τ-bench airline - 37% cheaper than the most accurate single model, at matching accuracy - 0% processing fee on Respan Gateway

Respan

18,112 görüntüleme • 1 ay önce

Announcing llama-ocr – a free + open source OCR tool! It takes documents (images for now) & outputs markdown, and does really well for complex receipts, PDFs with tables/charts, ect... Powered by Llama 3.2 vision on Together AI & available on npm today!
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Announcing llama-ocr – a free + open source OCR tool! It takes documents (images for now) & outputs markdown, and does really well for complex receipts, PDFs with tables/charts, ect... Powered by Llama 3.2 vision on Together AI & available on npm today!

Hassan

444,489 görüntüleme • 2 yıl önce

we built blazing fast computer/browser use with Jev + Stagehand. this task cost $0.001 and executed at near instant speed (in a remote browser btw) the loop: observe the page, send a11y tree as state + actions as questions, Jev decides the next action, then Stagehand executes it.
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we built blazing fast computer/browser use with Jev + Stagehand. this task cost $0.001 and executed at near instant speed (in a remote browser btw) the loop: observe the page, send a11y tree as state + actions as questions, Jev decides the next action, then Stagehand executes it.

Kyle Jeong

72,359 görüntüleme • 1 ay önce

Which are the you? Filmed at @Pursuit OCR #group #friend #friends
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Which are the you? Filmed at @Pursuit OCR #group #friend #friends

karseliz

73,700 görüntüleme • 1 yıl önce

Jev dropped and people immediately started doing stupid sh*t with it people gave this thing trading bots, browsers, Doom, drones, dinosaurs and tax forms and basically said: “you decide.” here’s what happened: > jev-trader Jev gets a new Monad block every ~300ms and decides whether to place a live limit order. no essay. just the decision. 1,911 stars ▸ ⁠ > jev-ultrafast a browser agent where Jev decides every click. the text model only wakes up when something actually needs to be written. 16,758 stars ▸ ⁠ > jev-doom-agent someone compiled actual Chocolate Doom to WebAssembly and let Jev make the tactical decisions every frame. yes. Doom. ▸ ⁠ > jev-t-rex-runner remember that stupid Chrome dinosaur? now Jev plays it. jump → duck → run → repeat ▸ ⁠ > typesafe-chess Jev went against a real chess search engine. two games. colors swapped. the search engine won both — and overruled Jev’s first instinct on roughly half the moves. ▸ ⁠ > jev-drone camera → Jev → drone. a simulated quadrotor has to clear five stations while Jev looks at the situation twice a second and decides what happens next. ▸ ⁠ > tax-doc-classifier then someone gave it IRS forms. 261 documents. 100% strict accuracy. roughly $0.001/page. ▸ ⁠ > killmyidea this one is evil lol tell it your startup idea. Jev looks at it from different angles and gives you: KILL / FIX / SHIP ▸ ⁠ > jev-curate throw huge Parquet / JSONL datasets at it. Jev judges 1,500+ rows/sec and keeps only the stuff that passes your rules. ▸ ⁠ > pg-jev and now it’s inside PostgreSQL. ask questions about your own tables in plain English → get the decision back. ▸ ⁠ and this is the weird part: none of these need Jev to write you a beautiful paragraph. they need it to look at a situation and pick: BUY / WAIT CLICK A / CLICK B JUMP / DUCK KILL / FIX / SHIP KEEP / DROP that’s basically the whole Jev idea. LLMs think and write. Jev decides. code does. full setup + my three-question Jev test below
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Jev dropped and people immediately started doing stupid sh*t with it people gave this thing trading bots, browsers, Doom, drones, dinosaurs and tax forms and basically said: “you decide.” here’s what happened: > jev-trader Jev gets a new Monad block every ~300ms and decides whether to place a live limit order. no essay. just the decision. 1,911 stars ▸ ⁠ > jev-ultrafast a browser agent where Jev decides every click. the text model only wakes up when something actually needs to be written. 16,758 stars ▸ ⁠ > jev-doom-agent someone compiled actual Chocolate Doom to WebAssembly and let Jev make the tactical decisions every frame. yes. Doom. ▸ ⁠ > jev-t-rex-runner remember that stupid Chrome dinosaur? now Jev plays it. jump → duck → run → repeat ▸ ⁠ > typesafe-chess Jev went against a real chess search engine. two games. colors swapped. the search engine won both — and overruled Jev’s first instinct on roughly half the moves. ▸ ⁠ > jev-drone camera → Jev → drone. a simulated quadrotor has to clear five stations while Jev looks at the situation twice a second and decides what happens next. ▸ ⁠ > tax-doc-classifier then someone gave it IRS forms. 261 documents. 100% strict accuracy. roughly $0.001/page. ▸ ⁠ > killmyidea this one is evil lol tell it your startup idea. Jev looks at it from different angles and gives you: KILL / FIX / SHIP ▸ ⁠ > jev-curate throw huge Parquet / JSONL datasets at it. Jev judges 1,500+ rows/sec and keeps only the stuff that passes your rules. ▸ ⁠ > pg-jev and now it’s inside PostgreSQL. ask questions about your own tables in plain English → get the decision back. ▸ ⁠ and this is the weird part: none of these need Jev to write you a beautiful paragraph. they need it to look at a situation and pick: BUY / WAIT CLICK A / CLICK B JUMP / DUCK KILL / FIX / SHIP KEEP / DROP that’s basically the whole Jev idea. LLMs think and write. Jev decides. code does. full setup + my three-question Jev test below

kiosa

67,916 görüntüleme • 1 ay önce

Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open TypeSafe AI , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓
0:15

Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open TypeSafe AI , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓

codila

389,079 görüntüleme • 1 ay önce

GutenOCR: A Grounded Vision-Language Front-End for Documents Fine-tunes Qwen2.5-VL-3B and 7B using a grounded OCR evaluation protocol, substantially improving region- and line-level OCR as well as text-detection recall
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GutenOCR: A Grounded Vision-Language Front-End for Documents Fine-tunes Qwen2.5-VL-3B and 7B using a grounded OCR evaluation protocol, substantially improving region- and line-level OCR as well as text-detection recall

Niels Rogge

45,811 görüntüleme • 9 ay önce

Mistral OCR 4 turned a handwritten calculus exam into clean LaTeX! We gave it a photo of a hand-written exam page. The model read the handwriting and rebuilt every formula into structured digital text Output: Time: 5.1s · Cost: $0.09 Formulas came through exactly right - the hard part was nailed. The graph, unfortunately, it didn’t redraw. But that’s the telling part: most OCR tools just dump the text and quietly drop the figure. OCR 4 caught the plot, boxed it, and tagged it as a chart. It doesn’t get redrawn, but it gets read and accounted for
0:12

Mistral OCR 4 turned a handwritten calculus exam into clean LaTeX! We gave it a photo of a hand-written exam page. The model read the handwriting and rebuilt every formula into structured digital text Output: Time: 5.1s · Cost: $0.09 Formulas came through exactly right - the hard part was nailed. The graph, unfortunately, it didn’t redraw. But that’s the telling part: most OCR tools just dump the text and quietly drop the figure. OCR 4 caught the plot, boxed it, and tagged it as a chart. It doesn’t get redrawn, but it gets read and accounted for

atomic.chat

422,052 görüntüleme • 4 ay önce

Baidu acaba de romper una de las limitaciones más grandes del OCR actual. Unlimited-OCR procesa documentos enteros de una sola pasada, sin chunking. Es el siguiente paso después de DeepSeek-OCR. REPOOO👇
0:14

Baidu acaba de romper una de las limitaciones más grandes del OCR actual. Unlimited-OCR procesa documentos enteros de una sola pasada, sin chunking. Es el siguiente paso después de DeepSeek-OCR. REPOOO👇

Erick

288,203 görüntüleme • 4 ay önce

ByteDance just dropped an OCR model that reads documents just like humans. This 0.3B model analyzes page layout first, then parses elements in parallel. 100% open-source.
0:13

ByteDance just dropped an OCR model that reads documents just like humans. This 0.3B model analyzes page layout first, then parses elements in parallel. 100% open-source.

Unwind AI

101,156 görüntüleme • 1 yıl önce

Also have been playing with TypeSafe AI Jev, insane! So many immediate use cases and new apps are possible. What a time to be a builder! Sharing some experiments here starting with: Keystroke oracle / predictive launcher: Your launcher ranks by aliases, fuzzy match, and habit. Jev reads intent: type "the pdf I just downloaded" and the newest PDF is already the top hit with a full confidence on every keystroke, in ~100 ms
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Also have been playing with TypeSafe AI Jev, insane! So many immediate use cases and new apps are possible. What a time to be a builder! Sharing some experiments here starting with: Keystroke oracle / predictive launcher: Your launcher ranks by aliases, fuzzy match, and habit. Jev reads intent: type "the pdf I just downloaded" and the newest PDF is already the top hit with a full confidence on every keystroke, in ~100 ms

nader dabit

434,529 görüntüleme • 1 ay önce

This is absolutely f*cking insane. you can put a $0.042-per-million-token model in front of Claude and let it decide what Claude ever has to see. that's Jev signal routing, and the trick is stupidly simple: Jev takes the first look at everything. Claude only gets the hard cases. instead of: data -> Claude -> tokens burnt $ it's: data -> Jev -> only the unsure ones -> Claude and here's the part people miss. every Jev answer comes with a probability. that's your router: -> Jev is sure? its answer is final -> Jev hesitates? the case goes up to Claude set the cutoff once, by testing on your own data, not by guessing. 3 repos that already do the routing for you: -> duckdb-jev: route your data. Jev labels every row straight from SQL, up to 1,000 calls in one request -> quicksilver: route your files. Jev checks all 187 files in parallel and Claude gets the 4 that matter. ~2.4k tokens instead of ~26k -> jev-router: route your models. before every message Jev decides simple or hard and sends it to the fast model or the strong one. works with your normal Claude login where this pays off right away: -> support tickets -> your inbox -> lead lists -> messy spreadsheets -> comment moderation on a public benchmark Jev labeled 1,000 rows for $0.015. Claude charged $0.80 for the same job. faster. cheaper. way fewer tokens burned. Jev sorts the mail so the genius only opens what matters. Claude thinks. Jev routes. code does the job.
0:19

This is absolutely f*cking insane. you can put a $0.042-per-million-token model in front of Claude and let it decide what Claude ever has to see. that's Jev signal routing, and the trick is stupidly simple: Jev takes the first look at everything. Claude only gets the hard cases. instead of: data -> Claude -> tokens burnt $ it's: data -> Jev -> only the unsure ones -> Claude and here's the part people miss. every Jev answer comes with a probability. that's your router: -> Jev is sure? its answer is final -> Jev hesitates? the case goes up to Claude set the cutoff once, by testing on your own data, not by guessing. 3 repos that already do the routing for you: -> duckdb-jev: route your data. Jev labels every row straight from SQL, up to 1,000 calls in one request -> quicksilver: route your files. Jev checks all 187 files in parallel and Claude gets the 4 that matter. ~2.4k tokens instead of ~26k -> jev-router: route your models. before every message Jev decides simple or hard and sends it to the fast model or the strong one. works with your normal Claude login where this pays off right away: -> support tickets -> your inbox -> lead lists -> messy spreadsheets -> comment moderation on a public benchmark Jev labeled 1,000 rows for $0.015. Claude charged $0.80 for the same job. faster. cheaper. way fewer tokens burned. Jev sorts the mail so the genius only opens what matters. Claude thinks. Jev routes. code does the job.

kiosa

11,388 görüntüleme • 22 gün önce