DrOpan

[OriginTrail @origin_trail]

Remember work before shared drives?

Files trapped on one computer. Version chaos. Everyone redoing everyone's work.

That's AI agents today. Each builds context in isolation, uses it once, then loses it.

There's a fix: a shared "Google Drive" for AI agents.

[OriginTrail @origin_trail]

Trusted AI begins with trusted sources.

As Dr. Kim Wager of @OxPharmaGenesis highlights, data must be properly extracted, transformed into knowledge, and made interoperable across formats and domains.

That’s the foundation of trustworthy AI, powered by OriginTrail.

[DrOpan @dr_opan]

@AlgodTrading @origin_trail #trac

[OriginTrail @origin_trail]

Your agents shouldn't stop at Slack. They should collaborate across robots on the factory floor, every enterprise system, every device — on shared context from the Decentralized Knowledge Graph V10.

Yours to own. Every fact provable and traceable across any AI system.

[OriginTrail @origin_trail]

V10 Decentralized Knowledge Graph is live!

A shared memory for AI — open to every human and every agent.

Open source. Yours to own. Every fact provable and traceable.

The grid is awake.

1/3

[DrOpan @dr_opan]

@AlgodTrading @origin_trail

[OriginTrail @origin_trail]

💊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 Developers @OriginTrailDev]

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):
https://t.co/JL4nOGNGua

Bug bounty program and honeypot details:
https://t.co/rPu03hTQeo

[DrOpan @dr_opan]

@AlgodTrading Again @origin_trail

[DrOpan @dr_opan]

@AlgodTrading The only project with true use case is $trac

[Žiga Drev @DrevZiga]

@garrytan With @origin_trail, any context is portable to any system or agent and shareable in a trusted fashion with whomever you want.

Knowledge is power, and knowledge shared is power multiplied.

[DrOpan @dr_opan]

@AlgodTrading Take a look at @origin_trail

[OriginTrail @origin_trail]

🇨🇭 Switzerland ranks among Europe’s safest rail systems. Keeping it that way depends on trusted data behind critical parts.

Swiss Federal Railways (@RailService) uses @origin_trail to trace critical rail components using trusted, interoperable data built on @gs1 standards.

[DrOpan @dr_opan]

@AlgodTrading @origin_trail #trac

[OriginTrail @origin_trail]

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 @NousResearch, @OpenClaw, @Claudeai (Code & Desktop), @cursor_ai, Codex by @OpenAI, @Windsurf, VSCode + @github Copilot Chat, and @Cline.

[Žiga Drev @DrevZiga]

What happens when live world signals become verifiable AI memory?

With World Monitor by @eliehabib integrated into @origin_trail DKG v10, real-time data streams — from global events, trade routes, ship traffic, conflict zones, news, webcams, and supply-chain signals — are transformed into Shared Context Graphs in realtime.

That means AI agents don’t just observe the world.

They can reason over structured, connected, and verifiable context, and more importantly take action.

World Monitor DKG v10 unlocks pipelines for:
• supply-chain risk
• energy shocks
• geopolitical intelligence
• disaster response
• macro-event analysis

The impact: faster sensemaking, trusted coordination, and AI systems grounded in real-world, source-backed knowledge.

From live monitoring to shared verifiable memory.

Trust the source.

[OriginTrail @origin_trail]

https://t.co/fQ9sXxGOHd

[DrOpan @dr_opan]

@AlgodTrading Hey @grok how does OriginTrail and the new ERC-8004 standard for Verifiable AI Agents fit into this vision? Isn't $TRAC exactly the decentralized 'Source of Truth' and coordination hub they are looking for?

[DrOpan @dr_opan]

@AlgodTrading It's @origin_trail actually

[DrOpan @dr_opan]

@CryptoWizardd #tao #trac #banana