It's what everyone's talking about... so at TaoStats we have put out a guide for the new proposed "Root Reborn initiative". Three sections... a basic TLDR: s3.hippius.com/rufus/public/… a full explainer: s3.hippius.com/rufus/public/… a visual guide: s3.hippius.com/rufus/public/…

Centralized AI companies like @AnthropicAI are more vulnerable to government intervention. Decentralized AI technology like $TAO @opentensor offers an alternative. Bittensor provides open source, permissionless access to AI through a decentralized global network. Following the suspension of Anthropic's AI model, $TAO rallied sharply, climbing 30% in just 12 hours. Read more on this from @LowBeta on the Stack: grayscale.com/the-stack/anth…

root is coming back, and it's going to be glorious

Been talking to real word potential customers and they are amazed at how much they can save. Ethereum traffic incoming - miners get ready.

Bittensor Ecosystem Highlights :: June 8–14, 2026 SUBNET ACHIEVEMENTS [ @chutes_ai - SN64 ] @jon_durbin shared a draft of the Parallax tech report, outlining a MoE training method to reduce per-participant VRAM and FLOPs. > bit.ly/3RYSpFM Chutes also became a launch partner for Respan’s new AI Gateway. > bit.ly/4aAdpJd [ @QuasarModels - SN24 ] Quasar released Quasar-Preview, its first public Quasar model trained on Bittensor: 18B MoE, 2B active and 5M context. > bit.ly/4ekxl3U Quasar is preparing a 10T-token decentralized training run on SN24, starting with a 5T-token phase to produce a stronger checkpoint. > bit.ly/3RZb7gv [ @oroagents - SN15 ] ORO shared its arXiv pre-print, code, data and post-training pipeline for building shopping agents from SN15’s open agentic shopping traces. > bit.ly/43vW3cG [ @webuildscore - SN44 ] Score showed how its 19MB vision model beat larger AI models on object detection while running much faster on CPU. > bit.ly/4os0FtX They also added new comparison pages on @manakoai against ChatGPT, Claude, Roboflow, SAM 3 and other vision AI tools. > bit.ly/4vMuCY7 [ @vidaio_ - SN85 ] Score is partnering with Vidaio to bring vision AI challenges to SN44 and make video archives searchable and actionable. > bit.ly/4ekAbWC [ @yanez__ai - SN54 ] Yanez partnered with Nexartis, an identity and trust infrastructure company, to help verify human, AI model and agent activity across digital transactions. > bit.ly/4eshDUp They also shared in their latest AMA that Yanez has generated $300K in 2026 sales, with an active pipeline over $1M and 11 clients. > bit.ly/4v9VUYq [ @trishoolai - SN23 ] Trishool was accepted into Anthropic’s Claude Partner Network. > bit.ly/4uUf5EZ [ @affine_io - SN120 ] AFFINE-XXIX beat the Qwen3-32B baseline on SWE-Rebench, SWE-Multi, HumanEval and MCP-Agent benchmarks, while staying close on BBH. > bit.ly/4xrRA8D [ @VantaTrading - SN8 ] Vanta Trading crossed 2000 users after launching free $1k eval accounts and cutting prices by 55% across all challenges. > bit.ly/3QGKOv5 [ @SwarmSubnet - SN124 ] Swarm announced SOTApilot, an open-source AI drone autonomy model with 95.34% success on its UAV navigation benchmark. > bit.ly/4opsiUi [ @blockmachine_io - SN19 ] Blockmachine launched Ethereum RPC. > bit.ly/4fGe67h [ @TrajectoryRL - SN11 ] TrajectoryRL is expanding SN11’s skill competition from skill packs to miner-submitted finetuned models. > bit.ly/4eoTA91 [ @heydittoai - SN118 ] Ditto reached 1000 users. > bit.ly/4gkB7Na [ @theminos_ai - SN107 ] Minos has run over 37,000 variant-calling evaluations on chromosome 21, with submissions improving by 10.21% on average. > bit.ly/4vO9noW [ @minotaursubnet - SN112 ] Minotaur launched its website and opened beta access to its DEX Aggregator. > bit.ly/447GpEq [ @ReadyAI_ - SN33 ] ReadyAI launched a revenue dashboard showing real-time demand for SN33’s structured data pipeline. > bit.ly/4epth2q [ @say_gm_ - SN28 ] Good Morning published the roadmap for its AI gateway running in a TEE, now live on testnet with mainnet beta next. > bit.ly/4gkzGOQ [ @EndureNet - SN30 ] Endure is integrating @SynthdataCo's forecasts into its DeFi risk engines. > bit.ly/4v5Remt [ @eirel_ai - SN36 ] Eirel released its first product, offering deep research, image generation, web search and agent tools across multiple model families. > bit.ly/4otVg5I [ @adtao_ppcrebel - SN21 ] @dsvfund took an OTC position in the SN21 alpha token. > bit.ly/4eHTs5G SUBNET LAUNCH [ @DeSciClaims - SN111 ] Claims is launching as SN111 to build a claim-evidence graph that turns scientific literature into machine-readable data for AI reasoning. > bit.ly/3SlCUaT PODCASTS & ARTICLES @opentensor Novelty Search hosted by @const_reborn with @zipcodenetwork > bit.ly/3SmDlli @TAO_dot_com Episode 14 with @Carrot_____1 and @KeithSingery > bit.ly/3QCA9S6 @gordonfrayne podcast with @josercaldera from Yanez > bit.ly/4vOufwf @gordonfrayne podcast with @knakamor from Vocence > bit.ly/4493MgY @AltcoinMillie podcast with @MaxScore from Score > bit.ly/3QGDX4L @AltcoinMillie podcast with @zeussubnet > bit.ly/4uBcNKu @TAO_dot_com article “The Impact of Conviction” > bit.ly/4esgBI1

People are finally waking up to the importance of #Bittensor.. In 2009, #Bitcoin was born from the abuse and failure of a centralized financial system Today.. we’re watching the same thing happen with centralised intelligence #Bittensor is our best shot to give power back to everyone Different era.. same fight $TAO

Unstoppable, uncensorable, global decentralized AI seems like a good investment bet to make. The “Bitcoin of AI” so to say…

Access to intelligence should not depend on a handful of companies or governments. This is why open, decentralized, permissionless AI matters. This is why Bittensor matters.

The US Government has given #Bittensor the best endorsement it could ever ask for They proved the entire thesis for why we need permissionless and decentralised AI Anthropic were forced by the Government to suspend access to Fable and Mythos models for any foreign nationals.. It doesn't matter what you think of Anthropic morals.. what the Governments reasons were.. whether the model was dangerous or not.. The point is.. Closed AI can be switched off We’ve been banging this drum in #bittensor for four years now If frontier intelligence can be restricted.. it will be restricted.. If access can be gatekept.. it will 100% be gatekept If a small group of powerful men and women can decide who gets intelligence and who doesn’t.. they will absolutely use that power This is exactly why bittensor:native matters Intelligence is just too important to have it sit behind permissioned walls controlled by governments and corporations #Bittensor is the fight against this.. Open.. permissionless.. incentivised True open intelligence built by the people.. for the good of the people This Anthropic news is proof that those fears are no longer theoretical.. This shit is real.. the door just got slammed The pure reason #bittensor exists.. is so the world has another one to walk through bittensor:native

Its more important than ever to see $tao succeed. Expect criticism, expect praise but most important lets make sure it progresses Aside from being critical we hired multiple people from frontier labs investing 6 figures in our own little compute cluster Actions matters and doubling down to have a meaningful impact

And just like that we collectively saw the future of inequality

gm. you've been asking what we're building. time to pull back the curtain a little. the roadmap is public - your prompts are not. saygm.com/#roadmap

Quasar is entering its next chapter on Bittensor SN24. We are moving toward a 10T-token decentralized training run. The idea is simple Quasar Models needs more useful training, not just bigger parameter counts. Real model quality comes from tokens, data quality, training direction, and the ability to keep improving checkpoint by checkpoint. This run starts with a 5T-token phase to produce a stronger checkpoint, then continues into another 5T-token phase, reaching 10T total trained tokens. SN24 will set the direction: the starting checkpoint, the data, the training recipe, and the evaluation system. Miners become the extra compute layer. They help Quasar train faster, improve continuously, and move forward together. this would be the largest token-scale training runs ever attempted in decentralized AI. This is how we scale Quasar. Help us train it 👇

Week 4. The big one: → Parallax went public. Whiteboard video Jon's technical report at chutes.ai/parallax.pdf → Finished a full migration to a locked-down cluster. TEE nodes everywhere, no public IPs, less overhead → Now a provider on AntSeed → Chutes Search runs on Desearch (SN22) → Conc3pt Squad won AI Marathon 2026 with Jobest, an AI recruiter platform built on Chutes Full breakdown below 👇 $TAO

Case in point... THOU SHALT NOT COMPETE. - AI/LLM development generally - distributed training - pretraining pipelines - ML accelerator design - cybersecurity - chemistry Already disclosed as nerfed for the plebs. What's next on the lobotomization list?

Cool, but I prefer optimization competitions where you own the company rather than work for it.

Here's a draft of the tech report on the model training method I've been experimenting with, "Parallax". chutes.ai/parallax.pdf TL;DR: MoE models' params are mostly routed experts, and you can massively reduce VRAM and FLOPS per participant by splitting up those experts. You can also offload the expert training to commodity hardware further saving compute/VRAM per island. The crazy cool thing about these sketches is, you can actually onboard workers nearly instantly (sync time with ternary weights is a few MB), and they never need to download or stream the raw datasets (sketches contain all the work they need to do and are tiny). You'd probably want the first couple layers of the model in your own infra if you had sensitive data because otherwise you could do gradient inversion attacks to reconstitute the raw text, but beyond the first couple layers and not knowing which layer/expert you're training I think it's infeasible so privacy is pretty baked in. Decoupled DiLoCo/RDA-diloco style backbone sync, surrogates for non-owned routed experts with low rank updates to sync those, tiered sync cadences for various components, "sketches" to offload expert work, etc. 20b tested two different ways, plenty of small model iterations, and 176b params just to prove out feasibility. There are hundreds of additional experiments and loads of data we could also highlight as well, but the guts are there. Variations: - freeze routed expert weights instead of using surrogates, eliminates adam state, backward pass, etc., though you'd need different sync methods vs. low rank updates to the surrogates (the surrogates are already tiny, rank-8 updates to those even smaller) - hierarchical parallax, i.e. each node itself becomes an rda diloco style multi-learner which then syncs with the outer/global islands (the point here is to enable GPUs without NVLink/etc. and reduce GPU<=>GPU comms to maximize MFU on less-capable/commodity GPUs) - pipeline parallelize each island itself such that each island can decompose the backbone etc.

Now we are cooking. 🔥 The largest demand for RPC services comes from #Ethereum devs ... time to dip into a huge customer market $ETH

Old world: giant data center, racks of GPUs, power/cooling/water/land, $200M gatekeeping. Parallax world: distributed GPUs, regular machines, no training data exposed to workers, same time target with ~82% less hardware/resources. Proof of Parallax. $TAO