Bittensor

Mining SHA-256 hashes was the most profitable computation of the last decade. $BTC Mining digital intelligence will be the most profitable computation of the next decade. $TAO

Never Give Up bitcoindeaths.com

🏔️ Ridges Q2 Roadmap Since launching Ridgeline, the team has been deep in the work, refining the subnet and improving agent performance. Our Q2 roadmap outlines the next phase of that progress focused on evaluation quality, infrastructure, and scaling autonomous software engineering. More to come.

We needed to run trusted workloads on untrusted host machines. So over a year ago, we started building the Targon Virtual Machine to enable Confidential TEEs in production. Today we're sharing our white paper written alongside @intel: Decentralized Compute on Untrusted Hardware Using Intel® TDX and Encrypted CVMs

Security first mindset. Crucible integrates directly with Ledger, so your keys stay cold while Smart Allocator keeps you exposed to high-performing subnets. Automated rotations. Continuous rewards. Long-term alignment with the network.

Subnet tokens will come to Robinhood and Coinbase eventually. Probably. For the Bittensor protocol to truly go mainstream, every day people are going to need a way to buy $TAO and subnet tokens. Right now, it involves a relatively complex process requiring a specialized Bittensor wallet (like the one offered by our pals at Crucible Labs!) But soon, you’ll probably be able to acquire a piece of these projects directly from popular exchanges like Robinhood and Coinbase. How close are we to this long-awaited day? cc: @Jason, @Lons, @MarkJeffrey, @shibshib89, @CrucibleLabs

For anyone trying to understand Bittensor from first principles, this lecture is a useful place to start. Presented by Bittensor co-founder @const_reborn. Learn Bittensor > Start with Bitcoin, distributed systems, incentives, > How Bitcoin leads to Bittensor Subnets coordinating AI infrastructure. Topics: // Start - Bitcoin as more than a digital currency // Risks of AI centralization + closed systems // "The incentive computer" // How Bittensor subnets work (mining, validating) // How distributed AI infrastructure could scale globally // Impact on students, builders & future founders Recorded at the National University of Singapore Computer Science Club. @NUSComputing Chapters - Bitcoin, AI, and Bittensor - Bitcoin history and decentralization - AI changes how engineers work - The danger of centralized AI power - Why most crypto visions fail - Bitcoin as the world’s largest compute network - Bitcoin as a market for compute - The idea of an “incentive computer” - Bitcoin compared to Bittensor - Classroom example of decentralized scoring - A simple subnet example - SN62 :: @ridges_ai SWE agents - SN3 @tplr_ai :: Distributed AI Training - SN52 @lium_io :: GPU rentals on Bittensor 128 subnets, some examples Why this matters for the future of work Q&A Subnet examples mentioned @ SN64 - Serverless + TEE Compute :: @chutes_ai SN8 - Prop firm @VantaTrading SN52 - AutoML :: @gradients_ai SN62 - SWE agents :: @ridges_ai SN51 - Compute / GPU rental @lium_io SN4 - TEE compute for enterprise :: @TargonCompute SN3 - 72B Distributed Training run :: @tplr_ai SN41 - Prediction markets :: @almanac_market SN44 - Computer Vision @webuildscore SN68 - Drug discovery :: @metanova_labs SN18 - Weather Forecasting @zeussubnet SN50 - Bitcoin prediction data :: @SynthdataCo SN61 - Quantum computing :: @qBitTensorLabs SN14 - Bitcoin mining pool :: @taohash SN34 - Perp Dex :: @0x_Markets SN17 - 3D model generation :: @404gen_ SN33 - Data analytics :: @ReadyAI_ SN19 - [Since relaunched] RPC infrastructure :

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The only thing holding back @Bittensor from being ranked as a top 5 cryptocurrency by market cap is that 95% of crypto holders are degenerate idiots. This changes the moment the financial mainstream realizes there are only two tokens that represent freedom from enslavement.

On the @theallinpod this week, @chamath asked @nvidia CEO Jensen Huang about decentralized AI training, calling our Covenant-72B run "a pretty crazy technical accomplishment." One correction: it's 72 billion parameters, not four. Trained permissionlessly across 70+ contributors on commodity internet. The largest model ever pre-trained on fully decentralized infrastructure. Jensen's answer is worth hearing too.

One of the sharpest independent analyses of Covenant-72B we've seen. The author identifies what they call a "cognitive arbitrage window": crypto investors see "another open-source model" and shrug, while AI researchers who understand the benchmarks don't follow crypto. That gap produced a 2-day price lag after the announcement before the market caught up. The piece is thorough on the technical details (SparseLoCo compression, the honest benchmark gap to Qwen2.5/LLaMA-3.1, why the trajectory matters more than any single number) and places Covenant-72B in the full history of decentralized training from GPT-JT to INTELLECT-1. Google Translate handles it well if you don't read Chinese.

🚀 When @JosephJacks_ took a closer look at what Ridges was building, it quickly became clear something meaningful was happening and that’s when the decision to partner with the subnet was made. Ridges is delivering 73.6–87.8% on SWE-Bench Verified and a 96.3% Polyglot score, placing it among the top coding agents on the market while costing just $29/month for ~100 PRs (~$0.29 per PR). Beating the benchmarks while making high-performance coding agents accessible. As Joseph put it: “We’re harnessing a decentralized, open-source intelligence network that gets more competitive and more capable over time by design. That’s a compounding advantage that no closed, proprietary system can replicate.”

Bittensor carries forward the original promise of crypto that began with @Bitcoin Bitcoin frees humanity from the grip of fiat money Bittensor marries that revolution with AI, thus freeing intelligence from technocratic tyranny. Both aim to end slavery.

This Decentralized Cloud is Thousands of Times Cheaper Than AWS x.com/i/broadcasts/1…

"AI" is a stack of components working together: When these layers integrate, you get the systems people call intelligence. 128 sub-networks make up Bittensor. Each subnet is a market-based factory, coordinating miners to produce a digital commodity and rewarding the best outputs. These commodities can include: Compute, distributed training, fine-tuning, inference, data, labels, synthetic data, evaluations, ranking, RL environments, agents, vision, audio, storage, privacy tooling, VPNs, search, forecasting, and prediction markets. This weekend at @frontiertower, builders at the @FundingCommons hackathon will be designing new subnets for the sovereign AI infra stack.

I have 6 agents running now, two are doing ML research, one is cloning Claude code, two trade hyperliquid and one mines Bittensor. All they need is compute from Bittensor (Lium, Basilica and Targon) and inference from Chutes and Openrouter. The trading agents pay for the compute and inference by buying credits with proceeds. I could literally die and this system wouldn’t turn off. Indeed it’s programmed to just get better.

Confidential H200s are available on Targon.com ☁️ → Rentals starting from $1.90/hr → On Demand or Long Term → Cryptographically Secure Build and Deploy with fully Secure TEEs on Targon Virtual Machine ⚡️

We just completed the largest decentralised LLM pre-training run in history: Covenant-72B. Permissionless, on Bittensor subnet 3. 72B parameters. ~1.1T tokens. Commodity internet. No centralized cluster. No whitelist. Anyone with GPUs could join or leave freely. 1/n

Since the last Bittensor Track at Proof of Talk 2025: @tplr_ai completed the largest decentralised LLM pre-training run in history: Covenant-72B. @ridges_ai beat SOTA on the industry standard benchmark for AI coding agent performance, scoring 73-88% on SWE-bench Verified. Built for under $10m in TAO emissions, through open collaboration and competition. @chutes_ai became the number one open source model provider on Hugging Face. 35 trillion tokens served. 715,000 users. None of it was VC funded. No CEO signed off. It just ran. June 2-3 - The Louvre, Paris Bittensor Track at Proof of Talk

🏔️Introducing Ridgeline Behind the product is SN62 Ridges on Bittensor, where miners compete to build and improve the agent itself. Agents are continuously evaluated and scored, driving rapid iteration and capability improvements. Ridgeline is the product layer where those agents are put to work. • Assign a GitHub issue to the agent • It reads the repository for context and begins work • Generates and tests patches automatically • Your agent works in parallel to you, completing tasks without supervision • It’s like having another team member you can assign tickets to Inference runs across Bittensor infrastructure (e.g. @TargonCompute and @chutes_ai), while performance is measured on correctness, speed, and cost. Sign up now → shorturl.at/F0Tvt During the open beta, there’s no paywall, and new users receive 10 free credits to get started.

Recorded @TheTaoPod with @jaltucher yesterday so we hopped on and mostly talked about quantum biology. Dig in, folks. QB is precisely how we evolve our species … where free will becomes indistinguishable from “natural selection”.