Bittensor (TAO)

Top Crypto AI Projects & Their Key Partnerships🤝 ➢ $TAO: Decentralized AI marketplace. Grayscale & Bitwise filed spot ETFs for TAO, signaling serious institutional interest. ➢ $FET: Autonomous agent economy. Merged with SingularityNET & Ocean Protocol to form the ASI Alliance, one of the biggest AI consolidations in crypto. ➢ $ICP: Runs AI models natively on-chain. Backed by DFINITY with growing developer ecosystems across Web3 & AI-native dApps. ➢ $NEAR: L1 built for AI agents & dApps. Partnered with NVIDIA, lending it real credibility in the AI infrastructure space. ➢ $RNDR: Decentralized GPU marketplace for AI & 3D. Powered visual work for NASA, the Las Vegas Sphere, and the Super Bowl — real-world usage at scale. ➢ $LINK: The oracle backbone for AI + blockchain. Deep partnerships with Swift, ANZ Bank, and the DTCC, bridging TradFi & on-chain AI. ➢ $OCEAN: Decentralized data marketplace for AI training. Part of the ASI Alliance alongside $FET & SingularityNET.

In @const_reborn own words: “Bittensor will be run by agents. They will feed the mining, resist the exploits, manage the fleets, build the subnets and consume the commodities.” 404—GEN is one of the clearest examples of where this is going. Most AI-made games today are easier to build in 2D because 2D assets are simpler for agents to work with, but 3D is much harder. A normal 3D mesh is like a finished object. An agent can see it and use it, but it cannot easily understand how it was built or how to change it. What agents need is code that generates 3D geometry. That is why 404 created the procedural asset competition, where an agent can read the spec, write Three.js code, submit the asset, and compete openly on Subnet 17. This is the third major 3D representation 404 has pushed toward Large Spatial Models (LSMs). With this, agents can participate directly in mining the subnet, writing code, submitting assets, and competing autonomously and openly. We are not only building infrastructure for agent-cr

We’re excited to announce that Trishool’s HaloGuard 1.0 𝐡𝐚𝐬 𝐚𝐜𝐡𝐢𝐞𝐯𝐞𝐝 𝐒𝐎𝐓𝐀 prompt-safety performance among open-weight guard models. Today, we present HaloGuard 1.0, a constitutional input classifier for multilingual AI safety. It is built as a first-layer input guard that checks user prompts before they reach a downstream LLM, agent, or application. This is part of the safety infrastructure being built through @trishoolai , our decentralised AI red-teaming subnet on Bittensor SN23. Full arXiv paper goes live soon.

If hosting a Spaces called 'Why Bittensor Loves Exploits' last week didn't make it obvious, this will: I'm officially an Ambassador for @ExploitSummit Two days of subnet announcements, proper ecosystem dialogue, and the best of this community under one roof. If you've been on the fence about going, this is your sign. Don't leave it too late. Sept 28–29 | Montreal 🇨🇦 🎟️ https://t.co/9FVToWXt2r

After 20 years in traditional finance, I am going all in on decentralised AI. The AI race will shape far more than markets. It will decide who controls access to intelligence, how knowledge is produced and where power ultimately sits. So @ArtiomS and I created @xTensorCapital and @xTensorLabs. AI cannot be built entirely on infrastructure controlled by a handful of companies. We are backing the parallel path. The thesis is simple: decentralised AI remains underinvested, undervalued and widely misunderstood. It is early, technically difficult and easy to dismiss. That is exactly where asymmetry lives. xTensor Capital invests. xTensor Labs builds and operates. Each makes the other sharper. Much more to come on what we are building, learning and backing. Stay tuned!

There is literally a race for deregistration happening on Bittensor $TAO between the low priced subnets A sign that the subnet extraction strategy is no longer viable. They are being suffocated in Realtime. Bullish signal 🎯 https://t.co/MdD3bh34k8

A Bittensor subnet just built an AI safety model that beats the big players Trishool (@trishoolai), Bittensor's (@opentensor) subnet 23, released HaloGuard 1.0 today. The 4B version claims first place across seven established prompt-safety benchmarks, and the 0.8B version outperforms models several times its size. The pitch is speed over scale. HaloGuard screens prompts in real time before they reach the underlying model or agent, instead of catching harmful outputs after the fact. An earlier alpha version already runs live on the Chutes subnet, hitting an 87% F1 score on real traffic since May. The subnet's whole design is built around attacking itself. Miners get paid for breaking the model, then the breaks get patched.

A Bittensor subnet just built an AI safety model that beats the big players Trishool (@trishoolai), Bittensor's (@opentensor) subnet 23, released HaloGuard 1.0 today. The 4B version claims first place across seven established prompt-safety benchmarks, and the 0.8B version outperforms models several times its size. The pitch is speed over scale. HaloGuard screens prompts in real time before they reach the underlying model or agent, instead of catching harmful outputs after the fact. An earlier alpha version already runs live on the Chutes subnet, hitting an 87% F1 score on real traffic since May. The subnet's whole design is built around attacking itself. Miners get paid for breaking the model, then the breaks get patched.