Loading video...

Video Failed to Load

Go Home

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 :

openalgoHQ's profile picture

OpenAlgo

1,408 subscribers

11,793 views • 6 days ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

google just turned karpathy's llm-wiki gist into a spec today: the open knowledge format. markdown files, one required field. the format question is now settled. the hard part still isn't. what it changes: - portability. a bundle is a tarball or a git repo. it moves between orgs and tools with no translation layer. - version control. knowledge lives beside the code it describes, with diffs and history for free. - decoupling. a human writes the bundle, an agent reads it, with no shared tooling between them. - no SDK, no account. you read and write plain markdown. what it leaves to you: - contradiction resolution. a new source fights an old claim. OKF stores both and never says which wins. - staleness. timestamp is a field. retiring a dead fact is a call the format won't make. - merge policy. which page a fact lands on, what gets forked or overwritten. the producer agent owns it. - retrieval. index.md holds for a few hundred pages. past that you need hybrid search and a reranker. OKF standardized the filing cabinet. the librarian is still yours to build.
0:45

google just turned karpathy's llm-wiki gist into a spec today: the open knowledge format. markdown files, one required field. the format question is now settled. the hard part still isn't. what it changes: - portability. a bundle is a tarball or a git repo. it moves between orgs and tools with no translation layer. - version control. knowledge lives beside the code it describes, with diffs and history for free. - decoupling. a human writes the bundle, an agent reads it, with no shared tooling between them. - no SDK, no account. you read and write plain markdown. what it leaves to you: - contradiction resolution. a new source fights an old claim. OKF stores both and never says which wins. - staleness. timestamp is a field. retiring a dead fact is a call the format won't make. - merge policy. which page a fact lands on, what gets forked or overwritten. the producer agent owns it. - retrieval. index.md holds for a few hundred pages. past that you need hybrid search and a reranker. OKF standardized the filing cabinet. the librarian is still yours to build.

Rohit

30,710 views • 27 days ago

Cabinet: local AI knowledge base. Plain Markdown files on your machine. No lock-in. (Imagine if Paperclip + Obsidian made a baby) AI workforce - your data + your agents, one place, locally, yours.
0:47

Cabinet: local AI knowledge base. Plain Markdown files on your machine. No lock-in. (Imagine if Paperclip + Obsidian made a baby) AI workforce - your data + your agents, one place, locally, yours.

Hila Shmuel

45,929 views • 3 months ago

This is one of the coolest open-source AI agent projects I've seen in a while: 'Understand Anything' It's a plugin for Claude Code, Codex, OpenCode etc. that analyzes your codebase and turns it into a knowledge base that you can interact with. It explains the codebase to you, rather than showing you the structure. It seems like it's designed for code but I opened my Obsidian vault of podcast highlights in Claude Code, then ran /understand. The result is a knowledge graph that I can search of highlights from 888 podcast episodes and 144K lines of markdown text.
0:22

This is one of the coolest open-source AI agent projects I've seen in a while: 'Understand Anything' It's a plugin for Claude Code, Codex, OpenCode etc. that analyzes your codebase and turns it into a knowledge base that you can interact with. It explains the codebase to you, rather than showing you the structure. It seems like it's designed for code but I opened my Obsidian vault of podcast highlights in Claude Code, then ran /understand. The result is a knowledge graph that I can search of highlights from 888 podcast episodes and 144K lines of markdown text.

Dan McAteer

172,524 views • 20 days ago

Google's new Open Knowledge Format opens up so many possibilities (and opportunities)! It is a standardized format for storing your knowledge in a way that agents can easily use. Instead of giving agents ALL of your knowledge to stuff into its context window, you give agents a map so they can find specifically what they need. I've shared some examples in my latest blog post and video.
18:09

Google's new Open Knowledge Format opens up so many possibilities (and opportunities)! It is a standardized format for storing your knowledge in a way that agents can easily use. Instead of giving agents ALL of your knowledge to stuff into its context window, you give agents a map so they can find specifically what they need. I've shared some examples in my latest blog post and video.

Marie Haynes

31,648 views • 24 days ago

Knowledge graphs are infinitely better than vector search for building the memory of AI agents. With five lines of code, you can build a knowledge graph with your data. When you see the results, you'll never go back to vector-mediocrity-land. Here is a quick video:
9:27

Knowledge graphs are infinitely better than vector search for building the memory of AI agents. With five lines of code, you can build a knowledge graph with your data. When you see the results, you'll never go back to vector-mediocrity-land. Here is a quick video:

Santiago

397,989 views • 1 year 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.
11:39

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

Building AI agents starts with a memory you can verify. The OriginTrail Decentralized Knowledge Graph (DKG) gives agents a verifiable memory layer built on structured knowledge you can trace back to its source. Brana Rakic explains 💬
0:39

Building AI agents starts with a memory you can verify. The OriginTrail Decentralized Knowledge Graph (DKG) gives agents a verifiable memory layer built on structured knowledge you can trace back to its source. Brana Rakic explains 💬

OriginTrail

25,946 views • 4 months ago

Your AI agents can now explore knowledge infinitely. The new HyperGraph. Type a topic. Get a skill graph. Click any node to go deeper. Watch new concepts bloom in real time. Start with one idea. End with a complete knowledge base. Open source. Powered by Hyperbrowser.
0:29

Your AI agents can now explore knowledge infinitely. The new HyperGraph. Type a topic. Get a skill graph. Click any node to go deeper. Watch new concepts bloom in real time. Start with one idea. End with a complete knowledge base. Open source. Powered by Hyperbrowser.

Hyperbrowser

11,243 views • 3 months ago

An China guy discovered a method to learn any knowledge instantly using AI. The key is Obsidian + any AI — Kimi, Claude, or Gemini. Most people learn slowly: read, forget, read again, forget again. His method: use AI to transform any content into small, interconnected notes. Use Obsidian to link them, so every piece of knowledge is never isolated. Slow method: highlight books, keep going, forget after a week. Fast method: AI breaks it into atomic notes, Obsidian links them into a network. Six months later, a new idea instantly connects to twenty things you already know. I compiled the full A–Z guide to building a second brain with Obsidian — works with Kimi, Claude, or Gemini — that most people have never discovered. Article below
0:26

An China guy discovered a method to learn any knowledge instantly using AI. The key is Obsidian + any AI — Kimi, Claude, or Gemini. Most people learn slowly: read, forget, read again, forget again. His method: use AI to transform any content into small, interconnected notes. Use Obsidian to link them, so every piece of knowledge is never isolated. Slow method: highlight books, keep going, forget after a week. Fast method: AI breaks it into atomic notes, Obsidian links them into a network. Six months later, a new idea instantly connects to twenty things you already know. I compiled the full A–Z guide to building a second brain with Obsidian — works with Kimi, Claude, or Gemini — that most people have never discovered. Article below

Kirill

70,061 views • 8 days ago

Btwww, this is my first time recording myself, so i know there are probably a lot of grammar mistakes. Sorry about that🙏 Quick technical breakdown: - Knowledge Base: Built entirely on top of my obsidian vault using its native graph view - Data Pipeline: reads and caches all my markdown files locally
3:37

Btwww, this is my first time recording myself, so i know there are probably a lot of grammar mistakes. Sorry about that🙏 Quick technical breakdown: - Knowledge Base: Built entirely on top of my obsidian vault using its native graph view - Data Pipeline: reads and caches all my markdown files locally

Rahmi

98,769 views • 5 days 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:
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 views • 9 months ago

Knowledge graphs are insanely good at giving agents human-like memory! Today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. It's built using a real-time knowledge graph. 100% open-source and self-hosted.
10:52

Knowledge graphs are insanely good at giving agents human-like memory! Today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. It's built using a real-time knowledge graph. 100% open-source and self-hosted.

Akshay 🚀

156,446 views • 1 year ago

knowledge graphs are INSANELY good at giving agents human-like memory! today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. it's built using a real-time knowledge graph. 100% open-source and self-hosted.
10:52

knowledge graphs are INSANELY good at giving agents human-like memory! today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. it's built using a real-time knowledge graph. 100% open-source and self-hosted.

Akshay 🚀

57,462 views • 9 months ago

A LONDON-BASED AI ENGINEER WITH A KNOWLEDGE GRAPH THESIS FROM BIRMINGHAM JUST SHIPPED GRAPHIFY: ONE COMMAND TURNS ANY FOLDER INTO A CLAUDE CODE SECOND BRAIN. Safi Shamsi built it 48 hours after Karpathy posted his LLM wiki idea. It turns any folder, codebase, docs, PDFs, into a knowledge graph Claude reads instead of grepping. Up to 71.5x fewer tokens per query on the right corpus. The trick almost nobody is using yet: one flag exports the entire graph as a fully-linked Obsidian vault. Repo: safishamsi/graphify Setup, end to end: Install: uv tool install graphifyy (or pipx install graphifyy). Verify with graphify --version. Install the skill in Claude Code: graphify claude install. Wires Graphify in as a callable skill. Open the folder you want mapped. In the terminal: graphify . It extracts every concept and builds the graph in graphify-out/. Export to Obsidian: graphify . --obsidian. Writes one note per concept, every relationship as a wikilink, every node linked back to its source. Open the new vault in Obsidian, or drag it as a subfolder into your existing one. Your Claude Code instance now has a navigable map of the codebase that loads instantly instead of re-reading files every session. Full step-by-step build of the Claude + Obsidian second brain in the article below. Bookmark this.
16:36

A LONDON-BASED AI ENGINEER WITH A KNOWLEDGE GRAPH THESIS FROM BIRMINGHAM JUST SHIPPED GRAPHIFY: ONE COMMAND TURNS ANY FOLDER INTO A CLAUDE CODE SECOND BRAIN. Safi Shamsi built it 48 hours after Karpathy posted his LLM wiki idea. It turns any folder, codebase, docs, PDFs, into a knowledge graph Claude reads instead of grepping. Up to 71.5x fewer tokens per query on the right corpus. The trick almost nobody is using yet: one flag exports the entire graph as a fully-linked Obsidian vault. Repo: safishamsi/graphify Setup, end to end: Install: uv tool install graphifyy (or pipx install graphifyy). Verify with graphify --version. Install the skill in Claude Code: graphify claude install. Wires Graphify in as a callable skill. Open the folder you want mapped. In the terminal: graphify . It extracts every concept and builds the graph in graphify-out/. Export to Obsidian: graphify . --obsidian. Writes one note per concept, every relationship as a wikilink, every node linked back to its source. Open the new vault in Obsidian, or drag it as a subfolder into your existing one. Your Claude Code instance now has a navigable map of the codebase that loads instantly instead of re-reading files every session. Full step-by-step build of the Claude + Obsidian second brain in the article below. Bookmark this.

CyrilXBT

31,188 views • 18 days ago

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

123,772 views • 1 year 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.
17:08

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.

0xMarioNawfal

68,173 views • 2 months ago

Andrej Karpathy quietly shipped the best second brain idea in years not an app. a pattern. let an llm maintain a wiki of your notes. you dump sources, it reads them, links them, files them. knowledge compounds like interest. someone built it into a free claude code plugin. setup is two commands: claude plugin marketplace add AgriciDaniel/claude-obsidian claude plugin install claude-obsidian@agricidaniel-claude-obsidian then open obsidian, open claude code in the same folder, type /wiki. that's it. your notes are now queryable by claude and they get richer every time you read something. bookmark this. best thing you'll build this weekend.
0:42

Andrej Karpathy quietly shipped the best second brain idea in years not an app. a pattern. let an llm maintain a wiki of your notes. you dump sources, it reads them, links them, files them. knowledge compounds like interest. someone built it into a free claude code plugin. setup is two commands: claude plugin marketplace add AgriciDaniel/claude-obsidian claude plugin install claude-obsidian@agricidaniel-claude-obsidian then open obsidian, open claude code in the same folder, type /wiki. that's it. your notes are now queryable by claude and they get richer every time you read something. bookmark this. best thing you'll build this weekend.

Avid

335,642 views • 27 days ago

This is why I say Arrogance + Ignorance = Liberalism Dhruv Rathee explains that Elon Musk has not built anything and does not have much knowledge Imagine a world where Dhruv Rathee is a tech visionary, but Elon Musk is not... That's the world liberals live in... 😂
0:32

This is why I say Arrogance + Ignorance = Liberalism Dhruv Rathee explains that Elon Musk has not built anything and does not have much knowledge Imagine a world where Dhruv Rathee is a tech visionary, but Elon Musk is not... That's the world liberals live in... 😂

Abhishek

56,098 views • 4 months ago

Exploring Andrej Karpathy Nanochat through a Knowledge Graph Love how this project combines simplicity with power. Want to navigate and understand the entire repo? Check it out here: Plus, you can connect it via MCP to any AI coding assistant and work directly on the repository. Powered by Deep Graph MCP AI at Meta and Groq Inc
0:23

Exploring Andrej Karpathy Nanochat through a Knowledge Graph Love how this project combines simplicity with power. Want to navigate and understand the entire repo? Check it out here: Plus, you can connect it via MCP to any AI coding assistant and work directly on the repository. Powered by Deep Graph MCP AI at Meta and Groq Inc

Daniel San

126,642 views • 8 months ago

The veil is lifting - BiomeAI awakens. 🧬 Forged from a living Knowledge Graph 🤖 Deployed as your autonomous Agent 🌐 Portal opening - landing page live Where Science converges with AI
0:58

The veil is lifting - BiomeAI awakens. 🧬 Forged from a living Knowledge Graph 🤖 Deployed as your autonomous Agent 🌐 Portal opening - landing page live Where Science converges with AI

BiomeAI

66,597 views • 10 months ago