Good evening, everyone) Recently, the @verona_dev team was at KBW, so there is plenty to talk about. I thought for a long time about where to start, and settled on the topic of from the intelligence layer to a real-world consequence. This is exactly what we discussed on the panel The AI Economy, from intelligence to the real world. Models already know how to reason. Acting with consequences is still something they cannot do. The path from a smart agent to the real economy runs through three things that LLMs do not have by default. Identity. An agent has to prove on whose behalf it is acting and what exactly it is allowed to know. Without this, a bank, insurance company, or hospital has no basis to trust it. Payment. An action in the economy is not text, but a transaction. An agent needs a way to pay and receive payment without turning every step into a manual human approval. Execution. Between the model’s response and a change in the real world (a loan, booking, prescription, contract), there has to be a fact that can be verified again. Not the most likely continuation, but proof. This is exactly where the gap is. An LLM predicts the next token. The economy requires a consequence that someone is accountable for. Until an agent can act on a user’s data without seeing the data itself, and cannot prove that it knows, it will not be allowed anywhere near a credit decision or a medical one. The intelligence layer is precisely what closes this gap: a fact is verified once, belongs to the person, and can be reused by any agent they have authorized. 🔗 verona.dev --- This post is for informational purposes only. verona:native

A little extra money changes how an ordinary purchase feels. That’s exactly what The Internet’s Reward Program by @earnos_io is about. In the ero app, rewards are earned for real actions: short brand missions, connecting accounts you already use. No points and no gift cards, the money goes straight to your balance. From there, you can transfer it to ero Visa, add it to Apple Wallet or Google Pay, and use it anywhere, whether you’re shopping at a store or grabbing a coffee. 🔗 ero.app

Why did @PrismaXai shift its focus from Live Control to selling verified datasets and on-demand collection tailored to specific tasks, scenes, and types of hands? Today I wanted to write specifically about this because I haven’t seen anyone break it down. So let’s get into it. At first, the team was heavily pushing Live Control. In real time, people controlled robotic hands: people got points, #PrismaX got data. But if you look at it this way, the buyer doesn’t need a stream of someone moving a robotic hand. They need an episode that has already been verified. That’s why the focus shifted, and they became a service layer for Physical AI. Robotics Data - a bank of already verified demonstrations. On-demand - you say what needs to be recorded, and they collect it for you. VLA Foundry remained on the other side of the same loop: operators record, review filters it, and only what passes goes into the bank. And just to avoid confusion: teleop isn’t going anywhere. It simply stopped being the showcase and became the production layer. They’re no longer selling a session. They’re selling a dataset. If you’re interested in teleop, you can join as an operator through the $99 Amplifier subscription: it gives you access to the hands and more points farming. But, as I already said, the subscription may not pay off. There are risks. If that’s not a problem - welcome: app.prismax.ai

Finally, I’ve crossed the 200-follower milestone on @DlicomApp. Of course, some might think that’s not much, but it’s a new app with only 50K+ users, so in my opinion, that’s a pretty good result. Let’s keep moving forward! Are you using the Dlicom app? If not, you can download it on your phone. Links below. 👇

Google Play : play.google.com/store/apps/d…