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🚀 New in Heptabase: PDF Parser - Extract text, tables, equations & images (even from scanned PDFs) → save as clean Markdown - Ask AI with precise context (page ranges & paragraphs) → responses include link refs back to the source - MAX mode ensures AI reads every word...

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36,400 просмотров • 10 месяцев назад •via X (Twitter)

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We’ve launched Web Tab in Heptabase! You can now search Google, YouTube, or open any website directly inside Heptabase — take notes side by side, see AI-suggested related cards, or even chat with the page you’re browsing.
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We’ve launched Web Tab in Heptabase! You can now search Google, YouTube, or open any website directly inside Heptabase — take notes side by side, see AI-suggested related cards, or even chat with the page you’re browsing.

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If you open our in-app browsing tool using the split panel, you can now drag text from a web page onto the whiteboard to create new text elements.
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If you open our in-app browsing tool using the split panel, you can now drag text from a web page onto the whiteboard to create new text elements.

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PDF parsing is still painful because LLMs reorder text in complex layouts, break tables across pages, and fail on graphs or images. 💡Testing the new open-source OCRFlux model, and here the results are really good for a change. So OCRFlux is a multimodal, LLM based toolkit for converting PDFs and images into clean, readable, plain Markdown text. Because the underlying VLM is only 3B param, it runs even on a 3090 GPU. The model is available on Hugging Face . The engine that powers the OCRFlux, teaches the model to rebuild every page and then stitch fragments across pages into one clean Markdown file. It bundles one vision language model with 3B parameters that was fine-tuned from Qwen 2.5-VL-3B-Instruct for both page parsing and cross-page merging. OCRFlux reads raw page images and, guided by task prompts, outputs Markdown for each page and merges split elements across pages. The evaluation shows Edit Distance Similarity (EDS) 0.967 and cross‑page table Tree Edit Distance 0.950, so the parser is both accurate and layout aware. How it works while parsing each page - Convert into text with a natural reading order, even in the presence of multi-column layouts, figures, and insets - Support for complicated tables and equations - Automatically removes headers and footers Cross-page table/paragraph merging - Cross-page table merging - Cross-page paragraph merging A compact vision‑language models can beat bigger models once cross‑page context is added. 🧵 1/n Read on 👇
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PDF parsing is still painful because LLMs reorder text in complex layouts, break tables across pages, and fail on graphs or images. 💡Testing the new open-source OCRFlux model, and here the results are really good for a change. So OCRFlux is a multimodal, LLM based toolkit for converting PDFs and images into clean, readable, plain Markdown text. Because the underlying VLM is only 3B param, it runs even on a 3090 GPU. The model is available on Hugging Face . The engine that powers the OCRFlux, teaches the model to rebuild every page and then stitch fragments across pages into one clean Markdown file. It bundles one vision language model with 3B parameters that was fine-tuned from Qwen 2.5-VL-3B-Instruct for both page parsing and cross-page merging. OCRFlux reads raw page images and, guided by task prompts, outputs Markdown for each page and merges split elements across pages. The evaluation shows Edit Distance Similarity (EDS) 0.967 and cross‑page table Tree Edit Distance 0.950, so the parser is both accurate and layout aware. How it works while parsing each page - Convert into text with a natural reading order, even in the presence of multi-column layouts, figures, and insets - Support for complicated tables and equations - Automatically removes headers and footers Cross-page table/paragraph merging - Cross-page table merging - Cross-page paragraph merging A compact vision‑language models can beat bigger models once cross‑page context is added. 🧵 1/n Read on 👇

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In the latest version, we've launched the Chat feature! You can now: 1. Chat with AI from OpenAI, Gemini, and Anthropic using your notes as context. 2. Chat with collaborators and track mentions in your Inbox. 3. Drag messages onto whiteboards for deeper exploration.
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In the latest version, we've launched the Chat feature! You can now: 1. Chat with AI from OpenAI, Gemini, and Anthropic using your notes as context. 2. Chat with collaborators and track mentions in your Inbox. 3. Drag messages onto whiteboards for deeper exploration.

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In the latest version, you can add whiteboards as contexts to AI chat! In AI's response, we’ve introduced reference links to the cards and blocks that the response is grounded on, so you’re able to locate the source content.
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In the latest version, you can add whiteboards as contexts to AI chat! In AI's response, we’ve introduced reference links to the cards and blocks that the response is grounded on, so you’re able to locate the source content.

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One of the hardest problems with using AI agents to automate meaningful document work (contracts, KYC, diligence, claims, and more) is not actually building the agent, but building the UI/UX audit trail so the human can understand decisions linking back to the source documents - in PDFs, Powerpoint, Word, etc. This requires really good layout detection and segmentation, that you need to surface as *metadata context* to the AI agent, beyond any document -> markdown conversion. We've built really-nice VLM-capabilities to identify and segment all the elements within your document: from tables to charts to form boxes. You can use this metadata to directly provide experiences where humans can analyze the source document even as decisions are being made. Come check out LlamaParse!
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📚🤖 Open Source: AI reads PDFs Page-by-Page, extracts Knowledge & Summarizes 🥳 Repo in comment 💝 - 📚 Automated page by page PDF book analysis and knowledge extraction - 🤖 AI-powered content understanding and summarization - 📊 Interval-based progress summaries - 💾 Persistent knowledge base storage - 📝 Markdown-formatted summaries - 🎨 Color-coded terminal output for better visibility - 🔄 Resume capability with existing knowledge base - ⚙️ Configurable analysis intervals and test modes - 🚫 Smart content filtering (skips TOC, index pages, etc.) - 📂 Organized directory structure for outputs ---- Want to level up your Cursor game? I’ve created a 28-chapter course on mastering Cursor. Check it out via the link in my bio! featuring 19 hours of content where we build full apps from scratch in each chapter. each chapter is short and independent and designed to get your started quickly.

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We have just released the first version of our web clipper! It allows you to save and tag web content in Heptabase, and further break it down into atomic knowledge cards.
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We have just released the first version of our web clipper! It allows you to save and tag web content in Heptabase, and further break it down into atomic knowledge cards.

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Everyone uploading PDFs to Claude is burning 70,000 tokens before asking a single question. A 20-page document costs between 1,500 to 3,000 tokens per page. Formatting. Broken tables. Images. Junk metadata. Claude processes all of it before you even type “summarize this.” There’s a free fix. It’s called Markitdown. Microsoft built it. 10,000+ GitHub stars. Almost nobody in the Claude community is talking about it. It converts any file PDFs, Word docs, Excel sheets, PowerPoints, YouTube videos into clean Markdown text. Token usage drops by up to 70%. Answers get better, not worse. Claude was trained on millions of Markdown documents. It reads the format natively. You’re not compressing quality you’re removing noise. There’s an MCP server too. Connect it to Claude Desktop once. Every file you upload auto-converts to .md before Claude touches it. You do nothing. It just works. Old way: upload PDF → Claude burns 70k tokens on junk → mediocre answer New way: Markitdown → clean .md → 70% fewer tokens → sharper answer The tool is free. The MCP setup takes 10 minutes. The token savings are permanent. You’ve been paying for Claude to read garbage. Stop.
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Everyone uploading PDFs to Claude is burning 70,000 tokens before asking a single question. A 20-page document costs between 1,500 to 3,000 tokens per page. Formatting. Broken tables. Images. Junk metadata. Claude processes all of it before you even type “summarize this.” There’s a free fix. It’s called Markitdown. Microsoft built it. 10,000+ GitHub stars. Almost nobody in the Claude community is talking about it. It converts any file PDFs, Word docs, Excel sheets, PowerPoints, YouTube videos into clean Markdown text. Token usage drops by up to 70%. Answers get better, not worse. Claude was trained on millions of Markdown documents. It reads the format natively. You’re not compressing quality you’re removing noise. There’s an MCP server too. Connect it to Claude Desktop once. Every file you upload auto-converts to .md before Claude touches it. You do nothing. It just works. Old way: upload PDF → Claude burns 70k tokens on junk → mediocre answer New way: Markitdown → clean .md → 70% fewer tokens → sharper answer The tool is free. The MCP setup takes 10 minutes. The token savings are permanent. You’ve been paying for Claude to read garbage. Stop.

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LiteParse Samples 📄🧑‍🏫 LiteParse is our free and fast document parser that can parse text with bounding boxes from any document. I’ve created a new repository that contains some demos on how you can make use of its outputs! ✅ Comparison against PyPDF and PyMuPDF ✅ Visual Citations: search for any keyword and see joined bounding boxes pop up in the source images It should be a default tool to help AI agents OCR any document type (supports PDF, Word, Powerpoint + dozens of other formats), before heavier-weight VLM-based document parsing solutions (like our own LlamaParse). LiteParse Samples repo: LiteParse:

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Got the vibe but not the words? Starting today, you can simply show or tell AI Mode about what you're looking for and get rich, visual results to explore. ✨ This experience works by bringing together Search’s world-class visual understanding from Lens and Image search with Gemini 2.5’s advanced multimodal capabilities. When you show AI Mode what you’re searching for it uses our new “visual search fan-out” technique to get a deeper understanding of what’s in an image (including subtle details and secondary objects), and runs multiple queries in the background. This helps it understand the visual context and nuance of your request to deliver relevant visual responses. You can also tell AI Mode what vibe you’re going for (no image needed). For example, ask AI Mode for bedroom design inspo and go back and forth to refine your vision. We’re rolling out this experience in English in the U.S. starting this week. Try it out and let us know what you think!

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In the latest version, you can use the Insight Generator, an early alpha feature that helps you grasp the knowledge structure of a long article ten times faster and quickly navigate to different parts of the knowledge source that are useful or interesting to you.
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In the latest version, you can use the Insight Generator, an early alpha feature that helps you grasp the knowledge structure of a long article ten times faster and quickly navigate to different parts of the knowledge source that are useful or interesting to you.

Heptabase

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Introducing LiteParse - the best model-free document parsing tool for AI agents 💫 ✅ It’s completely open-source and free. ✅ No GPU required, will process ~500 pages in 2 seconds on commodity hardware ✅ More accurate than PyPDF, PyMuPDF, Markdown. Also way more readable - see below for how we parse tables!! ✅ Supports 50+ file formats, from PDFs to Office docs to images ✅ Is designed to plug and play with Claude Code, OpenClaw, and any other AI agent with a one-line skills install. Supports native screenshotting capabilities. We spent years building up LlamaParse by orchestrating state-of-the-art VLMs over the most complex documents. Along the way we realized that you could get quite far on most docs through fast and cheap text parsing. Take a look at the video below. For really complex tables within PDFs, we output them in a spatial grid that’s both AI and human-interpretable. Any other free/light parser light PyPDF will destroy the representation of this table and output a sequential list. This is not a replacement for a VLM-based OCR tool (it requires 0 GPUs and doesn’t use models), but it is shocking how good it is to parse most documents. Huge shoutout to Logan Markewich and Clelia Bertelli (🦙/acc) for all the work here. Come check it out: Repo:
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Introducing LiteParse - the best model-free document parsing tool for AI agents 💫 ✅ It’s completely open-source and free. ✅ No GPU required, will process ~500 pages in 2 seconds on commodity hardware ✅ More accurate than PyPDF, PyMuPDF, Markdown. Also way more readable - see below for how we parse tables!! ✅ Supports 50+ file formats, from PDFs to Office docs to images ✅ Is designed to plug and play with Claude Code, OpenClaw, and any other AI agent with a one-line skills install. Supports native screenshotting capabilities. We spent years building up LlamaParse by orchestrating state-of-the-art VLMs over the most complex documents. Along the way we realized that you could get quite far on most docs through fast and cheap text parsing. Take a look at the video below. For really complex tables within PDFs, we output them in a spatial grid that’s both AI and human-interpretable. Any other free/light parser light PyPDF will destroy the representation of this table and output a sequential list. This is not a replacement for a VLM-based OCR tool (it requires 0 GPUs and doesn’t use models), but it is shocking how good it is to parse most documents. Huge shoutout to Logan Markewich and Clelia Bertelli (🦙/acc) for all the work here. Come check it out: Repo:

Jerry Liu

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In the latest version, you can now easily create and insert editor templates!
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In the latest version, you can now easily create and insert editor templates!

Heptabase

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Launching full support for Even Realities G2 smart glasses in MentraOS. With Mentra, you can run multiple smart glasses apps at once. You: in a meeting in Tokyo on 82nd floor. You're running translation on your smart glasses and understanding every word. Your Notetaker is running in the background capturing ideas as they're spoken. Your proactive AI agent is listening in, hears someone ask a question, and automatically overlays the answer on your vision. That's what's Mentra unlocks. MentraOS is the open source OS for smart glasses. We give users an app store and allow developers to write one app that runs on any pair of smart glasses. It's 100% open source, private, and free. How to use: Download the Mentra app, connect your G2, and get instant access to captions, AI notetaker, Merge proactive AI, translation, and other apps. You can switch back to the Even app at anytime. If you want to unlock your Even G2, then downalod Mentra on the Google or Apple App Store.

cayden 凯登

38,937 просмотров • 2 месяцев назад

"What would browser APIs for AI look like?" Here's a preview of Chrome's latest experiments integrating AI directly in the browser using Gemini Nano. The team has been working hard to bring the power of on-device AI models to developers via (experimental) new Web APIs. These include: ✍️ Writing new content (Writer API) ♻️ Rewriting existing text (Rewriter API) 📚 Summarizing information (Summarizer API) 🗣️ Translation (Translation API - coming soon) What sets this apart is the speed and privacy - you'll notice remarkably quick responses, all happening right in your browser thanks to the small language model being local. The initial exploration revolved around the Prompt API, allowing users to broadly interact with an AI model similarly to chat interfaces you might be familiar with. In the demo video I've recorded, you can see these features in action - from generating a post about a trip to rewriting content in different styles and lengths, and even translating text into various languages. Many of these experiments can be enabled through Chrome flags. We're excited to see what you build with the experimental APIs. Do feel free to pass along feedback to Kenji Baheux Domenic Denicola and André Bandarra
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"What would browser APIs for AI look like?" Here's a preview of Chrome's latest experiments integrating AI directly in the browser using Gemini Nano. The team has been working hard to bring the power of on-device AI models to developers via (experimental) new Web APIs. These include: ✍️ Writing new content (Writer API) ♻️ Rewriting existing text (Rewriter API) 📚 Summarizing information (Summarizer API) 🗣️ Translation (Translation API - coming soon) What sets this apart is the speed and privacy - you'll notice remarkably quick responses, all happening right in your browser thanks to the small language model being local. The initial exploration revolved around the Prompt API, allowing users to broadly interact with an AI model similarly to chat interfaces you might be familiar with. In the demo video I've recorded, you can see these features in action - from generating a post about a trip to rewriting content in different styles and lengths, and even translating text into various languages. Many of these experiments can be enabled through Chrome flags. We're excited to see what you build with the experimental APIs. Do feel free to pass along feedback to Kenji Baheux Domenic Denicola and André Bandarra

Addy Osmani

48,705 просмотров • 1 год назад

You can now search and add multiple cards and web elements from the search bar of the right sidebar!
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You can now search and add multiple cards and web elements from the search bar of the right sidebar!

Heptabase

12,580 просмотров • 2 лет назад

Our first Generative AI short course in JavaScript! GitHub recently reported that JavaScript is again the world’s most popular programming language. To support web developers exploring and developing with generative AI, we just launched a new short course in JavaScript taught by Jacob Lee, founding engineer at . In ​​Build LLM Apps with LangChain.js you’ll learn elements common in AI development, including: (i) Using data loaders to pull data from common sources such as PDFs, websites, and databases (ii) Prompts, which are used to provide the LLM context (iii) Modules to support RAG such as text splitters and integrations with vector stores (iv) Working with different models to write applications that are not vendor-specific (v) Parsers, which extract and format the output for your downstream code to process You’ll also build with the LangChain Expression Language, which lets you easily compose sequences (also called chains) of modules to perform complex tasks using LLMs. Putting all this together, you’ll also work on a conversational question-answering LLM application capable of using external data as context. Please sign up here:
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Our first Generative AI short course in JavaScript! GitHub recently reported that JavaScript is again the world’s most popular programming language. To support web developers exploring and developing with generative AI, we just launched a new short course in JavaScript taught by Jacob Lee, founding engineer at . In ​​Build LLM Apps with LangChain.js you’ll learn elements common in AI development, including: (i) Using data loaders to pull data from common sources such as PDFs, websites, and databases (ii) Prompts, which are used to provide the LLM context (iii) Modules to support RAG such as text splitters and integrations with vector stores (iv) Working with different models to write applications that are not vendor-specific (v) Parsers, which extract and format the output for your downstream code to process You’ll also build with the LangChain Expression Language, which lets you easily compose sequences (also called chains) of modules to perform complex tasks using LLMs. Putting all this together, you’ll also work on a conversational question-answering LLM application capable of using external data as context. Please sign up here:

Andrew Ng

284,320 просмотров • 2 лет назад

New course: Document AI: From OCR to Agentic Doc Extraction, built with LandingAI, where I'm executive chairman, and taught by David Park and Andrea Kropp. Much of the world's data is locked in PDFs, JPEGs, and other documents. This short course shows you how to build agentic workflows that process documents accurately: breaking them into parts, examining each piece carefully, and extracting information through multiple iterations. Traditional Optical Character Recognition (OCR) captures text but loses context from table headers, chart captions, or reading order of columns. After exploring OCR's limitations, you’ll use LandingAI's Agentic Document Extraction (ADE) framework to process documents. ADE treats pages as visually -- as images -- to parse information and extract fields. Skills you'll gain: - Build agents to convert unstructured files into structured Markdown/HTML and JSON - Use ADE to parse complex data like forms, handwriting, or equations - Map extracted information to named fields using a specified schema, with bounding boxes for grounding and validation - Deploy RAG applications with event-driven document processing Come learn about the best tools for processing documents like financial invoices, medical records, or academic papers intelligently:
3:31

New course: Document AI: From OCR to Agentic Doc Extraction, built with LandingAI, where I'm executive chairman, and taught by David Park and Andrea Kropp. Much of the world's data is locked in PDFs, JPEGs, and other documents. This short course shows you how to build agentic workflows that process documents accurately: breaking them into parts, examining each piece carefully, and extracting information through multiple iterations. Traditional Optical Character Recognition (OCR) captures text but loses context from table headers, chart captions, or reading order of columns. After exploring OCR's limitations, you’ll use LandingAI's Agentic Document Extraction (ADE) framework to process documents. ADE treats pages as visually -- as images -- to parse information and extract fields. Skills you'll gain: - Build agents to convert unstructured files into structured Markdown/HTML and JSON - Use ADE to parse complex data like forms, handwriting, or equations - Map extracted information to named fields using a specified schema, with bounding boxes for grounding and validation - Deploy RAG applications with event-driven document processing Come learn about the best tools for processing documents like financial invoices, medical records, or academic papers intelligently:

Andrew Ng

200,462 просмотров • 6 месяцев назад