Bittensor (TAO)

$TAO Momentum: Still Following $BTC? Short answer: yes. The structure still trades under Bitcoin’s gravity. BTC just lost the 4H EMA20 and EMA200 zone around 70k–71k and momentum accelerated downward. When Bitcoin loses both trend averages at the same time, liquidity usually seeks the next support before any real stabilization. TAO shows the same behavior on the lower timeframe. Price broke below the 1H EMA20 and EMA200 cluster near 182–183 and quickly pushed into the 176–178 support area. The RSI on TAO is now deeply compressed, which signals short term exhaustion but not structural reversal. Momentum right now is reactive to BTC direction. For the weekend the chart suggests a simple path. If BTC continues drifting toward the 66k–67k liquidity zone, TAO likely revisits the 170–172 demand pocket, where buyers previously stepped in. If BTC stabilizes and reclaims the 70k region, TAO quickly rotates back toward the 182–186 equilibrium zone. Right now the market is asking where Bitcoin decides to stop.

2026 is the year of the autonomous agent. On the Bittensor network, AI agents are economic actors. Utilizing the x402 payment protocol, agents can now trade specialized insights between subnets ...paying for their own compute and data in $TAO without human intervention. We're already seeing subnets like @handshake_58 and @TPN_Labs creating these agentic rails. We're going to continue to see the evolution of a decentralized economy where machine intelligence is the primary currency and the most productive thoughts earn the most stake.

https://t.co/MLwRSRAAR6 $TAO

A Bittensor $TAO subnet can run more than one task. Like Horizontal scaling inside a subnet. Subnet Mechanisms lets a single subnet run multiple incentive competitions at the same time. Instead of launching new subnets, builders can now scale inside one subnet economy. Team can run multiple business units inside the same subnet economy Example: Subnet • Mechanism 0 → Model training • Mechanism 1 → Data filtering Both share the same: • miners • validators • UID • alpha token Subnet owners can split emissions between mechanisms however they want: 60% training 40% data Or any other distribution. Miners can even earn from multiple mechanisms at once. This turns a subnet from a single AI market into a multi-product AI economy. One subnet. Multiple incentive engines. One alpha token. Bittensor will keep evolving. $TAO

Bittensor | $TAO can have up to 32,768 subnets Hard-coded max every subnet gets its own unique ID from 0 to 32,767. Basically means the network has massive room to grow with thousands of different specialized AI markets running in parallel. Think bigger. $TAO #Bittensor https://t.co/TT318lazEM

Reed’s Law playing out in real time. Network effects don’t just grow users…they compound innovation. The more subnets collaborate like @babelbit & @affine_io , the stronger $TAO becomes.

fully filled on $TAO

🚨 The $TAO engineers solving one of the hardest problems in machine learning, NOT a trillion-dollar lab but a Bittensor Subnet @IOTA_SN9 @Apex_SN1 Part 2 of 2(first post re-posted below): Problem 4: The backward pass has to match the forward pass. In neural network training, data flows forward through the network, then gradients flow backward to update the model. The backward path must follow the exact same route as the forward path. So every routing decision made on the way forward creates a constraint on the way back. If you split a batch across multiple machines going forward, you have to recombine it correctly going backward. This makes the routing and scheduling dramatically harder than normal network engineering. It's not just moving files around. It's coordinating a live mathematical process across hundreds of machines over the internet. What shipped and what's coming. They just released a new communication layer already built and tested. Expected improvement: 20-30% faster activations per second. But they intentionally kept the rest of the architecture unchanged for now. They want to measure the impact of faster transfers separately from the routing improvements. The next release targeted within the next month will introduce the optimized routing and overhead elimination. That's where the big performance gains come. They're also running multiple training configurations simultaneously. Different model sizes, batch sizes, miner counts, sync strategies. Every Train at Home user might be contributing to a different experimental run at any given time all to help the team figure out what system design produces the best convergence. Why this matters for IOTA and Apex holders. What @MacrocosmosAI is solving is not incremental. This is the foundational research that determines whether decentralized AI training actually works at scale. Every big lab in the world trains models in a single data center because distributed training over the internet is considered too hard. Engineers are proving it's possible and they're doing it in production with real participants on consumer hardware. If they crack this and the loss curves in the session suggest they're getting closer with every run, the implications are enormous. Any model, any size, trained by anyone, anywhere, without needing a billion-dollar data center. That's a complete industry shift. And it's happening on Bittensor. On SN9. Right now. While most people aren't watching. Massive respect to @mccrinbc and @Felix_Quinque for going deep on the technical details in public. This is the kind of transparency that builds real conviction. Full session: linked on @MacrocosmosAI's profile. Worth every minute. $TAO

What started as a simple idea slowly turned into real infrastructure. Somewhere between early sketches, protocol experiments, and long nights debugging, @Nametensor happened. One decision shaped everything: no off-chain registry, no backend control. If identity exists on @bittensor, it should follow the same principles. So we built NameTensor to be: Fully on-chain. Deterministic. Permanent. Free from discretionary control. Every domain registration is recorded directly on Subtensor. Read the story: https://t.co/rhR0DKCzTo TL;DR: built something for $TAO.