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Introducing NotebookLlama - an open-source version of NotebookLM! 📓🦙 NotebookLlama is a full implementation of NotebookLM that includes all the capabilities that makes it so great for researchers+business users: ✅ Create a knowledge repository of documents. Has likely higher accuracy than NotebookLM since it’s using LlamaCloud under the hood...

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Jerry Liu

75,447 subscribers

137,734 görüntüleme • 11 ay önce •via X (Twitter)

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Josue profil fotoğrafı
Josue11 ay önce

Why do open-source versions always have to look so bad?

Mobile Scanner profil fotoğrafı
Mobile Scanner11 ay önce

Scan any documents, convert images into text, PDF files, etc. 👍

Ed Choudhry profil fotoğrafı
Ed Choudhry11 ay önce

@Scobleizer Here’s the new YC batch lol

eimen profil fotoğrafı
eimen11 ay önce

man, if you'd improve the UIs of your demos, adoption would be crazy

Peter Wilczynski profil fotoğrafı
Peter Wilczynski11 ay önce

@Scobleizer Does it generate real podcasts (eg can I open them in my podcast app)?

Nikhil Chaudhari profil fotoğrafı
Nikhil Chaudhari11 ay önce

This is amazing Jerry!

psy profil fotoğrafı
psy11 ay önce

Hi Jerry I’m making a react agent as code assistant, the aim is for the agent to understand the repo and it’s md documentations. I have summary, vector and some other tools - in your opinion what’s the best way to optimise this so the agent picks the most relevant tools? Thanks ☺️

Shubham Saboo profil fotoğrafı
Shubham Saboo11 ay önce

Turn any LLM into a real-time voice AI agent. Unmute wraps a LLM into speech-to-text and text-to-speech models, while preserving the reasoning and tool calling capabilities. 100% Opensource.

Peter Steinberger profil fotoğrafı
Peter Steinberger11 ay önce

Telling the agent to keep track of the work in a markdown is so effective, especially once you automated the loop that it can work for hours.

elvis profil fotoğrafı
elvis11 ay önce

Context Engineering Guide I'm writing a detailed guide on context engineering for AI devs. v1 is out now! (bookmark it) I use a concrete deep research multi-agent example to show what context engineering involves.

Trae profil fotoğrafı
Trae11 ay önce

We’ve open-sourced Trae-Agent. You can all `git clone` `cd trae-agent` now

Benzer Videolar

🦙 Since you loved NotebookLlaMa, our open-source, LlamaCloud-backed NotebookLM alternative, we decided to give you more features! In the latest release you can: 📊 Extract and download images and tables from your files, and interactively visualize all tabular data ✅ Chat with your document and verify immediately everything that AI is saying about it, using the new claim verification and citation finding functionality 👇 Watch the video below for a full demo of the new functionalities 👩‍💻🧑‍💻 There is also a bonus: thanks to the community effort and contribution, you can now customize the embedding model you want to use to build the LlamaCloud index, through a textualize.io CLI! ⭐ Star NotebookLlaMa on GitHub: 🦙☁️ Sign-up for LlamaCloud and test our awesome products: 📚 Learn more about LlamaCloud:
1:13

🦙 Since you loved NotebookLlaMa, our open-source, LlamaCloud-backed NotebookLM alternative, we decided to give you more features! In the latest release you can: 📊 Extract and download images and tables from your files, and interactively visualize all tabular data ✅ Chat with your document and verify immediately everything that AI is saying about it, using the new claim verification and citation finding functionality 👇 Watch the video below for a full demo of the new functionalities 👩‍💻🧑‍💻 There is also a bonus: thanks to the community effort and contribution, you can now customize the embedding model you want to use to build the LlamaCloud index, through a textualize.io CLI! ⭐ Star NotebookLlaMa on GitHub: 🦙☁️ Sign-up for LlamaCloud and test our awesome products: 📚 Learn more about LlamaCloud:

LlamaIndex 🦙

11,051 görüntüleme • 11 ay önce

We built LobsterX 🦞, an OpenClaw🦞 specialized for document work on your computer. It uses high-accuracy document parsing, extraction, classification through LlamaCloud, meaning it can comb through complicated PDFs (with scans, tables, diagrams) and extract out 100% accurate context! It can run as a Telegram bot and is built on top of agentfs (Turso) as a file system. Big shoutout to Clelia Bertelli (🦙/acc). This is a fun project inspired by OpenClaw🦞’s success, and besides being a fun tool to use, it can be a great reference for building your own generalized coding agents! Readme: LlamaCloud:
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We built LobsterX 🦞, an OpenClaw🦞 specialized for document work on your computer. It uses high-accuracy document parsing, extraction, classification through LlamaCloud, meaning it can comb through complicated PDFs (with scans, tables, diagrams) and extract out 100% accurate context! It can run as a Telegram bot and is built on top of agentfs (Turso) as a file system. Big shoutout to Clelia Bertelli (🦙/acc). This is a fun project inspired by OpenClaw🦞’s success, and besides being a fun tool to use, it can be a great reference for building your own generalized coding agents! Readme: LlamaCloud:

Jerry Liu

27,071 görüntüleme • 4 ay önce

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:
9:38

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:

Jerry Liu

20,413 görüntüleme • 10 ay önce

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:
0:31

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

255,997 görüntüleme • 3 ay önce

Introducing Document Extraction as an MCP Server ✂️📑 A huge use case for AI agents is being able to extract out items from a diverse set of complex documents in a repeatable manner - whether it’s legal contracts, invoices, financial statements, passports, and more. In this case, 🚫 prompting ChatGPT/Claude is too error-prone/not systematic. 💡We’ve created a plug-and-play MCP server around our core document extraction capabilities within LlamaCloud. 1. Build an extraction agent in LlamaCloud with a standardized schema. 2. Plug it as an MCP tool within ChatGPT/Claude. 3. For your next extraction job, get the outputs in a standardized format through the tool! Huge shoutout to Tuana and Logan Markewich for working on this - this is thanks to the latest updates to our llamacloud-mcp demo repo, which supports both extraction and retrieval as MCP tools. Repo: Sign up:
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Introducing Document Extraction as an MCP Server ✂️📑 A huge use case for AI agents is being able to extract out items from a diverse set of complex documents in a repeatable manner - whether it’s legal contracts, invoices, financial statements, passports, and more. In this case, 🚫 prompting ChatGPT/Claude is too error-prone/not systematic. 💡We’ve created a plug-and-play MCP server around our core document extraction capabilities within LlamaCloud. 1. Build an extraction agent in LlamaCloud with a standardized schema. 2. Plug it as an MCP tool within ChatGPT/Claude. 3. For your next extraction job, get the outputs in a standardized format through the tool! Huge shoutout to Tuana and Logan Markewich for working on this - this is thanks to the latest updates to our llamacloud-mcp demo repo, which supports both extraction and retrieval as MCP tools. Repo: Sign up:

Jerry Liu

57,827 görüntüleme • 11 ay önce

LlamaParse now has an official Agent Skill you can use across 40+ agents. With built-in instructions for parsing complex documents, including different formats, tables, charts, and images, your agents gain access to deeper document understanding, not just raw text extraction. 👇 Watch the demo 📖 Read the docs: 🚀 Get started with LlamaCloud:
1:02

LlamaParse now has an official Agent Skill you can use across 40+ agents. With built-in instructions for parsing complex documents, including different formats, tables, charts, and images, your agents gain access to deeper document understanding, not just raw text extraction. 👇 Watch the demo 📖 Read the docs: 🚀 Get started with LlamaCloud:

LlamaIndex 🦙

51,845 görüntüleme • 2 ay önce

We're excited to launch HyperBookLM. The open-source NotebookLM with web-agents. Simply drop URLs, PDFs & Txt files and, - Ask questions across all your sources - Create structured mind maps - Export to slides in one click - Generate audio summaries you can listen to Open-source & Powered by Hyperbrowser.
1:04

We're excited to launch HyperBookLM. The open-source NotebookLM with web-agents. Simply drop URLs, PDFs & Txt files and, - Ask questions across all your sources - Create structured mind maps - Export to slides in one click - Generate audio summaries you can listen to Open-source & Powered by Hyperbrowser.

Hyperbrowser

68,005 görüntüleme • 6 ay önce

We’re excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts 📊📈 than virtually every other parser, it is now steerable through natural language instructions - output the document in whatever format you desire! It is also the only parsing solution that seamlessly allows you to build accurate RAG over complex documents, free of hallucinations 🔥 We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now it’s better than ever. LlamaParse contains the following killer features: ✅ SOTA table/chart extraction ✅ Seamless integration with LlamaIndex 🦙 advanced RAG/agents ✅✨ Natural language Parsing Instructions ✅✨JSON mode and image extraction ✅✨Support for ~10 document types (.pdf, .pptx, .docx, .xml) and more Our pricing is simple: 1k free per day, and additional pages at 0.3c a page, or $3 for 1k pages. If you want advanced document RAG and/or private deployments, come get in touch with us to chat about LlamaCloud. Check out our full blog post here: LlamaParse client repo: Signup at 🦙☁️: Come talk to us:
5:07

We’re excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts 📊📈 than virtually every other parser, it is now steerable through natural language instructions - output the document in whatever format you desire! It is also the only parsing solution that seamlessly allows you to build accurate RAG over complex documents, free of hallucinations 🔥 We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now it’s better than ever. LlamaParse contains the following killer features: ✅ SOTA table/chart extraction ✅ Seamless integration with LlamaIndex 🦙 advanced RAG/agents ✅✨ Natural language Parsing Instructions ✅✨JSON mode and image extraction ✅✨Support for ~10 document types (.pdf, .pptx, .docx, .xml) and more Our pricing is simple: 1k free per day, and additional pages at 0.3c a page, or $3 for 1k pages. If you want advanced document RAG and/or private deployments, come get in touch with us to chat about LlamaCloud. Check out our full blog post here: LlamaParse client repo: Signup at 🦙☁️: Come talk to us:

LlamaIndex 🦙

143,123 görüntüleme • 2 yıl önce

HyperbookLM - an open-source NotebookLM alternative with a web agent. You can add URLs and it browses, reads, and remembers everything. Then ask questions across all your sources, generate audio summaries, create mind maps, or export to slides. Powered by Hyperbrowser and GPT-5.2. Coming soon as open-source.
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HyperbookLM - an open-source NotebookLM alternative with a web agent. You can add URLs and it browses, reads, and remembers everything. Then ask questions across all your sources, generate audio summaries, create mind maps, or export to slides. Powered by Hyperbrowser and GPT-5.2. Coming soon as open-source.

AshutoshShrivastava

45,539 görüntüleme • 6 ay önce

GitHub + Cursor is just INSANE GitHub has 28 million open-source projects that anyone can fork and build a new product So I built a new product: Repo to Docs The Agent will scan entire GitHub repo, analyze all files and create a knowledge base (docs) for AI coding tools: ↓
2:30

GitHub + Cursor is just INSANE GitHub has 28 million open-source projects that anyone can fork and build a new product So I built a new product: Repo to Docs The Agent will scan entire GitHub repo, analyze all files and create a knowledge base (docs) for AI coding tools: ↓

CJ Zafir

123,037 görüntüleme • 1 yıl önce

Building “RAG 2.0” is just making Claude Code running over your filesystem 🤖🗂️ To make this work well, you need to solve three things 1️⃣ Virtualize your filesystem to prevent the agent from messing stuff up. AgentFS by Turso is a nice example of how you can give the agent access to a copy of all your files without messing up your raw data. 2️⃣ Parse unstructured documents like PDFs, pptx, Word into an LLM-ready format. Agentic OCR solutions like LlamaParse can help here 3️⃣ Creating an agentic loop with human-in-the-loop. If you want to control the agent implementation instead of using Claude Code out of the box, you can use LlamaIndex 🦙 workflows to help orchestrate these long-running agent tasks. Shoutout Clelia Bertelli (🦙/acc), check it out! Blog: Repo:
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Building “RAG 2.0” is just making Claude Code running over your filesystem 🤖🗂️ To make this work well, you need to solve three things 1️⃣ Virtualize your filesystem to prevent the agent from messing stuff up. AgentFS by Turso is a nice example of how you can give the agent access to a copy of all your files without messing up your raw data. 2️⃣ Parse unstructured documents like PDFs, pptx, Word into an LLM-ready format. Agentic OCR solutions like LlamaParse can help here 3️⃣ Creating an agentic loop with human-in-the-loop. If you want to control the agent implementation instead of using Claude Code out of the box, you can use LlamaIndex 🦙 workflows to help orchestrate these long-running agent tasks. Shoutout Clelia Bertelli (🦙/acc), check it out! Blog: Repo:

Jerry Liu

55,620 görüntüleme • 6 ay önce

Introducing LlamaCloud 🦙🌤️ Today we’re thrilled to introduce LlamaCloud, a managed service designed to bring production-grade data for your LLM and RAG app. Spend less time data wrangling and more time on application logic. Launching with the following components: 1️⃣ LlamaParse 📑: a proprietary parser designed to be really really good at complex documents with embedded tables. Build advanced RAG over semi-structured PDFs, and ask questions that simply aren’t possible with the naive stack. Available publicly day 1 🔥 2️⃣ Managed Ingestion/Retrieval API ⚙️: An API letting you easily ingest/retrieve data from data sources. Opening up in private beta to select enterprises. We’re excited to be joined by launch users, partners, and collaborators: Mendable POC MongoDB Qdrant NVIDIA + some awesome hackathon projects at the LlamaIndex 🦙 hackathon Check out our FULL blog post on LlamaCloud and LlamaParse: LlamaParse Client Repo: Signup for a LlamaCloud account to use LlamaParse: Interested in the broader LlamaCloud offering? Come talk to us: Also we have a slick new website 🌐:
2:59

Introducing LlamaCloud 🦙🌤️ Today we’re thrilled to introduce LlamaCloud, a managed service designed to bring production-grade data for your LLM and RAG app. Spend less time data wrangling and more time on application logic. Launching with the following components: 1️⃣ LlamaParse 📑: a proprietary parser designed to be really really good at complex documents with embedded tables. Build advanced RAG over semi-structured PDFs, and ask questions that simply aren’t possible with the naive stack. Available publicly day 1 🔥 2️⃣ Managed Ingestion/Retrieval API ⚙️: An API letting you easily ingest/retrieve data from data sources. Opening up in private beta to select enterprises. We’re excited to be joined by launch users, partners, and collaborators: Mendable POC MongoDB Qdrant NVIDIA + some awesome hackathon projects at the LlamaIndex 🦙 hackathon Check out our FULL blog post on LlamaCloud and LlamaParse: LlamaParse Client Repo: Signup for a LlamaCloud account to use LlamaParse: Interested in the broader LlamaCloud offering? Come talk to us: Also we have a slick new website 🌐:

LlamaIndex 🦙

141,230 görüntüleme • 2 yıl önce

I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:
0:54

I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:

Jerry Liu

21,073 görüntüleme • 4 ay önce

I used Claude Code Skills + souped up PDF parsing to create an M&A deal comp agent 🤖 Given a directory of public M&A filings (DEF 14A), it parses and analyzes each pdf, and generates an Excel sheet with deal terms and comparables. 1️⃣ The native parsing is pypdf which sucks, so I swapped it out with LlamaIndex semtools. This uses LlamaCloud for parsing - letting it handle deeply complex financial tables, charts, and anything else you throw at it. 2️⃣ Claude has native skills to write Excel spreadsheets! Full prompt is in the video below 👇 LlamaCloud + Semtools access also takes < 5 mins to setup, come check it out! Some notes: - disclaimer: Some of the values in the Excel spreadsheet are 100x bigger because they’re formatted as percentages not raw values - We’re working on native Claude skill integrations too! Semtools: LlamaCloud:
1:09

I used Claude Code Skills + souped up PDF parsing to create an M&A deal comp agent 🤖 Given a directory of public M&A filings (DEF 14A), it parses and analyzes each pdf, and generates an Excel sheet with deal terms and comparables. 1️⃣ The native parsing is pypdf which sucks, so I swapped it out with LlamaIndex semtools. This uses LlamaCloud for parsing - letting it handle deeply complex financial tables, charts, and anything else you throw at it. 2️⃣ Claude has native skills to write Excel spreadsheets! Full prompt is in the video below 👇 LlamaCloud + Semtools access also takes < 5 mins to setup, come check it out! Some notes: - disclaimer: Some of the values in the Excel spreadsheet are 100x bigger because they’re formatted as percentages not raw values - We’re working on native Claude skill integrations too! Semtools: LlamaCloud:

Jerry Liu

46,702 görüntüleme • 8 ay önce

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:
6:02

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Santiago

125,928 görüntüleme • 8 ay önce

“Chat with your PDF” still fails on ChatGPT/Claude when the doc is too complex 📉🧾 . If it’s a complicated financial report with scanned tables, you’re going to get back hallucinated values in your analysis! Check out the video 🎬 - in the first part, Claude gives back hallucinated values on the scanned tables in the equity research report. Having a proper document MCP server capable of high-accuracy parsing/indexing can help 💡. In the second part of the video I plug in LlamaCloud as an MCP tool to help with doc parsing and retrieval - it is fully able to render the scanned table in 2D format. You can set one up for yourself in 5 mins in the UI, no coding required. LlamaCloud: MCP Server:
1:17

“Chat with your PDF” still fails on ChatGPT/Claude when the doc is too complex 📉🧾 . If it’s a complicated financial report with scanned tables, you’re going to get back hallucinated values in your analysis! Check out the video 🎬 - in the first part, Claude gives back hallucinated values on the scanned tables in the equity research report. Having a proper document MCP server capable of high-accuracy parsing/indexing can help 💡. In the second part of the video I plug in LlamaCloud as an MCP tool to help with doc parsing and retrieval - it is fully able to render the scanned table in 2D format. You can set one up for yourself in 5 mins in the UI, no coding required. LlamaCloud: MCP Server:

Jerry Liu

21,369 görüntüleme • 11 ay önce

We’ve fully revamped our LlamaCloud integration with n8n.io as part of the LlamaParse v2 + new LlamaCloud SDK launch. What’s new: ✅ Fully stable nodes for LlamaParse, LlamaExtract, and LlamaCloud Index 🆕 Two new services: LlamaClassify and LlamaSheets 📘 A complete step-by-step setup guide Automating your document workflows has never been easier: parse → classify → extract → retrieve, just by plugging in a few nodes! ⭐ Find the repo on GitHub: 📚 Get started with our guide: 🦙 Sign up to LlamaCloud:
0:25

We’ve fully revamped our LlamaCloud integration with n8n.io as part of the LlamaParse v2 + new LlamaCloud SDK launch. What’s new: ✅ Fully stable nodes for LlamaParse, LlamaExtract, and LlamaCloud Index 🆕 Two new services: LlamaClassify and LlamaSheets 📘 A complete step-by-step setup guide Automating your document workflows has never been easier: parse → classify → extract → retrieve, just by plugging in a few nodes! ⭐ Find the repo on GitHub: 📚 Get started with our guide: 🦙 Sign up to LlamaCloud:

LlamaIndex 🦙

13,010 görüntüleme • 4 ay önce

ELON: WE’RE BUILDING A MODERN LIBRARY OF ALEXANDRIA ACROSS EARTH, THE MOON, AND MARS “The idea behind Galactica is to create an open source repository of all knowledge, a distillation of all knowledge, meaning anyone can access it, anyone can use it, and if other people want to train on it, they can do so. We want to create copies of this and distribute them throughout Earth, and even put them on the Moon, Mars, and out into deep space. In a way, it’s a modern day Library of Alexandria. It was a great tragedy that the Library of Alexandria burned down, and so to preserve this knowledge, we want to literally etch it in stone and distribute it widely." Source: Source: Baron Capital
1:32

ELON: WE’RE BUILDING A MODERN LIBRARY OF ALEXANDRIA ACROSS EARTH, THE MOON, AND MARS “The idea behind Galactica is to create an open source repository of all knowledge, a distillation of all knowledge, meaning anyone can access it, anyone can use it, and if other people want to train on it, they can do so. We want to create copies of this and distribute them throughout Earth, and even put them on the Moon, Mars, and out into deep space. In a way, it’s a modern day Library of Alexandria. It was a great tragedy that the Library of Alexandria burned down, and so to preserve this knowledge, we want to literally etch it in stone and distribute it widely." Source: Source: Baron Capital

Mario Nawfal

90,633 görüntüleme • 7 ay önce