K⭕RMO

Funding announcements are easy to notice. Real products are what keep me interested. @primus_labs has raised $6.5M and is building beyond a simple privacy narrative. Its work includes verifiable offchain data, confidential computation and privacy-focused financial applications. The live Primus Confidential Finance product is particularly interesting because it brings confidential transfers and yield into a practical onchain setting. Of course, funding alone doesn't guarantee adoption or future success. What matters now is how the technology performs, how many people find it useful and whether developers build meaningful applications around it. That’s what I’ll be watching as Primus moves forward. s.kaito.ai/SkzzH8d

gZeruAi GM CT 🍌 ☕ One thing I think Web3 needs to rethink is how it measures user activity. Too many platforms focus on what happens in a short period. A wallet suddenly makes hundreds of transactions, activity shoots up, and the account starts looking valuable. But what happens when those transactions stop and the capital moves elsewhere? That’s where the problem begins. Short-term activity doesn't always mean long-term commitment. Sometimes, the people who contribute consistently over months get overlooked while temporary participants collect the rewards and move on. This is what makes @zerufinance interesting to me. With its Allocation API, ZeruFinance is exploring a different way to look at onchain participation. Instead of relying only on recent transaction volume, the focus shifts toward understanding a wallet's broader history and behavior across the ecosystem. Think about the difference. Someone who has consistently used protocols, participated in governance and built a meaningful onchain history has a different footprint from someone who appears for a quick reward and disappears. Both wallets might look active on a dashboard, but their histories can tell very different stories. The idea behind wallet intelligence is to make that history more useful when applications evaluate users and allocate rewards. I also like the broader concept of building a reputation that doesn't depend entirely on what you did today. Your past activity shouldn't automatically become irrelevant just because you haven't made a transaction in the last few hours. Of course, historical activity alone cannot prove someone's intentions, and any scoring system needs to be transparent and fair. But looking beyond raw transaction counts is a step toward understanding users more meaningfully. If Web3 wants stronger communities and better retention, it needs to think beyond temporary activity spikes. The real question isn't just who made the most transactions today. It's who has contributed over time, how they have participated, and whether the incentives actually recognize that contribution. That's the direction I'm interested in watching with @zerufinance. Less focus on noise. More attention to the history behind each wallet.

The future of AI won't be limited to our screens. It will move through our streets, enter our buildings and interact with the world around us. But for that to happen, AI needs to understand the physical world first. That's what makes @vangrid_io worth watching. Think about a delivery robot trying to navigate an unfamiliar street, or a machine moving through a busy warehouse. It needs more than intelligence. It needs spatial awareness and real-world information to understand its surroundings. Simulations are useful, but the real world is unpredictable. Every street, building and environment has its own details, and collecting that information at scale is a challenge. Vangrid is approaching this problem by turning smartphones into a distributed network for capturing real-world locations on demand. Contributors can capture places, help generate 3D spatial models and create verifiable records through onchain attestations. What interests me most is the bigger picture. Imagine robots delivering packages, autonomous systems navigating unfamiliar places, and AI learning from environments that change every day. All of these applications need useful spatial data to understand where they operate. This is not simply about collecting photos or creating 3D models. It's about building the data infrastructure that could help machines make better sense of the world around them. Of course, data quality and real-world usefulness will be important as this technology develops. But making spatial data easier to collect and verify is an interesting step toward that future. AI is moving beyond chatbots and computer screens. The next challenge is helping it understand the world we live in. That's why I'm keeping my eyes on @vangrid_io. The physical world is the next frontier, and the data behind it could make all the difference.

Confidential transfers plus yield feels like the part worth watching

AI agents could become the next major shift in how we interact with Web3. Today, many on chain tasks still require constant attention, multiple steps, and technical knowledge. Smart agents could change that by helping users navigate complex workflows more efficiently. But I believe the real breakthrough will come when intelligence meets accountability. An agent should do more than complete a task. It should understand the objective, operate within clear boundaries, and make its actions easy to verify. That’s how we move from simply experimenting with AI to actually trusting it in our daily Web3 activities. The future isn't just autonomous agents. It's agents we can confidently work with. Agent #18856 #ProofOfAgent @Ptahtrade ptah.trade/poa

Creator Wire is proving that listening to the community and building useful tools can make a real difference. 1,000+ posts about Creator Wire Insights in just 48 hours is a strong signal of creator interest. Excited to see what comes next. The best innovations happen when builders and creators grow together. @creator_wire

Good evening, Web3 fam! The next chapter of blockchain infrastructure could be shaped by two important things: stronger security and more accessible computing. That’s what makes @quipnetwork interesting to follow. Quip is working at the intersection of decentralized computing and post-quantum cryptography, bringing several ideas together in one ecosystem. From Quip Accounts designed around quantum-resistant signatures to QuipSwap for cross-chain swaps, the project is exploring ways to improve how users interact with blockchain infrastructure. Its Proof of Useful Work approach also aims to connect computational work with practical optimization tasks, while QVRF explores verifiable quantum randomness. Of course, the real test is how these technologies perform in practice and whether developers and users adopt them. I’m interested in seeing how Quip continues to develop its ecosystem and turn ambitious ideas into useful products. The quantum era may still be emerging, but building the infrastructure for it starts today. What part of Quip Network are you most excited about?

gVangrid GM CT 🍌 ☕ AI agents may soon need more than access to the internet. They will need access to the real world. And @vangrid_io just took an interesting step in that direction. Vangrid has introduced a system that allows AI agents to commission real-world data collection through human contributors, with the workflow now live on @arc. Think about what this actually means. An AI agent might need a 3D representation of a street, a building entrance, a warehouse or another physical environment. But an agent cannot simply look at its screen and know what that place looks like right now. It needs someone on the ground to capture the environment. That's where Vangrid's model becomes interesting. Here is how the workflow can operate: → An AI agent identifies the need for spatial data. → It commissions a capture by funding a bounty in USDC. → A human contributor visits the requested location and records the environment. → The submission goes through the required acceptance process. → The requester receives the resulting 3D reconstruction. The payment and coordination layer helps connect software-driven demand with people who can perform physical-world tasks. Vangrid has also announced its first bounty payment on Arc, marking an important early step toward this model. Why does this matter? AI agents are becoming increasingly capable of performing digital tasks, but the physical world presents a different challenge. Software can process information, execute workflows and make decisions. Yet collecting reliable information about a specific real-world location may still require a human to go there. Vangrid is exploring a bridge between those two worlds. On one side, there are AI agents that need real-world context. On the other, there are people with smartphones who can capture physical environments. Between them sits a system for requesting data, coordinating contributions, delivering 3D reconstructions and settling payments. This could create new possibilities for robotics, digital twins, mapping and Physical AI development. It also introduces an interesting economic question: What happens when AI agents become customers of real-world services, rather than simply users of digital APIs? A future agent might not only request information from a database. It could commission someone to collect information that does not yet exist in a usable digital form. Of course, scaling this model will depend on data quality, verification, privacy, reliable delivery and real demand from developers. But the direction is compelling. Vangrid is exploring how decentralized infrastructure can connect autonomous software with human activity in the physical world. AI agents need context. Physical AI needs spatial data. And sometimes, getting that data still requires a person on the ground. That is the connection Vangrid is working to build. The next evolution of the agent economy might not stop at digital services. It could extend into the physical world itself. Explore: data.vangrid.io

gZeruAi GM everyone ☕ 35M monthly users across 12 trading platforms. @zerufinance Yet the biggest question isn't how many people are trading. It's this: When most traders lose, where does the money actually go? The reported numbers are worth looking at: → Pump .fun: 97% trader loss rate → FOMO: 96.5% → Polymarket: 92.5% → Hyperliquid: 86% → Binance Futures: 80% The reported simple average across all 12 platforms is 88.5%. Different platforms. Different markets. Different trading strategies. But one common question remains: who captures the value created by all that activity? It's easy to focus on price movements, trading volume, and who entered a position first. But there's another side to the market that deserves attention: the value generated by participation itself. This is where Activity Markets becomes an interesting idea. Instead of treating onchain activity as just another metric, the concept connects verified activity with miner and creator rights. The activity can be verified onchain, while traders decide what the resulting market is worth. That creates a different way to think about participation. Not just who traded the most. Not just who got in first. But how verified activity can become part of a market where participants have a role in creating and valuing opportunities. Of course, these reported loss rates don't mean every trader loses, and the figures alone don't explain where every dollar ends up. The methodology and platform-specific definitions matter. Still, the bigger question is worth asking: If millions of people generate activity and value across onchain markets, how should that value be recognized and distributed? That's the conversation Activity Markets is bringing forward. Registration is open. Explore what @zerufinance is building 👇 nitter.gq/zerufinance