WOKEN | NFT

We know creators are waiting for decisions on the rewards for the terminated campaigns. Here’s an update from projects that have already reached their decision: → @superformxyz will distribute 68% of rewards proportionally to the campaign’s duration. → @On_Veera will distribute $31,250 for phase two and all rewards for phase one. → @rails_xyz will distribute 50% of rewards proportionally to the campaign’s duration. → @alturax will distribute 50% of the rewards proportionally to the campaign’s duration. → @ConfidentialLyr Phase 2 has been cancelled as it lasted a couple of days only. Phase 1 rewards were distributed. All rewards will be distributed based on the Snapshot taken on January 14th. We are updating all details here as soon as projects make decisions: cookiedao.notion.site/Snaps-Campaign…

good morning Most platforms treat trust like a feature they can bolt on later. First they publish everything, then they try to clean up the mess with moderation teams, reports, and AI filters. It’s reactive, expensive, and ineffective and users pay the price. @dagama_world flips the model entirely by making proof the foundation, not the afterthought. On DaGama, every meaningful contribution carries its own verification. When someone adds a location or posts a review, the system captures real-world context time, place, and presence. That data becomes the backbone of trust, not a badge applied later. This changes the incentives completely. There’s no benefit in spamming, faking, or manipulating because contributions are tied to actual activity in the physical world. Truth becomes easier than deception. What emerges is a living map shaped by people who were truly there. Not influencers. Not bots. Not paid hype. Just verified experiences accumulating over time. Trust doesn’t come from promises or policies. It comes from proof. And when proof is built into every action, the entire system becomes stronger, fairer, and far more useful for everyone.

T-14 days. The $DEUS Community Auction with @virtuals_io is coming fast. Jan 8 → launch.xmaquina.io

Happy Monday Guys! ☀️ Time for a deep dive into @OfficialXYO , a true giant in the DePIN revolution XYO proves that data should be trusted, not guessed. With over 10M nodes through the COIN App, XYO has become the primary bridge between the physical and digital worlds. Why is XYO different? ➥ Proof of Location & OriginReal time validation to prevent data spoofing. You can prove exactly when, where, and how data was created. ➥ XYO Layer OneMore than just a standard blockchain. It is a modular infrastructure supporting Sovereign Chains and Permanent Data Storage. Data remains secure and unaltered. ➥ Dual Token System • $XYO: A deflationary token for staking and long term network security. • $XL1: The utility token for gas fees and daily network operations. From AI and Logistics to RWA, XYO is the foundation for a verifiable future. The era of trusted data is here! gXYO ll @OfficialXYO

good night eveyone We are moving fast toward a world where AI agents manage our money, supply chains, and vehicles. But the real question is: Can we truly trust them? At @inference_labs , we aren’t just building another model. We are building the trust layer the entire AI industry relies on. We call it Auditable Autonomy. Blind trust in "black box" systems is a risk we can no longer afford. That’s why we use Proof of Inference replacing guesswork with mathematical certainty. Right now, we are deploying this vision through the Inference Network. By combining AI with Zero-Knowledge Machine Learning (zkML) and our new DSperse technology, we are splitting complex models into verifiable slices. This allows for distributed, efficient verification at scale happening live right now on Bittensor Subnet 2. This is critical for high-stakes sectors like DeFi, Defense, and Healthcare. We are removing hidden errors and manipulation to create Unbreakable AI. The future isn't about hoping the AI is right. It’s about proving it. Join us in building the verifiable future. @inference_labs

More than 800 Chicago residents have signed a petition calling for a pause on sidewalk delivery robots. They aren’t anti‑technology, they simply demand clear safety data and guarantees that the robots will not impede people with disabilities. Their concerns are entirely legitimate. When an autonomous machine rolls among pedestrians and no one knows what it is “thinking” or how it decides at each moment, it is just a wheeled black box. Public trust in autonomous AI is extremely fragile, shattering after a minor collision or a viral video. The solution isn’t to ban or dismiss the benefits (faster deliveries, reduced emissions, cost savings), but to build genuine trust through provable transparency. This is where Proof of @inference_labs shines: using mathematical proofs (zero‑knowledge proofs) to verify that the AI made decisions in accordance with committed rules, without revealing the model or any sensitive data. From today’s food‑delivery robots in Chicago to self driving cars, AI driven medical diagnosis, or tomorrow’s automated financial trading, this technology can replace blind faith with evidence based trust. When every critical machine decision is accompanied by independently verifiable mathematical proof, society will be less fearful and more ready to embrace an autonomous future. The future of autonomy doesn’t have to be frightening it only needs to be trustworthy. #AI #Autonomy #ProofOfInference

loves ai… until it starts asking for your data. that’s the tradeoff most $AI apps quietly make today. the default flow is broken @inference_labs you want an answer, so you hand over your inputs. logs get stored, models get trained, privacy gets blurred. that’s just “how it works” or at least how we’ve accepted it. @inference_labs flips that assumption. instead of trusting the model, they make it prove itself. using zero-knowledge proofs, the system can mathematically verify that an output was computed correctly without ever exposing the underlying data. no raw inputs leaked. no blind trust required. this changes the relationship entirely. you’re no longer choosing between usefulness and privacy. the model doesn’t need to “see” your data to be accountable. it just needs to prove the computation was done right. zooming out, this isn’t a feature it’s a shift in architecture. if ai is going to touch sensitive workflows (finance, health, identity, governance), “trust me bro” simply doesn’t scale. proof does. and that’s why inference labs feels less like another ai tool… and more like infrastructure for what ai has to become next.