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⚽ The FIFA World Cup, live on the decentralized knowledge graph (DKG). Every match, result and player–club affiliation ingested in real time and published as one shared context graph powered by OriginTrail, structured with IPTC Sport Schema. Any AI agent can plug in and get reliable, source-verifiable context: live... show more

Žiga Drev

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34,772 views • 1 month ago •via X (Twitter)

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Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,166 views • 2 months ago

🧬A trust layer for genomic AI powered by OriginTrail, has been implemented under the ELSA - European Lighthouse on Secure and Safe AI funded by the EU. Trace Labs - Trusted Context for AI Agents's Jurij Skornik demonstrated it in action ↯ 00:00 - Introduction 00:30 - Key challenges in decentralized science 00:46 - Decentralized Knowledge Graph power 01:35 - How an AI agent can assess Alzheimer’s risk 02:32 - Working with private patient data 03:50 - Solution in action: Personal healthcare AI agent

🧬A trust layer for genomic AI powered by OriginTrail, has been implemented under the ELSA - European Lighthouse on Secure and Safe AI funded by the EU. Trace Labs - Trusted Context for AI Agents's Jurij Skornik demonstrated it in action ↯ 00:00 - Introduction 00:30 - Key challenges in decentralized science 00:46 - Decentralized Knowledge Graph power 01:35 - How an AI agent can assess Alzheimer’s risk 02:32 - Working with private patient data 03:50 - Solution in action: Personal healthcare AI agent

OriginTrail

37,757 views • 8 months ago

🆕Imagine hundreds of agents working in parallel, handing off to one another and building on each other's work. Every finding becomes a cryptographically anchored Knowledge Asset: verifiable, permanent, owned by the publisher, and queryable by any agent on the network. Enter Decentralized Knowledge Graph v9, already powering AI agent swarms to be: → up to 60% faster → up to 40% cheaper than markdown handoffs. The advantage compounds as the swarm grows. Build something exciting—or simply run a hello-world OriginTrail multiplayer game to try it!

🆕Imagine hundreds of agents working in parallel, handing off to one another and building on each other's work. Every finding becomes a cryptographically anchored Knowledge Asset: verifiable, permanent, owned by the publisher, and queryable by any agent on the network. Enter Decentralized Knowledge Graph v9, already powering AI agent swarms to be: → up to 60% faster → up to 40% cheaper than markdown handoffs. The advantage compounds as the swarm grows. Build something exciting—or simply run a hello-world OriginTrail multiplayer game to try it!

OriginTrail

24,697 views • 5 months ago

Introducing ADI Predictstreet⚡️ The Official Prediction Market Partner of the FIFA World Cup 2026™ More than 5 billion fans will watch the World Cup. ADI Predictstreet was built to reach every single one of them. The first consumer-facing ecosystem project on ADI Chain is going live on the biggest sporting stage the world has ever seen. → Predict match outcomes, player stats, and tournament moments → FIFA World Cup Bracket Challenge powered by ADI Predictstreet → In-stadium activations across FIFA Fan Festivals Every prediction settles onchain, powered by $ADI. More details coming soon.

Introducing ADI Predictstreet⚡️ The Official Prediction Market Partner of the FIFA World Cup 2026™ More than 5 billion fans will watch the World Cup. ADI Predictstreet was built to reach every single one of them. The first consumer-facing ecosystem project on ADI Chain is going live on the biggest sporting stage the world has ever seen. → Predict match outcomes, player stats, and tournament moments → FIFA World Cup Bracket Challenge powered by ADI Predictstreet → In-stadium activations across FIFA Fan Festivals Every prediction settles onchain, powered by $ADI. More details coming soon.

ADI Chain

685,764 views • 4 months ago

Imagine if coding agents never forgot decisions, never duplicated work, and could coordinate across the same codebase in real time. We’re advancing how agents collaborate on codebases with OriginTrail DKG V9 - turning a GitHub repo and agent activity into a shared knowledge graph for persistent memory and coordination. The result: faster agentic development, less repeated work, lower token usage, and agents that can actually build on each other’s progress. Here’s how it works 👇

Imagine if coding agents never forgot decisions, never duplicated work, and could coordinate across the same codebase in real time. We’re advancing how agents collaborate on codebases with OriginTrail DKG V9 - turning a GitHub repo and agent activity into a shared knowledge graph for persistent memory and coordination. The result: faster agentic development, less repeated work, lower token usage, and agents that can actually build on each other’s progress. Here’s how it works 👇

Jurij Skornik

11,802 views • 4 months ago

💊The red pill for medical science: 5 AI agents, 5 siloed sources, 1 shared context graph on Decentralized Knowledge Graph. Published literature, registered trials, real-world safety reports — different owners, different formats, no common ground. We handed the mess to agents that never edited each other's data and asked the question that matters most in medicine: where did each claim come from, and can you check? Working separately, they converged. On 5 disease hubs, 4 of 5 agents landed on the same condition from their own source. Every claim traceable, nothing taken on faith. Evidence, unshackled👇

💊The red pill for medical science: 5 AI agents, 5 siloed sources, 1 shared context graph on Decentralized Knowledge Graph. Published literature, registered trials, real-world safety reports — different owners, different formats, no common ground. We handed the mess to agents that never edited each other's data and asked the question that matters most in medicine: where did each claim come from, and can you check? Working separately, they converged. On 5 disease hubs, 4 of 5 agents landed on the same condition from their own source. Every claim traceable, nothing taken on faith. Evidence, unshackled👇

OriginTrail

607,106 views • 2 months ago

Check out Nano Banana 2 in action. We’re moving beyond simple generation into real world context. By combining Gemini’s knowledge base with real-time web search, the agent doesn't just "guess" what Mountain View looks like, it renders the actual vibe, the current $GOOG ticker, and live local events.

Check out Nano Banana 2 in action. We’re moving beyond simple generation into real world context. By combining Gemini’s knowledge base with real-time web search, the agent doesn't just "guess" what Mountain View looks like, it renders the actual vibe, the current $GOOG ticker, and live local events.

Google Antigravity

151,486 views • 5 months ago

Introducing the context course: a free course on doing ML with agent context. You will learn how to train models, optimize inferences, and build datasets, all by defining harness context with`SKILLS.md`, Plugins, MCP, Subagents, and Hooks. The course includes: - Weekly live AMA on YouTube - Weekly practical projects for ML with context - Instructions in Pi, Codex, Claude, and Opencode - Tutorials and guides on fundamentals - Interactive Quizzes Learn to give AI agents the right knowledge, tools, and structure to actually get work done. Skills, MCP servers, plugins, multi-agent workflows, and building an agent from scratch. Join here:

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Ben Burtenshaw

16,905 views • 3 months ago

RealBriefly has officially released! Decentralized media is now live on the XRP Ledger! A new era in digital media on the XRP Ledger focused on global content, covering markets, events, news, and real-time information from around the world. Write, publish, get paid (in $REAL tokens). You can now earn directly on the XRP Ledger through decentralized media. Publish articles and discussions and receive payments in REAL tokens per post. This movement is powered by the RealFi payment solution and the Real Token. Sign up at and start getting paid today!

RealBriefly has officially released! Decentralized media is now live on the XRP Ledger! A new era in digital media on the XRP Ledger focused on global content, covering markets, events, news, and real-time information from around the world. Write, publish, get paid (in $REAL tokens). You can now earn directly on the XRP Ledger through decentralized media. Publish articles and discussions and receive payments in REAL tokens per post. This movement is powered by the RealFi payment solution and the Real Token. Sign up at and start getting paid today!

Real Briefly

97,894 views • 3 months ago

MICROSOFT QUIETLY BUILT ONE MEMORY MODULE THAT PLUGS INTO ANY AI AGENT AND CUTS ITS CONTEXT BY UP TO 100X no retraining. no task-specific redesign. you just plug it in Microsoft's insight: agents don't need to remember what happened. they need the knowledge pulled from it - the facts and the reusable skills, not the raw logs so PlugMem stops storing history. it compiles experience into a knowledge graph, then feeds the agent only what matters the result: > up to 1-2 orders of magnitude fewer tokens > highest decision-utility per token of any system tested > one unchanged module beats task-specific memory on chat QA, multi-hop search, and web agents more memory was never the answer. better memory is - and Microsoft just shipped it as a plug-in bookmark this. the agents that win won't remember more, they'll remember smarter.

MICROSOFT QUIETLY BUILT ONE MEMORY MODULE THAT PLUGS INTO ANY AI AGENT AND CUTS ITS CONTEXT BY UP TO 100X no retraining. no task-specific redesign. you just plug it in Microsoft's insight: agents don't need to remember what happened. they need the knowledge pulled from it - the facts and the reusable skills, not the raw logs so PlugMem stops storing history. it compiles experience into a knowledge graph, then feeds the agent only what matters the result: > up to 1-2 orders of magnitude fewer tokens > highest decision-utility per token of any system tested > one unchanged module beats task-specific memory on chat QA, multi-hop search, and web agents more memory was never the answer. better memory is - and Microsoft just shipped it as a plug-in bookmark this. the agents that win won't remember more, they'll remember smarter.

NO1ennn

225,032 views • 10 days ago

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph

rewind

27,106 views • 26 days ago

OptimAI Search MCP brings live context to the agents people already use. Claude. Cursor. Copilot. Codex. One integration layer. Everywhere you build. Under the hood, OptimAI can process hundreds to thousands of real-time signals in parallel across the public web, social platforms, and onchain data - all powered by a decentralized network. That is the difference: not another search tool, but infrastructure for agent-native intelligence. Build with it. 💡 -- OptimAI: the #1 AI on BNB Chain DappBay.

OptimAI Search MCP brings live context to the agents people already use. Claude. Cursor. Copilot. Codex. One integration layer. Everywhere you build. Under the hood, OptimAI can process hundreds to thousands of real-time signals in parallel across the public web, social platforms, and onchain data - all powered by a decentralized network. That is the difference: not another search tool, but infrastructure for agent-native intelligence. Build with it. 💡 -- OptimAI: the #1 AI on BNB Chain DappBay.

OptimAI Network

57,439 views • 2 months ago

PREDICTION MARKET RESEARCH JUST GOT KILLED BY ONE .MD FILE. The .md file in the video plugs any AI agent into 1,800 live data sources -> Polymarket orderbooks, satellite imagery, vessel tracking, NOAA weather, SEC filings, sports lines, and the top 100 KOL wallets. It's pref.trade. No APIs, no scraping, no signup and no card. An agent with this installed doesn't ask "What's the price". It pulls the orderbook depth on Polymarket, cross-references vessel positions in the Strait of Hormuz, scans the latest SEC filings on the names mentioned, and watches what the top 100 KOL wallets did in the last 4 hours. Before it makes a single call. The numbers are insane: > $0 in API fees. > $0 in data subscriptions. > 670+ capabilities behind a single endpoint. Every datapoint with full provenance back to the source. The mechanism is wild too: It's called Preference. An MCP server that gives any AI agent structured access to prediction markets -> Polymarket, Kalshi, Hyperliquid, dFlow AND the real-world signals that price them. Your agent asks one question, gets the full picture before it acts. It goes way past Polymarket: Smart-money mirroring on the top 100 wallets in real time. Cross-venue arb scanners and event-driven agents that watch tanker traffic in the Strait of Hormuz and trade oil-linked markets. Backtesting pipelines over historical data plus the world signals that moved each market. The model was never the bottleneck. The data was. One agent, one .md file and Live world data on tap. -> Retail still has 12 CoinGecko tabs open. Agents already have the orderbook. Full info and guide at Don't forget to save.

PREDICTION MARKET RESEARCH JUST GOT KILLED BY ONE .MD FILE. The .md file in the video plugs any AI agent into 1,800 live data sources -> Polymarket orderbooks, satellite imagery, vessel tracking, NOAA weather, SEC filings, sports lines, and the top 100 KOL wallets. It's pref.trade. No APIs, no scraping, no signup and no card. An agent with this installed doesn't ask "What's the price". It pulls the orderbook depth on Polymarket, cross-references vessel positions in the Strait of Hormuz, scans the latest SEC filings on the names mentioned, and watches what the top 100 KOL wallets did in the last 4 hours. Before it makes a single call. The numbers are insane: > $0 in API fees. > $0 in data subscriptions. > 670+ capabilities behind a single endpoint. Every datapoint with full provenance back to the source. The mechanism is wild too: It's called Preference. An MCP server that gives any AI agent structured access to prediction markets -> Polymarket, Kalshi, Hyperliquid, dFlow AND the real-world signals that price them. Your agent asks one question, gets the full picture before it acts. It goes way past Polymarket: Smart-money mirroring on the top 100 wallets in real time. Cross-venue arb scanners and event-driven agents that watch tanker traffic in the Strait of Hormuz and trade oil-linked markets. Backtesting pipelines over historical data plus the world signals that moved each market. The model was never the bottleneck. The data was. One agent, one .md file and Live world data on tap. -> Retail still has 12 CoinGecko tabs open. Agents already have the orderbook. Full info and guide at Don't forget to save.

slash1s

61,519 views • 2 months ago

imagine palantir × polymarket × ai agents I built an open source intelligence platform tracking 16,000+ objects in real time across the globe: - 11,000+ flights with live tracking - 700+ ships - 100s of satellites - earthquakes, volcanoes, active fires - global conflicts & protests - internet outages & GPS jamming - 300+ live CCTV feeds worldwide - data centers & submarine cables - prediction market odds pinned to every country click anything on the map, and an ai agent pulls real-time context and generates a full intelligence briefing with sources

imagine palantir × polymarket × ai agents I built an open source intelligence platform tracking 16,000+ objects in real time across the globe: - 11,000+ flights with live tracking - 700+ ships - 100s of satellites - earthquakes, volcanoes, active fires - global conflicts & protests - internet outages & GPS jamming - 300+ live CCTV feeds worldwide - data centers & submarine cables - prediction market odds pinned to every country click anything on the map, and an ai agent pulls real-time context and generates a full intelligence briefing with sources

Suhail Kakar

42,758 views • 4 months ago

Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

Harjot Gill

4,775,791 views • 3 months ago

Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich

Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich

Helen Yu

10,982 views • 1 month ago

I built a war room dashboard using the Openclaw killer that tracks wars in real time. Missiles. Naval zones. Active fires. Battle damage. All on one map with live notifications as news breaks. It auto-refreshes every 15 minutes. The AI runs everything. I just watch the world move. It's all powered by Minimax's (Maxclaw)

I built a war room dashboard using the Openclaw killer that tracks wars in real time. Missiles. Naval zones. Active fires. Battle damage. All on one map with live notifications as news breaks. It auto-refreshes every 15 minutes. The AI runs everything. I just watch the world move. It's all powered by Minimax's (Maxclaw)

Avid

76,180 views • 5 months ago

OptimAI Search is now on Agentic(.)Market - and we’re bringing the stack to Base. Builders can plug into live, citation-backed context across web, social, and crypto data through MCP, API, and SDK - then pay seamlessly with x402. No brittle workflows. No manual stitching. Just agent-native access to real-time intelligence at scale. And with custom domain agents on the marketplace, specialized knowledge becomes reusable. Search is becoming infrastructure. Build with it: 💡 💡

OptimAI Search is now on Agentic(.)Market - and we’re bringing the stack to Base. Builders can plug into live, citation-backed context across web, social, and crypto data through MCP, API, and SDK - then pay seamlessly with x402. No brittle workflows. No manual stitching. Just agent-native access to real-time intelligence at scale. And with custom domain agents on the marketplace, specialized knowledge becomes reusable. Search is becoming infrastructure. Build with it: 💡 💡

OptimAI Network

63,108 views • 2 months ago

Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian Everything stays on your local machine nothing goes to the cloud

Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian Everything stays on your local machine nothing goes to the cloud

Asteri

1,094,858 views • 2 months ago

48 HOURS AFTER KARPATHY POSTED HIS LLM WIKI IDEA, A 26-YEAR-OLD BIRMINGHAM GRAD SHIPPED THE ONE COMMAND THAT MAKES IT WORK. IT NOW HAS 76,000 GITHUB STARS 1 command. 71.5x fewer tokens per query. 0 vector databases points graphify at any folder - codebase, docs, PDFs, screenshots, video. Tree-sitter parses the code, Claude reads the prose, the whole thing lands as a knowledge graph in graphify-out/ one flag - --obsidian - writes the entire graph as a fully-linked Obsidian vault: one markdown note per concept, every relationship a wikilink, every node linked back to its source. Drop the vault into Claude Code as a skill. Claude queries the graph instead of grepping through raw files, forever Safi Shamsi finished his MSc at Birmingham with Distinction in 2025. His thesis was a knowledge-graph RAG system for academic search. He shipped Graphify 48 hours after Karpathy's post, iterates every week, and has already been forked by Rootly AI Labs for incident data. Hacker News, Analytics Vidhya, Towards AI - all organic.76,000 stars. Three months old no neo4j server. no vector db. no embedding pipeline. no cloud. no monthly fee you're reading this on a device that could clone the repo, run one command, and have a Claude-native knowledge graph of your entire codebase in Obsidian before your next standup

48 HOURS AFTER KARPATHY POSTED HIS LLM WIKI IDEA, A 26-YEAR-OLD BIRMINGHAM GRAD SHIPPED THE ONE COMMAND THAT MAKES IT WORK. IT NOW HAS 76,000 GITHUB STARS 1 command. 71.5x fewer tokens per query. 0 vector databases points graphify at any folder - codebase, docs, PDFs, screenshots, video. Tree-sitter parses the code, Claude reads the prose, the whole thing lands as a knowledge graph in graphify-out/ one flag - --obsidian - writes the entire graph as a fully-linked Obsidian vault: one markdown note per concept, every relationship a wikilink, every node linked back to its source. Drop the vault into Claude Code as a skill. Claude queries the graph instead of grepping through raw files, forever Safi Shamsi finished his MSc at Birmingham with Distinction in 2025. His thesis was a knowledge-graph RAG system for academic search. He shipped Graphify 48 hours after Karpathy's post, iterates every week, and has already been forked by Rootly AI Labs for incident data. Hacker News, Analytics Vidhya, Towards AI - all organic.76,000 stars. Three months old no neo4j server. no vector db. no embedding pipeline. no cloud. no monthly fee you're reading this on a device that could clone the repo, run one command, and have a Claude-native knowledge graph of your entire codebase in Obsidian before your next standup

chewa.

299,208 views • 1 month ago