Search: “jerry-liu--x”

Gemini 3.8 Flash might be maxxed on other benchmarks, but not quite on document parsing. We benchmarked it on ParseBench. It does slightly better on tables + content faithfulness compared to Gemini 3.

Jerry Liu expressed that he is awaiting access to GPT‑6 “Astra” while subscribed to ChatGPT Pro. According to OpenAI’s tweet, GPT‑6 Astra aims to replicate any computer task and deliver results quickl

Unlike other players in the OCR space, we don't block our competitors from accessing our platform. We're also fully committed towards open, reproducible benchmarks for document parsing and extraction

We've massively improved our document parsing capabilities across the board in the past ~3 months. Our LlamaParse cost-effective mode is one of the cheapest OCR solutions out there at <0.4c per page,

Check out the blog here: https://www.llamaindex.ai/blog/introducing-turbo-our-fastest-extraction-tier If you're interested in trying it out, come sign up for a LlamaParse account: https://cloud.llamai

Document extraction just got a new gear. ⚡️ Introducing Turbo mode for Extract, our fastest way yet to pull structured data from documents. It runs roughly 4× faster than our Cost Effective Tier at co

I trust these vibe checks more than I trust most of the benchmark results GPT-6 astra seems more powerful for e2e automation and better at writing (a massively underrated gap for these models). But it

We've benchmarked all the latest frontier models on hard document extraction tasks (with Fable 5.1 and Gemini 3.8 Flash coming soon!). ExtractBench is now live on @kaggle . 5.6 Sol leads the pack, fol

ExtractBench is now live on @Kaggle. It tests schema-guided document extraction on the documents most likely to break downstream agents and workflows, including long record lists, noisy scans, handwri

Fable 5.1 didn't come with document OCR benchmarks, so we benchmarked it on ParseBench: a comprehensive parsing benchmark that's comprised of 2k+ real-world documents across finance, legal, insurance,

Most document OCR solutions have parse (doc->markdown) and extract (doc + schema -> structured output) endpoints. Sometimes you want to extract information from a semi-structured document like a form,

SO excited to share my next chapter: i’m joining @llama_index to lead all things growth marketing, brand, and community! 🦙🩷 when i came along them, it clicked incredibly fast: a team building the solu

There’s generally a massive difference in quality between specialized OCR providers, “simple” open-weight OCR models, and free/OSS solutions. 1️⃣ Specialized OCR models (including LlamaParse) solve fo

LlamaParse is officially a verified connector on Claude! 🦙 Claude is perfectly fine for the occasional text-only PDF. But most of the context your agent needs lives in messy PDFs, spreadsheets, scanne

My conversation with @jerryjliu0. He's building @llama_index: document infrastructure for AI agents, the layer underneath everything models need to work. Did you know he's the guy whose tool half the

Thanks @juliafedorin for a great conversation! Some tidbits from the podcast on the AI space: - We've pivoted from building a RAG framework towards building the document infrastructure for agents - Do

We’re excited to launch the official LlamaParse connector on Claude! Yes Claude can already read PDFs, and it probably works fine for relatively simple queries over relatively simple docs. But if you

at the end of the day, we all have to stand by our company and the values it represents 🙂 * frontier labs will want to own their own application/harness/model e2e. or at least, openai will to start wi

Agentic spreadsheet extraction 📊 is hard to execute well from a price/performance standpoint because your agent needs to ingest a massive quantity of input tokens from a highly complex interface (e.g.

Obligatory plug for static embeddings. Are you using static embeddings in your projects? Would love to hear how people are leveraging them, whether for retrieval and beyond. Static embeddings offer un

PDF parsing is fun because there's an infinite variety of enteprise documents 📑. For each document category, there's a long tail of work to build more precise bounding boxes, confidence scores, and do

static embeddings are extremely fast/cheap compared to dense embeddings, and still quite powerful. one issue is that they lose accuracy over longer text, since the tokens get pooled and averaged late

Most extraction tools treat spreadsheets like PDFs. They flatten the file into text or markdown, then ask a model to infer the original structure. But spreadsheets depend on structure. Headers, formul

We've introduced native, agentic spreadsheet extraction 📊 into LlamaParse. Spreadsheets are a wildly different format from PDFs (or any other document format). They can span arbitrarily many rows/colu

Another special guest is hitting the stage at #FullyConnected26! @jerryjliu0 built @llama_index, the framework thousands of teams use to connect LLMs to their data. Come see Jerry and many more Sept 2

Big shoutout to @perplexity_ai for properly benchmarking document understanding in their Portable Computer release 🔥 They took a subset of our ParseBench benchmark (https://www.parsebench.ai/) and mea

Hosting the 2nd founder dinner in sf on August 31 with @GuangyuRobert at @Fundamental - the team behind @tryshortcutai Agents are increasingly capable of solving tasks for longer task horizons without

I'm actually curious if it's that scary to be in the #1 camp * I still think humans will use software instead only doing work through agent apps. So humans will continue to use SaaS through the UI and

Perplexity.ai published a blog post titled “A Local‑First Agent for Private and Cost‑Effective Knowledge Work” on August 26 2026. The article describes the company’s new local‑first AI agent, which pr

We benchmarked 20+ open-weight models on easy-to-hard document extraction tasks through ExtractBench. The results are all available on @huggingface 🤗 ExtractBench is a schema-guided extraction benchma

We built one of the most comprehensive benchmarks for document extraction, and evaluated it across a lot of different systems: ✅ one-shot frontier VLMs ✅ frontier VLMs + coding agent harnesses ✅ one-s

not sure i fully get this. - i want my agents to use slack - i don't want my agents to build a new slack - i don't want to use slack's agent i think every piece of software/system of record needs to b

This summer, we brought in two first-year Waterloo interns (‘rising sophomores’ in 🇺🇸) and tasked them with shipping complex production features that help push the frontier of our document processing

Coming to Connected Stack in SF — @jerryjliu0, CEO & Co-founder of @llama_index, nominated by @GreylockVC. LlamaIndex is building document infrastructure for AI agents, with LlamaParse purpose-built f

Our 2nd founder dinner in SF co-hosted by @jerryjliu0 and @GuangyuRobert at @Fundamental - the team behind @tryshortcutai Talking about existing moats in the AI era. Frontier labs are moving past mode

the difference in productivity between 0-1 mode vs. working within a big team at a large co seems universally true even pre-chatgpt. it's really easy to be 'productive' when (a) you're the only person

The latest RAG trend for the current agent harnesses (Codex, Cowork) is to do two passes of document processing to solve a knowledge work task over a data room of documents: 1️⃣ A fast and light pass,

One of the interesting properties we’ve observed around schema-guided, complex document extraction tasks 📑 is that coding agent harnesses are good baselines (in terms of cost/accuracy) for longer docu

saas isn't dead, it just has to be repurposed (+ remonetized) for agent consumption So many vendors are NOT getting this I have one or two agents I use and like. For anyone else: give me an MCP interf

we're offering exclusive startup credits and discounts to Tampa Bay Wave startups for pdf parsing come sign up here 🙂 https://www.llamaindex.ai/startups TIME Magazine’s list of “best” accelerators is

We built revision tracking in LlamaParse ✍️ You can now extract Word-style tracked changes and reviewer comments as additional structured metadata alongside the parsed markdown content. This lets you

If you’re building an “agent over your PDFs” in 2026, one of the most underrated things you can do is to provide exact grounding back to the source document for any agentic answers. Specifically, show

Most parsers treat tracked changes as noise. Result: a deleted clause comes back as live text, and your pipeline reads a document that says the opposite of what it means. LlamaParse now handles revisi

We tuned an AI agent that can do large-scale document extraction from long docs (50+ pages, some with 10k-100k fields) with 94%+ accuracy 📈 It uses a harness + model set that is tuned specifically for

It makes sense to optimize model routing at the harness layer instead of the gateway layer if you want to hillclimb on accuracy/cost for any e2e task. Every task is solved by a combination of a model

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