Urați o seară bună întregii familii Cât despre E Zy, așteaptă electricitatea O jumătate de zi a avut loc o pană de curent așteaptă 😭😭😭 .... @quipnetwork

Bună ziua întregii familii Miercuri dimineață fericită Frații îl lăudă pe Payx și se simt prea lacomi și sunt pusă permanent pe pauză haizzzzzz @quipnetwork

One thing I like about @LLMTUNE_IO is that they don't see themselves as just another AI fine-tuning platform. Their vision goes beyond that. As the AI ecosystem becomes increasingly fragmented with more models, providers, and standards, LLMTune is focused on building the infrastructure that connects them all. That's why they're expanding beyond FineTune Studio with products like Inference Studio, Agent Hub, and most recently, their participation in the FAT Protocol. FAT (Fungible Agent Tokens) is an open standard that allows AI agents to become verifiable, ownable, and tradable on-chain assets. I think this is a very interesting direction. If millions of AI agents are going to operate across different blockchains and AI models in the future, the biggest challenge won't just be building a more powerful model. It will be creating the infrastructure that allows all of them to work together. And that's exactly the mission LLMTune is pursuing.

Ce vrea cel mai mult AE să vadă astăzi. a lucrat câteva luni, k egal cu 1 Payx X AR TREBUI SĂ TE GÂNDEȘTI LA COMPANIA K BROTHERS?

I recently read @axisrobotics post about what they're building, and one idea really stood out to me. The most interesting part isn't the task generation engine, the simulation platform, or the data processing pipeline. It's how they think about data. In AI, data is often treated as a resource. The more data you have, the better your models can become. But every resource is finite. Large language models have already consumed much of the publicly available internet. Eventually, making better models will require entirely new sources of data. For Physical AI, the challenge is even greater. A robot can't learn how to grasp objects, open doors, or organize a workspace by reading books or browsing the web. It can only learn through interaction with the world and the data generated from those interactions. That's why I believe the real competitive advantage won't be who owns the most data, but who can generate new, high-quality data faster than everyone else. This is what makes Axis Robotics interesting to me. They're not simply building a platform where contributors complete tasks. They're building a complete data generation loop. • A task generation engine creates thousands of task variations from a single prompt. • Contributors collect demonstrations or correct robot behavior in simulation. • A processing pipeline validates, cleans, and augments the collected data for training. • Once updated policies are deployed, every failure reveals what data should be collected next. Each cycle doesn't just improve the model. It improves the system's ability to generate the right data for the next cycle. That's why Axis calls it a Compounding Data Engine. To me, that's the real moat. Not having the best robot today. But building an infrastructure that continuously identifies knowledge gaps, generates the data needed to fill them, and makes the next generation of models even better. When data generation itself becomes the advantage, model improvement becomes a continuous process. And I think that's what Axis Robotics is really building.