LlamaParse

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

Agents like @openclaw are incredibly powerful, as long as the information they receive is clean and structured🦞 When it comes to PDFs and other unstructured documents, most agents struggle. The tool

Agents like @openclaw are incredibly powerful, as long as the information they receive is clean and structured🦞 When it comes to PDFs and other unstructured documents, most agents struggle. The tools they rely on often return only raw text, losing critical context like layout, tables, and images❌ That’s why we created LlamaParse and LiteParse Agent Skills, designed to give agents access to a deeper layer of document understanding, enabling more reliable knowledge extraction and automation across complex documents📝 📚Learn more about the problem, and how the skills solve it: https://t.co/dn33HE6Z0k 🦙 Get started with LlamaParse: https://t.co/ADG9CPTcAV

Layman: Agentic Insight and Oversight (same same but different)

What's the most common duplicate project on r/ClaudeAI? Usage trackers. What's the second most common? AI Monitors. Does Layman do those things? Yes, of course. So what makes it different? Layman's Dashboard, Flowchart, and Logs view (with Layman's Terms and Analysis examples) Like many similar tools, Layman runs as a web service in a container on your local machine. It installs hooks and accesses harness logs to "look over your shoulder," then leverages a secondary AI instance to help keep your multiple sessions, sub-agents, and alternate harnesses in line. So, short answer: Drift Monitoring. Repeatedly named as one of the most frustrating issues for heavy Claude Code users, Layman takes into account all user prompts issued to CC as well as current project and global CLAUDE.md instructions, and at configurable intervals scores the current degree of "drift" occurring from your goals and the rules you have established. You can optionally receive warning notifications or place a block when different thresholds are reached. Risk Analysis. Layman will classify all tool calls and operations with a "risk" level based on simple, consistent criteria (such as read-only, writing, modifying, network access, deletion, etc.) and can automatically analyze the AI agent's current intended action, the overall goal or purpose behind that intention, and summarize the safety and security implications at stake. Layman's Terms. The eponymous origin of the tool, offering a plain-language (and if possible non-technical) explanation of the purpose of any given tool call. It can summarize what was performed at the session level as well, helpful for later recall and understanding after some time has passed. Vibe coders aside, should a professional developer already have knowledge of what their tools are doing before they grant permission? Yes, of course, but when you are operating at scale and (say) that TypeScript project you are polishing needs to look up some JSON value and your AI agent writes a one-off Python script to parse it out, it can be helpful to have an "extra pair of eyes" taking a look before you effectively begin yet-another code review. Meanwhile, typical features you might come to expect are included, from Session Recording (opt-in is required first for data tracking and there is no telemetry to worry about), Bookmarking, and Search, PII filtering (including PATs and API keys), File and URL access tracking, and a handy Setup Wizard for helping get those hooks installed in the first place and walking you through configuration of core capabilities. Did I mention besides Claude Code it supports Codex, OpenCode, Mistral Vibe, and Cline (with more to come)? Whether using these for local agents or as an alternative when hitting session limits, Layman can monitor and track them all at once. But wait, doesn't a "secondary AI instance" just end up wasting tokens? My Precious? (erm...) Our precious, precious tokens? When session limits already hit so hard? It turns out these algorithms do not require nearly the level of "intelligence" you might desire for your planning and coding sessions themselves. Personally I keep an instance of Qwen3-Coder-Next running locally via llama.cpp server on my system's GPU to field those calls, with no discernible impact on system performance. And when a local LLM is not available, Haiku does the job excellently (now you have a reason to use it). You absolutely do not need to use anything more resource-intensive to get the job done. Now you have a complete picture. GitHub repository: https://github.com/castellotti/layman License: MIT submitted by /u/jigsaw-studio [link] [comments]

Common Failure Modes Break VLM-Powered OCR in Production. 🔁 Repetition Loops — model spirals into infinite whitespace, exhausts resources, cascades latency across your system 🛑 Recitation Errors

Common Failure Modes Break VLM-Powered OCR in Production. 🔁 Repetition Loops — model spirals into infinite whitespace, exhausts resources, cascades latency across your system 🛑 Recitation Errors — safety filters hard-stop legitimate extractions as "copyright violations" Same pipeline. Completely different root causes. Completely different fixes. Our enginerring leadership broke down what went wrong and how we solved both 👇 https://t.co/fFkLmnG11h

Visually rich documents are especially challenging for agents. Tables, charts, and images often break traditional document pipelines, making complex reasoning difficult📄 So we teamed up with @lanced

Visually rich documents are especially challenging for agents. Tables, charts, and images often break traditional document pipelines, making complex reasoning difficult📄 So we teamed up with @lancedb to build a structure-aware PDF QA pipeline🚀 Here’s how it works: 1. LiteParse extracts structured text and captures page screenshots📸 2. We embed the text with Gemini 2 Embedding⚙️ 3. Text, vectors, and images are stored in LanceDB🗄️ 4. A Claude agent retrieves the relevant context and, if text isn’t enough, it falls back to image-based reasoning on the screenshots🧠 In our evaluations, the agent achieved near-perfect scores across most tasks, showing how strong parsing (LiteParse) plus multimodal storage (LanceDB) can significantly improve agentic search pipelines📈 📚 Full breakdown: https://t.co/k3swCwPmme 🦙 Learn more about LiteParse: https://t.co/lHZWj9hhl1

Open call to fintech leaders in NYC 🏦 May 13, in-person workshop with @jerryjliu0 on turning complex financial docs into LLM-ready data using agentic OCR. Build real pipelines. Hear from a Top 5 PE f

Open call to fintech leaders in NYC 🏦 May 13, in-person workshop with @jerryjliu0 on turning complex financial docs into LLM-ready data using agentic OCR. Build real pipelines. Hear from a Top 5 PE firm's production agent. Make sure to bring your laptops→ https://t.co/j6nBDaaDqo

[P] GPU friendly lossless 12-bit BF16 format with 0.03% escape rate and 1 integer ADD decode works for AMD & NVIDIA

Hi everyone, I am from Australia : ) I just released a new research prototype It’s a lossless BF16 compression format that stores weights in 12 bits by replacing the 8-bit exponent with a 4-bit group code. For 99.97% of weights, decoding is just one integer ADD. Byte-aligned split storage: true 12-bit per weight, no 16-bit padding waste, and zero HBM read amplification. Yes 12 bit not 11 bit !! The main idea was not just “compress weights more”, but to make the format GPU-friendly enough to use directly during inference: sign + mantissa: exactly 1 byte per element group: two nibbles packed into exactly 1 byte too https://preview.redd.it/qbx94xeeo2tg1.png?width=1536&format=png&auto=webp&s=831da49f6b1729bd0a0e2d1f075786274e5a7398 1.33x smaller than BF16 Fixed-rate 12-bit per weight, no entropy coding Zero precision loss bit-perfect reconstruction Fused decode + matmul, so there is effectively no separate decompression stage Byte-aligned storage, no LUT, no bitstream parsing Works on both NVIDIA and AMD Some results so far: Single-user (B=1), RTX 5070 Ti Llama 2 7B: 64.7 tok/s (1.47x vs vLLM) Mistral 7B: 60.0 tok/s (1.10x vs vLLM) Llama 3.1 8B: 57.0 tok/s (vLLM OOM on 16 GB) Multi-user (B=256), total tok/s Llama 2 7B: 2931 vs 1086 in vLLM (2.70x) Mistral 7B: 2554 vs 872 in vLLM (2.93x) It also seems surprisingly stable across model types: Llama 3.1 405B: 0.034% escape rate Mixtral 8x7B: 0.050% SDXL UNet: 0.233% CogVideoX 2B: 0.128% So far this is tested on BF16 safetensors only. Repo: https://github.com/cenconq25/Turbo-Lossless Also worth noting: the V3 fused decode+GEMM kernel uses tensor-core patterns inspired by ZipServ / ZipGEMM (Fan et al., ASPLOS 2026). Happy to hear criticism, edge cases, or reasons this idea won’t scale. Thanks for your time : ) submitted by /u/Embarrassed_Will_120 [link] [comments]

We took a brief break from parsing PDFs this First Thursday and welcomed the AI community to "Series B Lane" in San Francisco 🦙 New office. A-parse-rol Spritzes. LlamaIsland Iced Teas. 100+ builders

We took a brief break from parsing PDFs this First Thursday and welcomed the AI community to "Series B Lane" in San Francisco 🦙 New office. A-parse-rol Spritzes. LlamaIsland Iced Teas. 100+ builders. Then everyone walked one block to catch Reggie Watts at SF's street fest. More of this coming soon 🎥⬇️

After the release of Parse v2, Extract is also getting an upgrade — 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝘃𝟮! 🎉 We've been reworking the experience from the ground up to make document extraction m

After the release of Parse v2, Extract is also getting an upgrade — 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝘃𝟮! 🎉 We've been reworking the experience from the ground up to make document extraction more powerful and easier to use than ever. Here's what's new: ✦ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝘁𝗶𝗲𝗿𝘀: we've replaced modes with cleaner, more intuitive tiers. (And stay tuned: agentic plus is coming to Extract too, very soon.) ✦ 𝗣𝗿𝗲-𝘀𝗮𝘃𝗲𝗱 𝗲𝘅𝘁𝗿𝗮𝗰𝘁 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀: load your saved extraction configs directly, so you can skip the setup and get straight to extracting. ✦ 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝗯𝗹𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗽𝗮𝗿𝘀𝗶𝗻𝗴: now you can control how your documents get parsed before extraction, giving you more flexibility and better results end to end. And for those who need a transition period: Extract v1 will remain accessible via the UI under 'Settings → General' for a limited time. Try Extract v2 today → https://t.co/yPVJzqoKal

Lawyers <3 documents We're proud to sponsor @StanfordLaw and @CodeXStanford's FutureLaw Week 2026! 🏛️⚖️ AI x Law bootcamps, hackathons, the UN AI For Good Law Track & the FutureLaw Conference — a

Lawyers <3 documents We're proud to sponsor @StanfordLaw and @CodeXStanford's FutureLaw Week 2026! 🏛️⚖️ AI x Law bootcamps, hackathons, the UN AI For Good Law Track & the FutureLaw Conference — all exploring the future of legal AI. Join us alongside friends from @DLA_Piper, @normativeai, @filevine, @harvey, @LexisNexis & the global legal tech community. April 11–16 👉 https://t.co/9MFWAn46ti

LlamaIndex is proud to be named to the 2026 Enterprise Tech 30, #3 in the Early Stage category. The ET30 is an annual list by @Wing_VC and Eric Newcomer, voted on by 90+ leading investors and corpora

LlamaIndex is proud to be named to the 2026 Enterprise Tech 30, #3 in the Early Stage category. The ET30 is an annual list by @Wing_VC and Eric Newcomer, voted on by 90+ leading investors and corporate development leaders. It recognizes the private companies wi th the most potential to shape the future of enterprise technology. Thank you to Wing Venture Capital and Eric Newcomer, and congratulations to all the companies honored this year.

👉 Signup to LlamaParse to try it out: https://t.co/QQzVCOwiVl

👉 Signup to LlamaParse to try it out: https://t.co/QQzVCOwiVl

Transform your document processing with intelligent table extraction that goes beyond basic OCR. Tables in PDFs aren't just text - they're structured data trapped in visual formats. Our new deep dive

Transform your document processing with intelligent table extraction that goes beyond basic OCR. Tables in PDFs aren't just text - they're structured data trapped in visual formats. Our new deep dive explains how modern OCR for tables reconstructs spatial relationships, preserves header hierarchies, and ensures data integrity across complex documents. 📊 Why table extraction is fundamentally harder than standard text OCR - spatial relationships matter more than character recognition 🔧 The three core phases: detection, structure recognition, and data extraction with validation 💼 Real-world applications across financial services, healthcare, and logistics - from invoice processing to lab results ⚡ How LlamaParse handles multi-line rows, merged cells, and borderless tables while maintaining logical consistency We show a complete invoice processing example where complex line-item tables get converted to clean JSON with preserved relationships and validated totals - ready for immediate ERP integration. Read the complete guide: https://t.co/NjUf30AeC7

🚀 The @GoogleDeepMind team just added Gemini 3.1 to the Live API, so we built a small demo showing how Gemini voice agents can plug directly into the document processing ecosystem powered by LlamaInd

🚀 The @GoogleDeepMind team just added Gemini 3.1 to the Live API, so we built a small demo showing how Gemini voice agents can plug directly into the document processing ecosystem powered by LlamaIndex. 🔥 In this example, we integrate LiteParse to enable fast, fully-local document parsing. With our TUI-based voice assistant, you can literally talk to your terminal: - Speak commands - Trigger live document parsing via tool calls - Hear the agent read back results in real time 🔊 The assistant can extract content from single files or entire folders, leveraging the lightning-fast local parsing that LiteParse provides ⚡ Take a look at the demo👇 👩‍💻 GitHub repo https://t.co/ySmenP2HoY 📚 LiteParse docs https://t.co/NlpoI4CqEq

Bounding boxes are key for citations, and we just shipped a new guide showing how to use LiteParse for visual citations! LiteParse is our fast and open-source document parser. Using both bounding box

Bounding boxes are key for citations, and we just shipped a new guide showing how to use LiteParse for visual citations! LiteParse is our fast and open-source document parser. Using both bounding box extraction and page screenshots, anyone (including agents) can learn how to associate text with an element on the page. https://t.co/Vauhx5Yh9n

👉 Signup to LlamaParse to try it out: https://t.co/QQzVCOwiVl

👉 Signup to LlamaParse to try it out: https://t.co/QQzVCOwiVl