We’ve released Teutonic-I-Reliquary, a 9B model post-trained entirely on Bittensor using untrusted GPUs, from RTX 3090s to B200s. It beats Qwen3.5-9B on SWE-bench Verified, scoring 43.6% vs 35.1%.

Yesterday at @42born2code Paris, we announced something big 👇 Path to Subnet Owner: a 3-episode workshop series with @opentensor 01 Mining Discovery · Nov 30 02 Subnet Ownership · Dec 03 Subnet’s Mastermind · Dec/Jan Learn to mine and end up running your own subnet. 1 TAO for #1# on the leaderboard 🏆 Ep. 1 → Mon Nov 30, 2pm, 42 Paris

Robotics on Bittensor is making its way onto real factory floors. @openroboto just signed its first commercial pilot with a manufacturer in Hungary, bringing its data collection hardware into a real industrial environment.

Bittensor subnet @trishoolai just released a SOTA model for AI response safety. Their 0.8B HaloGuard 2.0 outscored Alibaba’s 4B and 8B Qwen3Guard-Stream models on average across six response-safety benchmarks.

Our India Bittensor Builders House winners are here 🏆 Five days. 20 builders. Countless ideas built on @opentensor. From developer infrastructure to subnet powered apps, we saw some exciting projects come to life. Here are the teams taking home the $1,000 USDC prize pool 👇

Every company shipping AI agents asks one question: how can I stop my agent from going rogue? To answer it, HaloGuard Output Guard now sets a new state of the art (SOTA): 83.63 average F1, ahead of the previous best at 82.5. All measured on public benchmarks. And we did it with a 0.8B model, a quarter of the size of the previous best. Small enough to run efficiently. Cheap enough to make checking every response practical. This was the focus of Phase 3 on Trishool: • 7,500+ jailbreaks submitted • 125+ unique miners • 15 weeks of challenges It comes at the right time. We’re taking our product to enterprises now, and this makes our security stack stronger. We’ll share the models on Hugging Face, the arXiv paper and a full explainer soon. There’s much more to come, and we’re looking forward to an exciting week ahead. SOTA AI security, on Bittensor. Ψ

.@Pareton_ai miners achieved up to 54% faster AI inference on @engyai’s setup, using the same GPUs. On the latest Novelty Search, Pareton explains how Bittensor’s Subnet 10 improves the software that runs AI models, with each winning optimization becoming the next baseline to beat. The team also got a miner-developed optimization merged into @vllm_project, one of the leading open-source engines for AI inference. Hosted by @const_reborn Full episode in the first comment

.@openroboto just built a decentralized alternative to @Figure_robot’s Index for collecting robot training data, with real-world capture hardware. Open robotics on Bittensor now has its data network to challenge the industry’s biggest labs. Bittensor is coming for physical AI.

A Harvard research team and @chutes_ai just released a public dataset covering one year of real-world LLM inference on Chutes: 6.12B requests across 9,174 models. Technical usage data from a Bittensor subnet is now open to the wider AI research community.

This Thursday on Novelty Search :: SOMA, SN114 @SomaSubnet is building a decentralized marketplace for AI services, starting with context compression. Miners compete by submitting compression algorithms that make AI workloads more efficient. Validators test & score miners on the balance between compression and retained performance. Thursday :: 5PM EDT / 9PM UTC Live via Youtube and X

The Hippius mobile app is live on Android. Your photos and files, backed up automatically and end-to-end encrypted on your phone before they ever upload. The same account and storage you already use on desktop and web. Your whole cloud, now in your pocket. iOS is coming next.