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ANTHROPIC JUST SHOWED HOW THEY USE AN OBSIDIAN KNOWLEDGE GRAPH TO MANAGE COMPANY-WIDE KNOWLEDGE

0xMarioNawfal

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46,194 views • 12 days ago •via X (Twitter)

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before collaborator i built out this whole agentic knowledge base app with an entire source docs → knowledge graph → mcp pipeline... and just never released it. you guys want me to release it?

before collaborator i built out this whole agentic knowledge base app with an entire source docs → knowledge graph → mcp pipeline... and just never released it. you guys want me to release it?

Yilio

83,486 views • 3 months ago

OpenAlgo now has a brain. Not Obsidian. Not a vector DB. It is a live knowledge graph built on Google's Open Knowledge Format (OKF). Plain, portable Markdown that any AI agent can read and traverse. Access OKF here :

OpenAlgo now has a brain. Not Obsidian. Not a vector DB. It is a live knowledge graph built on Google's Open Knowledge Format (OKF). Plain, portable Markdown that any AI agent can read and traverse. Access OKF here :

OpenAlgo

11,793 views • 12 days ago

The OpenAI Codex repo turned into a Knowledge Graph! 🤩 If you want to start working on massive codebases and serious projects, you need to use CodeGPT’s Knowledge Graphs. Here’s a step-by-step 🧵 guide to create your Codex graph and connect it to VSCode 👇

The OpenAI Codex repo turned into a Knowledge Graph! 🤩 If you want to start working on massive codebases and serious projects, you need to use CodeGPT’s Knowledge Graphs. Here’s a step-by-step 🧵 guide to create your Codex graph and connect it to VSCode 👇

Daniel San

179,466 views • 1 year ago

What would it look like to combine Google search (shallow Knowledge Graph reasoning over an ultra-ultra-wide index) with LLM in-context learning (highly intelligent operations on a tiny index)?

What would it look like to combine Google search (shallow Knowledge Graph reasoning over an ultra-ultra-wide index) with LLM in-context learning (highly intelligent operations on a tiny index)?

Dwarkesh Patel

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Rwanda has represented Africa and showed that Africa is the continent of integrity, knowledge and organised society. UCI President, David Lappartient, affirms how Rwanda’s hosting has exceeded the expectations Rwanda has a story to share! How did they manage?

Rwanda has represented Africa and showed that Africa is the continent of integrity, knowledge and organised society. UCI President, David Lappartient, affirms how Rwanda’s hosting has exceeded the expectations Rwanda has a story to share! How did they manage?

Africa Today Media Group

18,049 views • 9 months ago

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,284 views • 2 years ago

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

Santiago

155,514 views • 1 year ago

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:

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 views • 9 months ago

Friedrich Hayek: “Knowledge is widely dispersed. We want to make use of knowledge possessed by millions of individuals, which they can only use if they’re fed signals [i.e., prices] about the general situation.” “You can’t possibly concentrate this knowledge.”

Friedrich Hayek: “Knowledge is widely dispersed. We want to make use of knowledge possessed by millions of individuals, which they can only use if they’re fed signals [i.e., prices] about the general situation.” “You can’t possibly concentrate this knowledge.”

F. A. Hayek Quotes

12,683 views • 16 days ago

How the OriginTrail Decentralized Knowledge Graph (DKG) powers the real-world adoption of decentralized AI? It’s built in 3 layers: trust, knowledge base & AI. Let’s explore each layer below! 🧵👇

How the OriginTrail Decentralized Knowledge Graph (DKG) powers the real-world adoption of decentralized AI? It’s built in 3 layers: trust, knowledge base & AI. Let’s explore each layer below! 🧵👇

OriginTrail

34,621 views • 1 year ago

In this brief tutorial, we demonstrate how Alphacast converts documents into an AI-powered knowledge base. You’ll see how to upload PDFs, reports, and presentations — not just structured datasets — and turn them into searchable, contextual knowledge.

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11,115 views • 5 months ago

Obsidian + Claude Code builds a real second brain in 17 minutes. Claude Code reads entire vault, connects notes & builds a knowledge graph of everything you've ever written. Bookmark it. This one actually works.

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0xMarioNawfal

68,173 views • 3 months ago

We’re kicking off 2025 with everything we've got! 🤩 Introducing CodeGPT's Knowledge Graphs, navigating through the entire Anthropic SDK repository. In this example, we loaded Anthropic Python SDK repository and successfully provided the LLM with all the knowledge it needs to fully understand the codebase. You can leverage these knowledge graphs using the CodeGPT extension in VSCode by simply calling the Codebase command 👏 Stay tuned for more tutorials on how to use this powerful tool! Happy 2025! 🎉

We’re kicking off 2025 with everything we've got! 🤩 Introducing CodeGPT's Knowledge Graphs, navigating through the entire Anthropic SDK repository. In this example, we loaded Anthropic Python SDK repository and successfully provided the LLM with all the knowledge it needs to fully understand the codebase. You can leverage these knowledge graphs using the CodeGPT extension in VSCode by simply calling the Codebase command 👏 Stay tuned for more tutorials on how to use this powerful tool! Happy 2025! 🎉

Daniel San

35,682 views • 1 year ago

with only a couple of prompts, Gemini CLI can convert a messy folder containing hundreds of notes into a neatly named, well-structured, cross-linked Obsidian knowledge graph all in about half an hour and at minimal cost. from r/singularity/Ryoiki-Tokuiten

with only a couple of prompts, Gemini CLI can convert a messy folder containing hundreds of notes into a neatly named, well-structured, cross-linked Obsidian knowledge graph all in about half an hour and at minimal cost. from r/singularity/Ryoiki-Tokuiten

Rohan Paul

415,737 views • 1 year ago

Introducing Codebase Knowledge Graphs in Cursor 🤩 In this video, I’ll walk you through how we went from using a knowledge graph of a repository on the CodeGPT platform to leveraging it directly within the Cursor editor This tool is specifically designed to navigate massive codebases, identify nodes and relationships, and deliver richer context to models specialized in code development, such as Anthropic Claude 3.5 Sonnet Stay tuned for an upcoming tutorial where I'll show you step-by-step how to create knowledge graphs and integrate them seamlessly into your entire software development workflow, from Commits and Pull Requests to even Trello tickets!

Introducing Codebase Knowledge Graphs in Cursor 🤩 In this video, I’ll walk you through how we went from using a knowledge graph of a repository on the CodeGPT platform to leveraging it directly within the Cursor editor This tool is specifically designed to navigate massive codebases, identify nodes and relationships, and deliver richer context to models specialized in code development, such as Anthropic Claude 3.5 Sonnet Stay tuned for an upcoming tutorial where I'll show you step-by-step how to create knowledge graphs and integrate them seamlessly into your entire software development workflow, from Commits and Pull Requests to even Trello tickets!

Daniel San

230,185 views • 1 year ago

Traces, knowledge, and reasoning paths form a reliable source of truth for AI agents. 🤝 Charles Ivie, former Senior Graph Architect at Amazon Web Services, on why knowledge-backed traces are essential for trustworthy AI. 03:34 – Why data origin matters 07:17 – Interoperability and trusted knowledge 11:12 – Knowledge graphs as structured AI memory 16:09 – How combining graphs improves trust

Traces, knowledge, and reasoning paths form a reliable source of truth for AI agents. 🤝 Charles Ivie, former Senior Graph Architect at Amazon Web Services, on why knowledge-backed traces are essential for trustworthy AI. 03:34 – Why data origin matters 07:17 – Interoperability and trusted knowledge 11:12 – Knowledge graphs as structured AI memory 16:09 – How combining graphs improves trust

OriginTrail

12,528 views • 6 months ago

Cross-context Personal Knowledge Base with Social Graph🧠 Introducing Cross-context Personal Knowledge Base with Social Graph. Your Memory, Across Every Conversation Most AI forgets you the moment a chat ends. Teamily's Personal Knowledge Base remembers — across every group, every agent, and every context. Powered by your social graph, it connects the dots between your work, relationships, and ideas, so your AI always knows who you are, what you care about, and what happened before. No setup. No tagging. Just intelligence that grows with you. 🙌Try the prompt today: “visualize my knowledge base with social graph” #teamily #AI #Agent #KnowledgeBase #Social #SocialGraph

Cross-context Personal Knowledge Base with Social Graph🧠 Introducing Cross-context Personal Knowledge Base with Social Graph. Your Memory, Across Every Conversation Most AI forgets you the moment a chat ends. Teamily's Personal Knowledge Base remembers — across every group, every agent, and every context. Powered by your social graph, it connects the dots between your work, relationships, and ideas, so your AI always knows who you are, what you care about, and what happened before. No setup. No tagging. Just intelligence that grows with you. 🙌Try the prompt today: “visualize my knowledge base with social graph” #teamily #AI #Agent #KnowledgeBase #Social #SocialGraph

Teamily AI

13,287 views • 2 months ago

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

167,963 views • 10 months ago

When Charlie dropped knowledge on them, they almost had no idea how to react. #ForCharlieKirk

When Charlie dropped knowledge on them, they almost had no idea how to react. #ForCharlieKirk

🚫👁️Drinks on Saturday🇺🇸

2,618,146 views • 10 months ago