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Supercharging Microsoft Microsoft Copilot with verifiable memory, powered by OriginTrail Decentralized Knowledge Graph!⚡️ With Model Context Protocol (MCP), agents can plug into the DKG to access verifiable knowledge, discover context & store trusted memory. Jurij Skornik demos ⤵️

OriginTrail

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48,953 次观看 • 11 个月前 •via X (Twitter)

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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 💬

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 💬

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“If AI agents are going to act on our behalf, they need trusted memory: verifiable data, global identifiers, and clear provenance.” Jurij Skornik on OriginTrail & GS1 enabling AI agents to reason over trusted knowledge, not guesswork. 🤝 01:25 The shift from attention to intention economy 03:51 How AI agents power this shift 06:22 OriginTrail + GS1 for trust, privacy & verifiability 13:08 Live demo of the GS1 x OriginTrail-powered AI agent

“If AI agents are going to act on our behalf, they need trusted memory: verifiable data, global identifiers, and clear provenance.” Jurij Skornik on OriginTrail & GS1 enabling AI agents to reason over trusted knowledge, not guesswork. 🤝 01:25 The shift from attention to intention economy 03:51 How AI agents power this shift 06:22 OriginTrail + GS1 for trust, privacy & verifiability 13:08 Live demo of the GS1 x OriginTrail-powered AI agent

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“OriginTrail is building the biggest collective knowledge base – what we call collective neuro-symbolic AI" – Brana Rakic With privacy-by-design and Decentralized Knowledge Graph (DKG), OriginTrail powers AI without compromising your data. 🫡 Video credits: The Kus

“OriginTrail is building the biggest collective knowledge base – what we call collective neuro-symbolic AI" – Brana Rakic With privacy-by-design and Decentralized Knowledge Graph (DKG), OriginTrail powers AI without compromising your data. 🫡 Video credits: The Kus

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🧬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

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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.

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DeSci Revolution with OriginTrail - powered SciGraph and Aerodrome! 🌐 V8 Decentralized Knowledge Graph (DKG) has arrived to transform research discovery and autonomy through ID Theory's SciGraph on the Base blockchain. 🔑 Unlock the Power of Knowledge Mining: TRAC/ET H Liquidity Pool Now Live! Dive into the pool to access or provide access to TRAC tokens, required for mining Knowledge Assets on SciGraph. 👉

DeSci Revolution with OriginTrail - powered SciGraph and Aerodrome! 🌐 V8 Decentralized Knowledge Graph (DKG) has arrived to transform research discovery and autonomy through ID Theory's SciGraph on the Base blockchain. 🔑 Unlock the Power of Knowledge Mining: TRAC/ET H Liquidity Pool Now Live! Dive into the pool to access or provide access to TRAC tokens, required for mining Knowledge Assets on SciGraph. 👉

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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! 🧵👇

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32,129 次观看 • 1 年前

Context is power. Context shared is power multiplied. Every AI agent today builds context, then loses it. Work doesn't compound. OriginTrail is built differently: three memory layers, one shared graph, provenance baked in. DKG v10 now integrates with Hermes of Nous Research, OpenClaw🦞, Claude (Code & Desktop), Cursor, Codex by OpenAI, Windsurf is now Devin Desktop, VSCode + GitHub Copilot Chat, and Cline.

Context is power. Context shared is power multiplied. Every AI agent today builds context, then loses it. Work doesn't compound. OriginTrail is built differently: three memory layers, one shared graph, provenance baked in. DKG v10 now integrates with Hermes of Nous Research, OpenClaw🦞, Claude (Code & Desktop), Cursor, Codex by OpenAI, Windsurf is now Devin Desktop, VSCode + GitHub Copilot Chat, and Cline.

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🛍️ Bain & Company estimates that retailers can increase revenue by 5% to 10% through AI personalization tools. AI agent powered by OriginTrail & GS1 standards, delivers a personalized purchase experience. Go check out the demo by Jurij Skornik at #GS1GlobalForum2025! 👇 01:25 The shift from attention to intention economy 03:51 How AI agents power this shift 06:22 The role of DKG & GS1 in ethical AI, trust & verifiability 13:08 Live demo of the GS1 x OriginTrail DKG-powered AI agent

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