We’re proud to welcome @GeneralistAI to the @NorwestVP portfolio! The company is on a mission to make general-purpose robots a reality and is led by a world-class team in @peteflorence and @andyzengineer and Andrew Barry bloomberg.com/news/articles/…

We Parse PDFs We spent 7 figures to put this on billboards throughout SF. I thought long and hard about putting something more creative and whimsical. But then you wouldn’t know what we do. AI agents (and humans) are consuming exponentially more documents as they do real work. They need the best quality document parser to not output garbage on downstream tasks. This is what we do today as a company. If you have any PDFs (or other documents), we parse them :) If you’re around SF in June for one of the following events, come stop by our booths: ✅ Snowflake Summit (this week, Booth 1123) ✅ Databricks Data AI Summit (June 15-18, Booth 137) ✅ AI Engineer World Fair(June 29-July 2, Booth L-G47) You can find us by the same sign we put on our billboards! We Parse PDFs @llama_index

Invest and sign a definitive agreement in the same quarter? Well, this is a new one! Congrats to @pratyus and the @natomalabs team on entering into a definitive agreement with @Snowflake. The demand for agentic AI infrastructure is moving fast. natoma.ai/blog/natoma-snowfl…

If you're stopping by the SF Caltrain station over Memorial Day weekend, you might catch a glimpse of our digital ads 📺 We parse (PDFs) (50 other document types)

Turso now includes unlimited active databases in every plan. We already had unlimited databases, but we would charge you based on how many of them were active. That is now gone. You want a database, you get a database.

This is why we released liteparse :) Free, open-source, designed for agents. Natively supports OCR / screenshotting for deeper visual understanding in a document when needed.

We’re open sourcing the first document OCR benchmark for the agentic era, ParseBench. Document parsing is the foundation of every AI agent that works with real-world files. ParseBench is a benchmark that measures parsing quality specifically for agent knowledge work: ✅ It optimizes for semantic correctness (instead of exact similarity) ✅ It has the most comprehensive distribution of real-world enterprise documents It contains ~2,000 human-verified enterprise document pages with 167,000 test rules across five dimensions that matter most: tables, charts, content faithfulness, semantic formatting, and visual grounding. We benchmarked 14 known document parsers on ParseBench, from frontier/OSS VLMs to specialized parsers to LlamaParse. Here are some of our findings: 💡 Increasing compute budget yields diminishing returns - Gemini/gpt-5-mini/haiku gain 3-5 points from minimal to high thinking, at 4x the cost. 💡 Charts are the most polarizing dimension for evaluation. Most specialized parsers score below 6%, while some VLM-based parsers do a bit better. 💡 VLMs are great at visual understanding but terrible at layout extraction. GPT-5-mini/haiku score below 10% on our visual grounding task, all specialized parsers do much better. 💡 No method crushes all 5 dimensions at once, but LlamaParse achieves the highest overall score at 84.9%, and is the leader in 4 out of the 5 dimensions. This is by far the deepest technical work that we’ve published as a company. I would encourage you to start with our blog and explore our links to Hugging Face to GitHub. All the details are in our full 35-page (!!) ArXiv whitepaper. 🌐: Blog: llamaindex.ai/blog/parsebenc… 📄 Paper: arxiv.org/abs/2604.08538?utm… 💻 Code: github.com/run-llama/ParseBe… 📊 Dataset: huggingface.co/datasets/llam… 🎥 YouTube: youtube.com/watch?v=g5p7G-Nw…

We're excited to collaborate with @googledevs on building an agentic workflow over complex financial documents - using LlamaParse and Gemini 3.1 Pro Brokerage statements have complex layouts, dense tables, and oftentimes visual elements like charts. Our multi-step agentic workflow does the following: 1. Ingest PDF into LlamaParse 2. Extract text and tables 3. Generate human-readable summary using Gemini Shoutout to @Vish_ow and @itsclelia 🙌 Check it out: developers.googleblog.com/bu…

We’re proud to share that @OuroMeds , a Norwest portfolio company, has signed a definitive agreement to be acquired by @GileadSciences. When we co-led Ouro Medicine’s Series A in 2024, we deeply believed in its mission to fundamentally change how chronic immune-mediated diseases are treated. Congratulations on this significant milestone, and we look forward to supporting the company in its next chapter with Gilead. Read more: gilead.com/news/news-details…

The DOJ messed up some redactions on the latest Epstein files 🗄️🔏 - they didn’t flatten the PDF layers and you can highlight/copy the underlying text. If you want to extract this text at scale, you *can’t* just feed everything to a VLM (gpt-5.2, sonnet-4.5, gemini 3). VLMs only look at the top-level visual layer of the page, and will output the redacted blocks. You need to also reconstruct the text from the PDF binary itself, which is more in line with “traditional” techniques. LlamaParse uses a combination of both VLMs along with reading the underlying binary. * If you try out our agentic mode by default, it will output the redacted blocks in the markdown `md` field, but extract out the full text in the `text` field * With a simple prompt change you can also extract out the full text in `md`. Prompt: "Do not output redactions if the underlying extracted text already exists - output the full extracted text instead" Whether you want to comb through any set of released government documents or any other file, come check out LlamaParse! Source reddit thread: reddit.com/r/Epstein/s/ax3we… File: justice.gov/multimedia/Court… To use LlamaParse, sign up to LlamaCloud: cloud.llamaindex.ai/

Congratulations to @NorwestVP portfolio company @tv_scientific as they are joining @Pinterest

Huge Congratulations to @thakurtarun and @vezainc as @ServiceNow announces intent to acquire. Deeply thankful for allowing @NorwestVP to be a part of the journey from very early on. businesswire.com/news/home/2…

Claude Code over Excel 🤖📊 Claude already 'works' over Excel, but in a naive manner - it writes raw python/openpyxl to analyze an Excel sheet cell-by-cell and generally lacks a semantic understanding of the content. Basically the coding abstractions used are too low-level to have the coding agent accurately do more sophisticated analysis. Our new LlamaSheets API lets you automatically segment structure complex Excel sheets into well-formatted 2D tables. This both gives Claude Code immediate semantic awareness of the sheet, and allows it to run Pandas/SQL over well-structured dataframes. We've written a guide showing you how specifically to use LlamaSheets with coding agents! Guide: developers.llamaindex.ai/pyt… Sign up to LlamaCloud: cloud.llamaindex.ai/

You might’ve known us as a “RAG framework” company - but we’ve been a best-in-class, agentic document OCR/workflow company for the past 1.5 years! 📑🤖 We’re building the future of knowledge work over documents. Our website is awesome - check it out if you haven’t already 👇 llamaindex.ai/

Drop the mic @tursodatabase

Let’s go @tursodatabase

TURSO LAUNCH PARTY IS OCTOBER 8 🎉 We're hosting a Launch Party in San Francisco on October 8 to celebrate the Turso Beta Launch. Join us in-person! RSVP at tur.so/soma More details to follow.

Well said @jerryjliu0 and snazzy new website too!

Turso 0.1.4 alpha is now out! Highlights: 🔄 Initial bidirectional sync support 📦 TypeScript/JavaScript SDK improvements 🗄️ MCP server for accessing databases ⚡ Performance improvements and more Full release notes 👇

Turso, the next evolution of SQLite (previously codenamed "Limbo") reaches its first alpha milestone and first official release. We are so confident in our foundations, that we are offering you $1,000 if you find any data corruption bugs through our partnership with @algoraio (more details below) We understand that you love SQLite because of its legendary reliability. Which is why we wrote Turso with two layers of Deterministic Simulation Testing to generate the most improbable combinations of corner cases and surface every bug: our own, built-in to the project, and our partnership with @AntithesisHQ that catches every bug that survives the simulator. Including bugs in the simulator itself. We are rewriting SQLite because we believe that despite its incredible track record, SQLite could be more. And the secret to unlocking this is an open community of contributors that can unleash the true power of Open Source. In the months leading up to the alpha, Turso has already counted more than 120 contributors. And we are hoping you are next. For the upcoming beta, work has already started on features like concurrent writes, better schema changes, and change data capture. All features we constantly hear from people are sorely needed in SQLite. Performance was not the focus of our alpha. Yet, some workloads already perform a lot better on Turso. Which is why companies like @spice_ai are reaching for Turso as a replacement for SQLite to accelerate AI workloads. Lastly, we are testing Turso, A LOT. It would be a lot harder to do this without the high performance CI infrastructure that @useblacksmith provides. Learn more about Turso 👇