Bittensor (TAO)

$TAO Grid Spot Bot Why this bot looks bad and is actually performing pretty badly. The original goal here was simple: accumulate more $TAO using a DCA-style grid strategy. But I was expecting an expansion move sooner, and I was wrong on timing. Because of that, the grids didn’t generate profits yet. Right now I’m sitting on 23 TAO and about a temporary ~$200 unrealized loss. Still, I’m keeping the bot running because I expect $TAO could test the $255 area soon. Another possible scenario is a move toward $210–220, which would already be outside this bot’s current range. If we drop below $240, I’ll pause, wait the next move and rebuild the grid with a new range until I get the outcome I’m targeting: accumulating more $TAO through DCA while harvesting grid profits along the way.

🚨 $TAO BITTENSOR PROPOSAL A few hours ago, there was a live meeting on Discord with Bittensor founder Const and our amazing and committed community. I took notes on the most important points and I’m leaving them below. Here are the key takeaways. Bittensor philosophy; -Const reinforced that the protocol has always “rolled with the punches” and learned from exploits since the very beginning. Every failure helped improve the chain, refine incentives, and move closer to building functional decentralized AI markets. -Const acknowledged Sam’s betrayal, took responsibility, and offered direct apologies to affected holders. (You are not the one to blame. But apology accepted.) Main proposal: Locked Stake (Conviction Mechanism) A new staking dimension is being introduced. Beyond normal stake, time now becomes part of the equation. Stake can be locked for months or even years. This creates a public and cryptographically verifiable conviction metric: locked stake multiplied by remaining lock time. Subnet owners, and any holder, will be able to demonstrate long-term commitment transparently. Objectives of the mechanism; -Prevent rugs and sudden exits that drain liquidity and harm investors -Give Alpha and TAO holders more influence over the direction of subnets -Replace human trust assumptions with mathematical, protocol-level guarantees -Make subnet ownership dynamic and competitive. Today it is immutable. How rollout will work; -First step will be the lock mechanism itself, without hierarchical governance yet -Initial testing will happen in mature subnets like 3, 39, 81 and possibly other large ones -New subnets will have a grace period (immunity) -During governance activation; -Anyone will be able to lock stake and vote on the owner key -The highest conviction wins Benefits for the ecosystem; -Increases investor confidence and helps capital formation -Teams can signal real skin in the game through locked stake -Improves long-term alignment between owners, miners, validators and stakers -Opens the path for stronger governance at subnet level and later at root/chain level Long-term vision; -Bittensor wants to solve real governance problems that no one in Web3 has fully solved yet. Not just degen cycles and pump mechanics. -The Conviction mechanism is seen as a key step toward building a real federation of decentralized and trustworthy AI companies. My thoughts; After the Covenant episode, this feels like the beginning of a full transparency phase. Now it’s time to implement, improve, evolve and keep building. The best builders will come out stronger from this moment. Holders miners validators teams owners Everyone moves forward from here. I hope so. I strongly recommend going directly to the original source on the official Bittensor channel. Don’t be lazy. 😅

🚨 $TAO’s @MacrocosmosAI just dropped a major new research paper on arXiv. SN3 showed the world decentralized frontier training was possible. SN9 may be about to make that look like the warm‑up round. (Without the Drama) This is about turning the public internet itself into a viable training cluster for frontier AI. Real research. Real benchmarks. Real state‑of‑the‑art results. ResBM: Residual Bottleneck Models. This is the core compression architecture now powering IOTA Subnet 9 @IOTA_SN9 on Bittensor. The problem it solves is massive. Today, frontier AI training needs thousands of GPUs inside private data centers connected by 300 Gb/s InfiniBand. That infrastructure costs billions and keeps frontier training locked inside a handful of companies. The world’s biggest AI models are trapped inside private super‑factories. The hardest part has always been the handoff between machines. Each GPU has to pass intermediate information to the next one instantly. Inside big labs, those handoffs move on highways. Across the public internet, they move on side roads. That speed gap is why decentralized pipeline training usually breaks. Older methods tried compressing those handoffs, but too much signal got lost and training quality collapsed. Some got to 100x compression, but only with fragile custom optimization tricks. ResBM changes that. Instead of forcing compression as a weird side process, the compression is built directly into the model itself and trained naturally end‑to‑end. That makes it far closer to something usable in production. Now the proof. They reduced the data moving between GPUs by 128x. A full moving 📦 to a small ✉️ Yet the receiving machine still gets enough useful signal to keep training almost perfectly. Final score: 21.77 vs 21.75 against the uncompressed baseline. Basically identical model quality. This was tested on a 2B parameter model trained over 26B tokens. So this is not some made up benchmark. At 100x compression, it actually performed slightly better than baseline: 21.60 vs 21.75. Then the real‑world speed test: On a normal 80 Mbps home/office internet connection, ResBM delivered 12.8x higher throughput than normal distributed training at the same bandwidth. Communication per step dropped from 112 MiB to 896 KiB Like from a Short HD video clip to small photo size. That massive reduction means the network no longer chokes. And the cost? Only 3.3% more parameters. Tiny tradeoff. Huge unlock. The paper’s biggest takeaway says it best: Pipeline‑parallel pretraining is no longer fundamentally limited by inter‑stage communication. Training serious frontier models across the internet is starting to look real. Across distributed compute. Across Bittensor. Across idle GPUs around the world. Every internet‑connected GPU just became more useful to large‑scale training than it was before. This is what Bittensor was built for. Open infrastructure producing real science that pushes AI training beyond the walls of private data centers. @MacrocosmosAI @IOTA_SN9 $TAO 🔗https://t.co/b0Ud9WKe25

If I’m being honest, the low $TAO price is the last thing I’m thinking about. All of the real opportunity is within dTAO. It’s a safe haven for Bittensor stakers to freely trade companies amongst themselves A truly undervalued subnet could easily deliver 3x gains here 👀 $TAO

$TAO The price has reached deep support and formed the expected additional low. There is no confirmation of a reversal. A break above $282 would be required to indicate that a local bottom may be in place. Until then, the pressure is pointing to the downside. https://t.co/Kh2EICE9OJ

Facts.