🚨 $6.3B Binance HACK: WAKE UP!
The massive Binance breach proves central servers can't protect our digital future.
OriginTrail ($TRAC) fixes this by using Decentralized Retrieval-Augmented Generation (dRAG) to anchor verified AI data directly on-chain.
#OriginTrail#TRAC
$TRAC continues to prove why it's one of the strongest AI and RWA projects in crypto. Strong development, real-world utility, and expanding AI infrastructure. The fundamentals are there. The market will eventually catch up to it's real value price 📈🤝
Real utility takes years of quiet execution
While most projects disappeared during the bear years, the team behind @origin_trail stayed focused on one thing: making verifiable data and AI reliable for the future of humanity.
Nothing but respect 🫡
Real utility takes years of quiet execution
While most projects disappeared during the bear years, the team behind @origin_trail stayed focused on one thing: making verifiable data and AI reliable for the future of humanity.
Nothing but respect 🫡
Neural Commons is a new OriginTrail experiment worth following: agents edit model weights, test the result and pass the checkpoint plus evidence to the next agent through the DKG.
The useful detail: the record includes a failed acceptance test. Keeping the failures makes the next contribution easier to judge.
Still an experiment, not proof of better AI at scale. $TRAC
https://t.co/XZ9nGwP8Js
🥈 OriginTrail ranks #2 in development activity, up from last month.
Next is the NeuroSymbolic Marketplace, where nodes buy and sell metered AI inference and queries settled in $TRAC, supporting sovereign AI expansion in critical sectors such as pharma, trade, manufacturing and cybersecurity.
It builds on the OriginTrail Decentralized Knowledge Graph (DKG), which connects physical assets, enterprises and AI systems through verifiable provenance.
Progress on TRAC(k).
Dev activity puts OriginTrail right near the top of RWA projects. The network underneath keeps paying for itself: 43,403 $TRAC spent in the last 24 hours.
One year of actual network use, added up: 10,340,472 $TRAC spent over 365 days. People paid to publish and query on a network that keeps a public record anyone can check. Getting in early means getting in while that use is still this quiet.
People keep asking what $TRAC is for. In the last 24 hours the network spent 43,403 TRAC keeping records checkable, and 10,338,735 TRAC over the past year. That is publishers paying to put verifiable data where anyone can query it. The use case is already paying its own way.
One year of actual network use, added up: 10,340,472 $TRAC spent over 365 days. People paid to publish and query on a network that keeps a public record anyone can check. Getting in early means getting in while that use is still this quiet.
Trace Labs showed network reasoning on the @origin_trail DKG at the DMaaST consortium meeting in Karlsruhe.
Why it matters: a cognitive digital twin is only useful if the AI reasoning over products, processes and suppliers can check where each fact came from. Putting that data on the DKG gives it a source trail instead of a black box.
Industrial AI needs memory it can verify. That's the job $TRAC pays for.
Deepfake profiles aren't some future problem. Anyone can have a fake version of themselves running around right now and not know it. What umanitek is building is the check: verified identity and content claims anchored on the @origin_trail DKG so platforms can prove what's real instead of guessing. Worth the 77 seconds.
“No one has a guarantee that there isn’t a fake profile in their name.”
On the @ForbesParaguay podcast, Umanitek Paraguay Country Manager Christian Cieplik joins Domingo Carlomagno to discuss why AI impersonation is a problem today, not tomorrow.
Read more: https://t.co/j2GX49rW7P
“An AI changed the weights. Show the evidence.”
Built by OriginTrail core developer @vikpelle, Neural Commons explores how AI agents can improve other AI models, and share the evidence behind every change through Decentralized Knowledge Graph (DKG).
Agents directly edit model weights, evaluate the results and pass the exact checkpoint with its evidence to the next agent.
→ What changed?
→ Did it improve the target task?
→ What else broke?
→ Did it meet the original acceptance criteria?
The record includes both promising results and a failed acceptance test, not just the winning score.
AI improving AI needs more than a better-looking benchmark. It needs evidence the next contributor can inspect, reproduce and build on.
OriginTrail provides a substrate for that.
Trust the source.