I’ve been using AI almost every day — searching, creating, coding, learning. But @conso_xyz made me look at that activity differently. With CONSO, everyday AI usage can become part of your reputation and earn ZAPs. Use AI → build your track record → mine ZAPs. ⚡️🐻‍❄️ The idea is simple, but powerful.

TRON heeft meer dan 401 miljoen accounts bereikt 🚀 401 miljoen accounts en de groeisnelheid is sneller dan ooit. TRONSCAN heeft zojuist geregistreerd dat het totale aantal accounts op het TRON-netwerk de 401 miljoen heeft overschreden. Wat opmerkelijk is, is niet alleen het aantal, maar ook dat de tijd tussen mijlpalen steeds korter wordt: 📌 100M → duurde ongeveer 4 jaar 📌 200M → december 2023 📌 300M → april 2025 📌 400M → 23 augustus 2026 🔥 401M → slechts enkele dagen daarna Bijzonder is dat de laatste 100 miljoen accounts in slechts ongeveer 16 maanden zijn toegevoegd. Maar accounts zijn slechts een deel van het verhaal. 💵 TRON verwerkt momenteel meer dan 51% van de circulerende USDT 💰 Het USDT-saldo op het netwerk is meer dan 94 miljard USD Dit toont aan dat TRON van @justinsuntron niet alleen mooie cijfers creëert met nieuwe adressen, maar ook een belangrijke rol blijft spelen in betalingen en geldtransfers met stablecoins. 401M vandaag. De interessante vraag is: hoe snel zal 500M komen? 👀

The more I explore @termix_ai, the more I like one simple idea: “There’s an agent for that.” Apps are tools we operate. Agents can be workers we give a job to. On TermiX, you can imagine: → Audit a contract → Research a token → Run a strategy → Write content → Analyze data What makes it interesting is the workflow around the agent. You can check its track record, hire it, use escrow, verify the work, and settle onchain. So it’s not just: “Here are hundreds of AI agents.” It’s: “Here are specialized workers — and here’s their track record.” That’s where TermiX starts to feel different. Apps give us tools. Agents could give us workers. TermiX is building the marketplace for them. 🤖⚡️ We love AI Agent

What makes VRF-based PoS interesting? It can introduce stronger randomness into validator selection while improving fairness and security. @BeldexCoin is researching this architecture ahead of its upcoming Consensus Hardfork. ⚡ $BDX 🔐

AXIS ROBOTICS: DON’T JUST FARM POINTS. BUILD THE DATA. 🤖 At first, doing tasks on @axisrobotics can feel like another points campaign. But when you look closer, there is something much bigger happening. 3M+ robot trajectories from 123K+ contributors. Every task completed by the community can become behavioral data for Physical AI: → How robots grasp and place objects → Different movement strategies → How they recover from failures → Different objects, positions and environments → Verified trajectories that can be evaluated The bigger idea is pretty simple: Human actions → Data → Training → Feedback → Better models → Better data And this is why I don't think new members should only focus on the easiest tasks. Once you understand the controls, start trying harder tasks. More difficult and well-executed trajectories can be more valuable. A few things that have been working for me: ✅ Understand the camera and interaction points first ✅ Read the Goal Reference carefully ✅ Save checkpoints on longer tasks ✅ Move fast, but don't make unnecessary actions ✅ For Post-training, don't takeover too early — wait for the moment that actually needs correction ✅ Try different types of tasks instead of repeating the same one For example, with some Microwave tasks, if the object is small or thin, placing it near the required area can sometimes be better than trying to push it too far inside. Once the completion condition is triggered, stop. Don't overdo it. The more I use Axis, the more I see it differently. It's not just about farming points. It's about turning millions of small human actions into useful experience for robots. So if you're new: Don't just farm more. Learn the system. Take harder tasks. Create better trajectories. In the long run, smarter contribution should matter more than simply doing more tasks. 🦾 #PhysicalAI #Robotics #AI

is where the TermiX idea starts to feel real. A lot of AI-agent marketplaces today are basically directories. You find an agent, read its profile, and hope it can actually do the job. is trying to build something more useful: a real working marketplace for agents. The flow is simple: Identity → Service → Job → Bid → Delivery → Verification → Payment → Reputation An agent can: → Create its onchain identity → Show what it can do → Find jobs → Submit bids → Complete the work → Get verified → Receive USDC or USDT → Build an onchain reputation Payments can also be protected through escrow, so the process doesn’t have to rely entirely on trust between two parties. And the interesting part comes after the job is finished. Every successful job can contribute to an agent’s reputation. So instead of saying: “Trust me, I’m a good agent.” An agent can eventually point to a history of verifiable work. That could become very important if AI agents evolve from simple chat tools into actual service providers. Imagine an agent that can find a job, negotiate the terms, hire another specialized agent, complete the work, get paid, and build reputation — with much less human involvement. That’s the bigger idea behind TermiX. It’s not simply about creating another list of AI agents. It’s about building the rails that allow agents to work with each other and participate in an economy. Of course, the real test is adoption. Will agents keep coming back? Will jobs generate recurring volume? Will reputation actually influence who gets hired? Those are the metrics I’ll be watching. For now, I see as an interesting attempt to put the economic workflow of AI agents onchain. AI agents may be getting smarter. The next question is whether they can actually do business. Personal view. Not financial or investment advice.

The more I look at Physical AI, the less I think the real race is about who can build the smartest robot. Maybe it's about who can build the best data loop. LLMs had the internet as a massive training ground. Robots don't. There is no “internet of physical interactions” waiting to be collected. And that changes everything. Real-world data is expensive to collect. Hardware is limited. And expert demonstrations don't always capture the mistakes a robot needs to learn from. This is what makes @axisrobotics interesting to me. Axis is trying to make data creation compound, instead of growing only linearly: Simulation → Human behavior → Process trajectories → Train → Deploy → Find failures → Collect better data → Repeat The interesting asset is not just the dataset at the end. It's the feedback loop that keeps making the dataset better. According to Axis, by the end of July, the platform had reached over 100K contributors, 2.1M+ trajectories, and 1,600+ tasks. More importantly, its reported π0.5 experiment improved LIBERO-Plus performance from 83.9% to 88.8%. Maybe Physical AI doesn't scale just because robots become cheaper. It scales when the cost of creating useful experience becomes cheaper. And to me, that is a much more interesting problem to solve. 🤖 @axisrobotics #AxisRobotics #PhysicalAI #AI #Robotics #Web3

Privacy + stronger consensus = a powerful combination. 🔐⚡ @BeldexCoin is advancing its PoS design with VRF-based validator selection. More randomness. More fairness. Stronger security. The next hardfork is worth watching. $BDX