Miners are already finding our testnet and loving the mining experience! The best part is this is Level 0… @MaxSebti called us out at exploit for being too careful with what we were planning to allow miners to do. We agree. So we’ve moved that from a roadmap item, to a pre-launch item. Here’s a look at how we’re getting there 👇 Testing for exploits at every level with our in-house adversarial agent before we move up. New model architectures will be Bittensor miners making real Physics AI breakthroughs for real Engineering teams. 🔥

If you'd like to understand the initial services Carbon will offer, there's a dedicated customers page on our website. carbonphysics.ai/customers

Carbon investors, future miners and researchers, we'd love to have you in our Telegram group 🫱🏽‍🫲🏼 t.me/carbonphysicsai

We’re putting Bittensor to work on industrial problems, building the evidence to show customers what Physics AI can deliver. The initial eight challenge portfolio we're building out has been published on our website, check it out 👇🏼 carbonphysics.ai/portfolio

Graphite has ingested 3000 papers today. Graphite is Carbon's in-house testing agent built to adversarially push the limits of every Carbon challenge before it hits mainnet. It works through Carbon the way a miner would, so we can see what construction freedom and the scoring do under real use.

What is Physics AI? What is Carbon's output and why does it matter? Miners design Surrogate Models using Neural Operators for specific high value Engineering Simulation tasks. Validators grade them against Traditional Solvers and Physics Laws. That's a lot. What does it mean? Let's break that down. Traditional Solvers (The Physics Calculations): They are solving the exact mathematical equations of nature (like fluids, heat, or stress) from scratch. It is highly accurate but incredibly slow and requires massive computing power for every new scenario. Surrogate Model (A Shortcut): This is a simplified "look-alike" model trained on data from past physics simulations. It is a fast, localized approximation. Neural Operators (The System Learner): This is Surrogate Model that uses advanced AI to learn the entire continuous field of physical behavior. Instead of just learning a few data points, it learns the underlying equations (PDEs). This allows it to make instant predictions across completely different designs because it learned to map the function space used to get from A to B. Engineering Simulation (The Virtual Testbed): This is where these models are put to work inside a digital environment. It is the software pipeline where engineers subject a design to real-world forces to analyze its performance, safety, and durability before anything is physically built. Carbon models are different than traditional AI models. They're built for physics. They're going to unlock serious real world value when put into the right hands. Powered by $TAO

We wrote this a couple of months ago for 10 followers, thought it was worth resharing now, our Carbon thesis.