Search: “jerry-liu--x”

Our 'agentic plus' extractor in LlamaParse is great for extracting out massive volumes of fields (e.g. 10k-100k+ fields) from long documents (100-500 pages) We tested with a doc that contains a giant

We think a lot about the distribution of all real-world documents, particularly for real-world use cases. We think about the distribution in terms of page length, field length, domain, and task type.

Murtaza Khomusi posted a tweet (12:58 AM UTC, Aug 14 2026) announcing that he had never before added a “kitschy discount sticker” beside a promotional tier on the LlamaIndex pricing page. The tweet in

There's a lot of real-world documents that are scanned, rotated, handwritten, or some combination of any of these elements. This week we created a comprehensive document extraction benchmark that cont

Every document extraction system has a perception blind spot. We mapped them. For ExtractBench, we tested 14 systems on documents that weren't born digital: 1950s regulatory filings, hand-filled tax f

🦙☀️ It’s a hot Llama summer, and it pays to be a PRO at document parsing. For a limited time, upgrade to the LlamaParse Pro plan and we'll triple your credits: 400K → 1.2M credits in your first month.

Our UK reps didn’t remember to smile 😂 We recently opened a @llama_index office in London 🇬🇧. It is three people strong (Frank, Max, Joe), with two more people starting soon. It also has a beautiful v

This week we launched the world's most accurate document extraction agent over real-world documents. Introducing LlamaExtract Agentic Plus 💫 . It is a complete document extraction model+harness engine

A 36‑page ArXiv whitepaper titled **ExtractBench** was released by Jerry Liu (jerryjliu0), describing a large‑scale, schema‑guided benchmark for real‑world document extraction from complex enterprise

The most dangerous document extraction failure isn't a wrong value. It's a missing row that looks like nothing is wrong. We released ExtractBench yesterday: 370 enterprise docs, 14 systems. The hardes

We wrote a 36-page ArXiv whitepaper on ExtractBench 🧑‍🔬 , our effort to create the most comprehensive, schema-guided, real-world document extraction benchmark. It’s extremely detailed and covers every

ExtractBench is one of the most comprehensive benchmarks for real-world document extraction. ✅ It covers 4869 pages, across 67 document types, spanning 8 real-world domains: finance, energy, gov, auto

Introducing ExtractBench, the most comprehensive benchmark for information extraction from complex enterprise documents. The latest models are pushing the frontier of coding and knowledge work, but su

Introducing 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗕𝗲𝗻𝗰𝗵: the most comprehensive benchmark for information extraction from complex enterprise documents. Our applied research team tested: 14 systems — frontier VLMs, coding agents, ex

A downside with VLM-based parsing is that they’re generally slower than text-based heuristic approaches. As a result they add latency to any ad-hoc file processing *in-the agent loop* (e.g. if you upl

是我见过OCR效果最好的一个了，人眼都难以分辨的，它能处理的十分准确，而且速度非常的快。llamaindex 真不愧是文档解析界的一哥啊。 The best 'raw' frontier model for document parsing is gemini 3 flash, but the issue is that since then the flash models have gotte

Simon @disiok proposed a novel IQ test for language models, asking whether an LLM becomes smarter or dumber after being fine‑tuned on a user’s stream of consciousness. The tweet was posted on 9 Aug 20

The best 'raw' frontier model for document parsing is gemini 3 flash, but the issue is that since then the flash models have gotten 3x more expensive while flatlining on visual recognition across comp

The future of FDE work seems closely related with all work around evals/posttraining/RL envs. FDEs are effectively responsible for the following: 1. Define the business problem. 2. Codify the business

We're not Palantir, but we do think a lot about evals and hillclimbing w.r.t. document processing. If you have really hairy problems around large-scale extraction over complex, real-world document cor

LiteParse can now extract structured data from your PDF in milliseconds: ✅ checkbox states ✅ annotations ✅ vector graphics ✅ word-level bounding boxes It is the most comprehensive, accurate (and fast)

We've built a new feature in LlamaParse that lets you automatically extract any complex form into a structured JSON output 📋🤖 The best part is there's no schema needed! We will systematically detect a

Fully onboard with productizing hillclimbing as an automated service for any agentic task. I've had the fortune of knowing @silennai since the AutoGPT days, and I know that him and Kion are going to d

Document OCR is not Getting Commoditized (by Frontier Models) The most common question I get is whether frontier models are going to eat all document processing solutions - just screenshot the page an

'OCR is just a feature now. Frontier models will eat it.' We hear this constantly. The data says otherwise. Across three GPT generations, parsing accuracy gained ~24 points, while cost per page 4x'd.

The tweet is a repost of Sarah Chieng’s “ggp run” experiment where people recite names of AI‑native companies in alphabetical order, making time feel like it passes faster. The list presented alphabet

Parsing a W-2 into markdown was always the easy part. Getting the fields out was a second pipeline: define a schema, map the fields, handle the edge cases. Set processing_options.forms='𝗲𝗻𝗿𝗶𝗰𝗵', and L

OpenAI’s entire growth loop: new models, price cuts, and Tibo’s token resets major price cuts today: *80% drop for GPT-5.6 Luna, now 0.20 per million input tokens and 1.20 per million output *20% drop

On July 31, 2026 at 01:40 AM, Twitter user Murtaza Khomusi posted a “throwback” tweet reminiscing about opening his first iPhone in 2007 (“2k7 vibez”) and questioned whether this marked the start of t

We created a document OCR router that can estimate the complexity of every single page and parse it with the relevant mode 💫 * Some pages are full of native text, which can be directly handled with Li

We have an idea for dinner 2 already :) But what ideas are you all interested in? - technical topics like rl envs, continual learning, cloud agents, world models - general startup / company building f

Introducing the Parse Gateway There's been an explosion of interest in model routing - you don't always need the best model for every task. That is especially true for document parsing 📄🔀: - Some page

Not every page in your PDF needs the same treatment. A scanned cover, a dense table, a clean text page, a figure-heavy diagram.... most parsing pipelines throw all of them at the same parser, forcing

We’re excited to rollout an official batch parsing experience to LlamaParse. ✅ Instead of hitting our APIs one file at a time, create a batch of 10k at once. ✅ Get a dedicated UI where you can audit t

Yesterday I cohosted a dinner with @dexhorthy with a wonderful group of founders, to talk about agent loops and loop engineering. Some interesting insights: * Most of our group was *not* actively usin

Batch parsing used to mean writing API scripts. Now it's a button. 🦙 Parse or extract across up to 10,000 files in one run, straight from the LlamaParse UI. Point it at a folder and go — no code requi

Sakana is at the frontier of having fun and I respect that We are excited to share our latest work, together with @nyuniversity: 'Dream-Cubed: Controllable Generative Modeling in Minecraft by Training

If I only went off X posts, I'd think Ramp was an AI lab We’re open-sourcing PorTAL, our framework for shared task representations and cross model LoRA adaptation. It now spans from hybrid attention m

This part is spot on: > 'Overall, we found that we were over-constraining Claude Code...while these constraints were once needed to avoid worst case scenarios, we have since found we can delete many o

Title: “don’t do this” refers to a conversation on Twitter where Jerry Liu warns against a particular action, while Julian Schrittwieser comments on a separate thread expressing excitement that Jensen

The Opus 5 system card itself is a fun PDF to parse. It's 193 pages and stacked with labeled and unlabeled charts 📊 LlamaParse does a surprisingly good job on agentic (1.25c per page) and agentic plus

Am I supposed to interpret that it's better than Fable 5 from the benchmarks? Or it's ~close but cheaper? Introducing Claude Opus 5. It's a thoughtful and proactive model that comes close to the front

An interesting thing I'm observing from the blog/system card is that on a good chunk of the reported benchmarks (~20-30% from a skim), Opus 5 max thinking leads to a degradation in performance compare

We benchmarked Opus 5 comprehensively on document understanding through ParseBench. It is roughly on par with Opus 4.8 - it does a few points worse on dense tables, but does slightly better on parsing

LlamaParse can't solve a conjecture, but it can parse this as a PDF 🫡 Dinitz-Garg-Goemans conjecture is false. This graph theory problem was open for ~30 years. The graph below has fractional flow cos

You asked, we delivered! 🚀 Expanding our developer toolkit with: 🐹 Go SDK 🏢 Java SDK >_ CLI Parse a document from Go or Java in a few lines, or straight from your terminal. 🦙 Grab an API key and start