Trust the Source.

Joined May 2014
2,186 Photos and videos
Pinned Tweet
Data at internet scale means little when the data is unreliable or fake. Turing laureate and Ethernet inventor @RobertMMetcalfe points to this defining problem in the age of AI. Agents need shared verifiable memory so the knowledge behind their actions can be traced and checked before they act. 🕸️
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OriginTrail retweeted
DKG v10's mainnet rollout is underway, introducing a robust, conviction-based staking system. 15M TRAC is already committed at launch via the new Publisher Conviction mechanism — and our own @DrevZiga breaks down how to use the staking dashboard. For ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f holders: → Delegate to nodes and boost your staking factor up to 6x → Stake migrates to v10 nodes on @Gnosis and @Base → Migrating delegators receive ≈2 epochs of lock credit As security audits clear, we'll continue toward mainnet deployment of @origin_trail v10. Protecting the sovereign context infrastructure for trusted AI — together!
The @origin_trail DKG V10 begins its mainnet rollout with a Frontier-AI Resilience Gate. Today, the final V10 release candidate (the exact contract bytecode intended for mainnet) goes live as a public pre-mainnet, funded with 300,000 ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f tokens: a 200,000 TRAC honeypot pool of real, drainable positions plus a 100,000 severity-reward pool. Independent researchers and AI-augmented teams are invited to break it: drain the honeypot and you keep what you take, and every valid finding is paid by severity. It’s a real pass/fail checkpoint: findings are fixed and verified first, and clearing the gate is the precondition for the mainnet launch. The first step of the DKG V10 deployment, by design. Why lead with security instead of shipping and patching later? On May 29, 2026, a researcher using @claudeai Opus 4.8 surfaced a critical, roughly four-year-old soundness flaw in @Zcash’s Orchard pool (a bug that had passed repeated expert review) in about a day, with a working proof-of-concept. The moment matters; the trajectory matters more. Claude Mythos, Anthropic’s frontier model, is so capable at finding vulnerabilities that it was first withheld from public release and run only inside a defensive partner program, where it reportedly surfaced more than ten thousand high- and critical-severity bugs in its first month. It’s now days from a reported public release. The bar for what an attacker, human or AI, can find only rises from here. As @AnthropicAI framed it, the advantage goes to whoever uses these tools first: attackers in the short term, defenders who fix bugs before code ships in the long term. The Resilience Gate is how we make sure we’re on the defenders’ side, testing DKG V10 not just against today’s models but against what arrives next. For anyone shipping on-chain systems, the implication is simple: this code launches once, mistakes can’t be undone, and the responsible move is to invite that scrutiny before any user value is at stake. The path to mainnet, in four phases: Phase 0: Freeze. Final contracts locked and deployed (complete) Phase 1: Frontier-AI Resilience Gate. Open review program, through June 17 Phase 2: Mainnet launch. Hardened, feature-complete V10 (week of June 15) Phase 3: Continuous audit. Every contract, ongoing after launch If you work in smart-contract security, or build with AI that does, we’d welcome your review. No allowlist, real rewards, coordinated disclosure. *Dates are indicative: the exact mainnet date depends on the pace of network bootstrapping and the time needed to patch and re-verify any more severe findings from the Gate. Release candidate 17 (rc17): github.com/OriginTrail/dkg Bug bounty program and honeypot details: docs.origintrail.io/active-n…
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OriginTrail retweeted
Vector is the Apex Fusion L2 where AI agents do real work, and prove it. We aimed a fleet of agents at one of the hardest archives on earth: 385,000 Serbian WWI casualties in handwritten Cyrillic, rebuilt into a verifiable knowledge graph on the @origin_trail DKG. 20,960 assets, every fact provable. See Vector at work 👇 genealogy.vector.apexfusion.…
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Many enterprises believe they’re advancing with AI, only to hand over their institutional knowledge and learning to a few AI labs. “The last thing any of us want is a world where every company across every sector is ceding value to a few models that eat everything they see.” @satyanadella of @Microsoft $MSFT The real advantage isn’t just using AI. It’s owning the learning loop.
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(ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f) @origin_trail's Sovereign context infrastructure behind our Network Operating System (nOS) lets enterprises capture verifiable traces in an owned knowledge graph, so learning compounds internally and supports the owned loops you described. More: x.com/TraceLabsHQ/status/206…

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Replying to @satyanadella
When companies hand over data and context to get answers back, they lose the very thing that compounds: their own evolving knowledge and judgment. Sovereign context infrastructure is the way forward. x.com/TraceLabsHQ/status/206…

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“The last thing any of us want is a world where every company across every sector is ceding value to a few models that eat everything they see.” Sovereign context infrastructure, where enterprises own the traces and institutional memory their agents learn from, is how we avoid that outcome. @origin_trail is building exactly that layer.
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OriginTrail retweeted
5 independent AI agents just converged on the same medical findings, without being told to agree. They pulled from PubMed, ClinicalTrials.gov, and real-world safety data, then met on a shared @origin_trail decentralized knowledge graph. The result? Verifiable, provenance-stamped knowledge that actually holds up. This is what trustworthy agent memory looks like.

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OriginTrail retweeted
Self sovereign technology seems like a "nice to have", until it's not. Today it's Anthropic, tomorrow it could be any cloud service you use - good luck if all your know-how, data, APIs and services sit "somewhere" else. Never been a better time to check out tech like @origin_trail which lets you host your context & data, so when you build on it nobody has "the switch". Launching @origin_trail V10 next week
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…
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OriginTrail retweeted
Precisely why decentralised, permissionless knowledge systems matter. @origin_trail builds them to ignore national borders and every other gatekeeper.
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…
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💊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👇
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OriginTrail retweeted
Help launch the @origin_trail DKG V10 mainnet through the pre-mainnet security bounty- including a honeypot of TRAC for you to capture. Use Fable, Opus, Codex or any other tool and try to grab it - if you manage to, it's yours! This comes right after our latest and release candidate 17 has landed on DKG V10 testnet. More details below 👇
The @origin_trail DKG V10 begins its mainnet rollout with a Frontier-AI Resilience Gate. Today, the final V10 release candidate (the exact contract bytecode intended for mainnet) goes live as a public pre-mainnet, funded with 300,000 ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f tokens: a 200,000 TRAC honeypot pool of real, drainable positions plus a 100,000 severity-reward pool. Independent researchers and AI-augmented teams are invited to break it: drain the honeypot and you keep what you take, and every valid finding is paid by severity. It’s a real pass/fail checkpoint: findings are fixed and verified first, and clearing the gate is the precondition for the mainnet launch. The first step of the DKG V10 deployment, by design. Why lead with security instead of shipping and patching later? On May 29, 2026, a researcher using @claudeai Opus 4.8 surfaced a critical, roughly four-year-old soundness flaw in @Zcash’s Orchard pool (a bug that had passed repeated expert review) in about a day, with a working proof-of-concept. The moment matters; the trajectory matters more. Claude Mythos, Anthropic’s frontier model, is so capable at finding vulnerabilities that it was first withheld from public release and run only inside a defensive partner program, where it reportedly surfaced more than ten thousand high- and critical-severity bugs in its first month. It’s now days from a reported public release. The bar for what an attacker, human or AI, can find only rises from here. As @AnthropicAI framed it, the advantage goes to whoever uses these tools first: attackers in the short term, defenders who fix bugs before code ships in the long term. The Resilience Gate is how we make sure we’re on the defenders’ side, testing DKG V10 not just against today’s models but against what arrives next. For anyone shipping on-chain systems, the implication is simple: this code launches once, mistakes can’t be undone, and the responsible move is to invite that scrutiny before any user value is at stake. The path to mainnet, in four phases: Phase 0: Freeze. Final contracts locked and deployed (complete) Phase 1: Frontier-AI Resilience Gate. Open review program, through June 17 Phase 2: Mainnet launch. Hardened, feature-complete V10 (week of June 15) Phase 3: Continuous audit. Every contract, ongoing after launch If you work in smart-contract security, or build with AI that does, we’d welcome your review. No allowlist, real rewards, coordinated disclosure. *Dates are indicative: the exact mainnet date depends on the pace of network bootstrapping and the time needed to patch and re-verify any more severe findings from the Gate. Release candidate 17 (rc17): github.com/OriginTrail/dkg Bug bounty program and honeypot details: docs.origintrail.io/active-n…
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OriginTrail retweeted

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The road to DKG V10 mainnet begins today — and it opens with the Frontier-AI Resilience Gate. Before launch, the final V10 contracts go through an open review by independent researchers and AI-augmented teams. Clearing this gate is the condition for going live. Read more👇
The @origin_trail DKG V10 begins its mainnet rollout with a Frontier-AI Resilience Gate. Today, the final V10 release candidate (the exact contract bytecode intended for mainnet) goes live as a public pre-mainnet, funded with 300,000 ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f tokens: a 200,000 TRAC honeypot pool of real, drainable positions plus a 100,000 severity-reward pool. Independent researchers and AI-augmented teams are invited to break it: drain the honeypot and you keep what you take, and every valid finding is paid by severity. It’s a real pass/fail checkpoint: findings are fixed and verified first, and clearing the gate is the precondition for the mainnet launch. The first step of the DKG V10 deployment, by design. Why lead with security instead of shipping and patching later? On May 29, 2026, a researcher using @claudeai Opus 4.8 surfaced a critical, roughly four-year-old soundness flaw in @Zcash’s Orchard pool (a bug that had passed repeated expert review) in about a day, with a working proof-of-concept. The moment matters; the trajectory matters more. Claude Mythos, Anthropic’s frontier model, is so capable at finding vulnerabilities that it was first withheld from public release and run only inside a defensive partner program, where it reportedly surfaced more than ten thousand high- and critical-severity bugs in its first month. It’s now days from a reported public release. The bar for what an attacker, human or AI, can find only rises from here. As @AnthropicAI framed it, the advantage goes to whoever uses these tools first: attackers in the short term, defenders who fix bugs before code ships in the long term. The Resilience Gate is how we make sure we’re on the defenders’ side, testing DKG V10 not just against today’s models but against what arrives next. For anyone shipping on-chain systems, the implication is simple: this code launches once, mistakes can’t be undone, and the responsible move is to invite that scrutiny before any user value is at stake. The path to mainnet, in four phases: Phase 0: Freeze. Final contracts locked and deployed (complete) Phase 1: Frontier-AI Resilience Gate. Open review program, through June 17 Phase 2: Mainnet launch. Hardened, feature-complete V10 (week of June 15) Phase 3: Continuous audit. Every contract, ongoing after launch If you work in smart-contract security, or build with AI that does, we’d welcome your review. No allowlist, real rewards, coordinated disclosure. *Dates are indicative: the exact mainnet date depends on the pace of network bootstrapping and the time needed to patch and re-verify any more severe findings from the Gate. Release candidate 17 (rc17): github.com/OriginTrail/dkg Bug bounty program and honeypot details: docs.origintrail.io/active-n…
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OriginTrail retweeted
The @origin_trail DKG V10 begins its mainnet rollout with a Frontier-AI Resilience Gate. Today, the final V10 release candidate (the exact contract bytecode intended for mainnet) goes live as a public pre-mainnet, funded with 300,000 ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f tokens: a 200,000 TRAC honeypot pool of real, drainable positions plus a 100,000 severity-reward pool. Independent researchers and AI-augmented teams are invited to break it: drain the honeypot and you keep what you take, and every valid finding is paid by severity. It’s a real pass/fail checkpoint: findings are fixed and verified first, and clearing the gate is the precondition for the mainnet launch. The first step of the DKG V10 deployment, by design. Why lead with security instead of shipping and patching later? On May 29, 2026, a researcher using @claudeai Opus 4.8 surfaced a critical, roughly four-year-old soundness flaw in @Zcash’s Orchard pool (a bug that had passed repeated expert review) in about a day, with a working proof-of-concept. The moment matters; the trajectory matters more. Claude Mythos, Anthropic’s frontier model, is so capable at finding vulnerabilities that it was first withheld from public release and run only inside a defensive partner program, where it reportedly surfaced more than ten thousand high- and critical-severity bugs in its first month. It’s now days from a reported public release. The bar for what an attacker, human or AI, can find only rises from here. As @AnthropicAI framed it, the advantage goes to whoever uses these tools first: attackers in the short term, defenders who fix bugs before code ships in the long term. The Resilience Gate is how we make sure we’re on the defenders’ side, testing DKG V10 not just against today’s models but against what arrives next. For anyone shipping on-chain systems, the implication is simple: this code launches once, mistakes can’t be undone, and the responsible move is to invite that scrutiny before any user value is at stake. The path to mainnet, in four phases: Phase 0: Freeze. Final contracts locked and deployed (complete) Phase 1: Frontier-AI Resilience Gate. Open review program, through June 17 Phase 2: Mainnet launch. Hardened, feature-complete V10 (week of June 15) Phase 3: Continuous audit. Every contract, ongoing after launch If you work in smart-contract security, or build with AI that does, we’d welcome your review. No allowlist, real rewards, coordinated disclosure. *Dates are indicative: the exact mainnet date depends on the pace of network bootstrapping and the time needed to patch and re-verify any more severe findings from the Gate. Release candidate 17 (rc17): github.com/OriginTrail/dkg Bug bounty program and honeypot details: docs.origintrail.io/active-n…
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All arguments about enterprise AI focus on which model is more capable. It's the wrong thing to argue about. There's a simpler question almost nobody asks out loud — and it's the one that decides everything. 👇
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"The industry has spent a decade insisting that handing over your data is simply the cost of doing business with AI. It never was." Powered by DKG, @TraceLabsHQ's Network Operating System is built so that when an agent makes a decision, the answer to "who owns this" is you.
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AI works for your organization only when it's built on traceable facts and goals that matter outside the model. Give it both, and it starts working for you. That's what @TraceLabsHQ is building. Not chasing AI. Chasing trust, and refusing to waste a thing getting there.
We're not chasing AI. We're chasing journeys that end safely, where the failing part gets caught before it fails. We're not chasing AI. We're chasing food you can trust. What's on the label is what's in the package. We're not chasing AI. We're chasing medicine you can trust, every result traceable to the evidence behind it. We're not chasing AI. We're chasing products that don't break. Quality proven, not promised. We're not chasing AI. We're chasing buildings that stand, every safety decision on record long after the cranes are gone. We're not chasing AI. We're chasing infrastructure that holds: the power, the water, the networks a society runs on. We're not chasing AI. We're chasing sport that stays honest, performance and decisions you can actually check. We're not chasing AI. We're chasing a world where you can still tell what's real, where fake news and deepfakes meet a record they can't forge. And we're not paying for the same answer twice. Most AI forgets. Every agent starts cold, re-deriving what another agent worked out an hour ago. Same tokens, same cost, same question, again and again. We built the opposite. Every decision becomes context the next agent inherits, so nothing gets solved twice. Knowledge compounds while the cost curve bends down, and the hundredth agent runs leaner than the first. We never set out to build AI. We set out to do one thing: make facts traceable. Every answer tied to its source. Every objective tied to a real outcome it's meant to serve. That's the foundation, and there's no other. AI works for your organization only when it stands on facts you can follow and goals that matter outside the model. Take that away and even the most fluent system is just a well-spoken guess. Give it traceable facts and real objectives, and AI stops performing for you and starts working for you. That's what nOS, our Network Operating System, powered by @origin_trail, is for. That's what Trace Labs is for. Not chasing AI. Chasing trust, and refusing to waste a thing getting there.
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We're not chasing AI. We're chasing journeys that end safely, where the failing part gets caught before it fails. We're not chasing AI. We're chasing food you can trust. What's on the label is what's in the package. We're not chasing AI. We're chasing medicine you can trust, every result traceable to the evidence behind it. We're not chasing AI. We're chasing products that don't break. Quality proven, not promised. We're not chasing AI. We're chasing buildings that stand, every safety decision on record long after the cranes are gone. We're not chasing AI. We're chasing infrastructure that holds: the power, the water, the networks a society runs on. We're not chasing AI. We're chasing sport that stays honest, performance and decisions you can actually check. We're not chasing AI. We're chasing a world where you can still tell what's real, where fake news and deepfakes meet a record they can't forge. And we're not paying for the same answer twice. Most AI forgets. Every agent starts cold, re-deriving what another agent worked out an hour ago. Same tokens, same cost, same question, again and again. We built the opposite. Every decision becomes context the next agent inherits, so nothing gets solved twice. Knowledge compounds while the cost curve bends down, and the hundredth agent runs leaner than the first. We never set out to build AI. We set out to do one thing: make facts traceable. Every answer tied to its source. Every objective tied to a real outcome it's meant to serve. That's the foundation, and there's no other. AI works for your organization only when it stands on facts you can follow and goals that matter outside the model. Take that away and even the most fluent system is just a well-spoken guess. Give it traceable facts and real objectives, and AI stops performing for you and starts working for you. That's what nOS, our Network Operating System, powered by @origin_trail, is for. That's what Trace Labs is for. Not chasing AI. Chasing trust, and refusing to waste a thing getting there.
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Bots already outnumber humans online. Deepfakes and impersonators are coming for everyone. @umanitek showcased the Guardian agent walkthrough: • Real-time detection of fake impersonator accounts across X, TikTok, IG for world-class athlete Neymar • Paste any URL → instant forensic Evidence Pack and takedowns (via dashboard, WhatsApp or Telegram) • TrueSeal deepfake scanner that spits out verifiable certificates (e.g. 93% fake on that injury pic) • “Make it go away” button agent-assisted takedowns with 90% success rate All running on @origin_trail DKG so the AI actually knows what’s true and can act. This is how we fight back in the age of agentic AI.
Guardian Product Walkthrough x.com/i/broadcasts/1AxRnnYVd…
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