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 29th era of Affine Champion now. While champions iterate fast, benchmark performance keeps raising the bar AFFINE-XXIX vs. Qwen3-32B baseline: • SWE-REBENCH 10.5 • SWE-MULTI 9.0 • HUMANEVAL 8.5 • MCP-AGENT 1.7 • BBH within tolerance

That is what we are building, and the miners on our subnet are the engine. The incentive mechanism turned an open, adversarial competition into a model that beats the giants at its job. Point the same mechanism at the next task and it does it again.

The miners did this. We benchmarked Score 'Skills' against foundation models and frontier chat VLMs under the fairest protocol we could design. Result → alone on the accuracy-latency Pareto frontier, near real time on a laptop-class CPU, while frontier models need a datacentre to do worse. The incentive mechanism works. The frontier moves on Bittensor $TAO.

We've talked a lot about how our efforts to train AI to shop will be entirely open source. Through Bittensor, we're committed to that ethos. We're excited to share our pre-print on arXiv, our code, our data and our entire post-training pipeline. Huge shoutout and thanks to @JarrodBarnes in helping us leverage this very valuable data. This is how AI is going to learn to shop.

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.

This is why we're building Exploit. Because the future of AI is being decided right now. Not by you. Not by us. By three companies with closed doors. Bittensor was built to change that. Exploit is where we choose how 👇

Today we’re releasing Quasar-Preview! Our first public proof that the Quasar architecture works at real scale. [ 18B MoE - 2B active / 5M context ] Built with Loop Transformer Quasar attention Trained on Bittensor through decentralized infrastructure 👇

This week, @const_reborn speaks with @sebyrubino #SN46 @zipcodenetwork building accurate property appraisal models on Bittensor. SN46 Miners submit models ~30 days before evaluation; evaluation uses properties listed and sold within the last 30 days - ensuring models are tested on data they couldn't have seen Novelty Search E076 >> Live via Bittensor Discord

NOVA Conviction Update Incentive Alignment We didn't choose drug discovery because it was an easy or fast problem to solve. We chose it because it's one of the hardest and most meaningful problems AI can tackle. Our ambition and track record reflects our commitment to long-term value creation. Since launching, NOVA has shipped 3 incentive mechanisms, established 3 relevant partnerships with other biotech companies outside of the Bittensor ecosystem, provided open-source access to optimized and fine-tuned SOTA models, run ongoing screening campaigns across multiple therapeutic modalities, and much more. That work has been funded by token sales and it will continue on multi-year timelines regardless of short-term price action. As the conviction mechanism rolls out across Bittensor, we want to be transparent about how we're approaching it by locking company tokens and aligning founder incentives with subnet growth. Company Wallet — 50% Locked Even before conviction, we've always balanced self-financing with token health. We locked 50% of our company tokens formalizing that commitment (and we’re one of the top subnets in % and TAO equivalent). From here, the owner's cut will be automatically locked so our conviction will only grow over time. The liquid 50% funds operations through token sales and OTC, and gives us flexibility to navigate price volatility. We will continue to minimize price impact and optimize for token health. Founder Incentive Alignment We're allocating 20% of what we earn split across the four founders as a direct incentive. Each founder has real skin in the game, individually motivated to grow NOVA. Based on the current wallet distribution, each founder's individual position will remain below the top 10 holders after tax. This distribution naturally decentralizes stake across the team. Our upside is tied directly to yours. Built for the Long Term We came to Bittensor to build infrastructure that accelerates drug discovery. The problems we're solving have the potential to reshape how medicine is made with multi-year timelines and billions in implications. Long term thinking is baked into everything we do. Long-term thinking is baked into everything we do. We remain focused on: • building valuable discovery systems • advancing therapeutic development • expanding partnerships • improving incentive mechanisms • and creating lasting value for the network Thank you to everyone helping us build. This is only the beginning.

Cloudflare reported that agent traffic surpassed human traffic for the first time. That's exactly why we're making Cacheon docs agent-ready. Every page now has a clean markdown source, an LLM-friendly text endpoint, and one-click handoff to ChatGPT, Claude, Cursor, and more.

This is the current state of Bittensor-based, end-user-facing products on Apple Silicon: Right now, @IOTA_SN9 and @actualinc are the only working examples. @IOTA_SN9: Train LLMs and help @MacrocosmosAI turn years of research around decentralized training into a real business, while earning competitive incentives. @actualinc: Run local models and cluster any kind of compute. It is Architecture-agnostic and you can pool inference speed and VRAM across any kind of compute and say goodbye to API credits and subscriptions by gettingyour work done locally. You can hire other people’s clusters or enable mining and get your own cluster hired.

Proof of Talk: done ✓ Bittensor showed up loud. Now comes Exploit - our conference. Our room. Our rules. 1,000 builders and breakers in Montréal. Sept 28–29. Phase 1 tickets close in a month! Don't sleep on it 👇

bittensor:native is the printing press of intelligence. 21 million tokens mined with useful ai work. the world will tap into Bittensor’s decentralized compute & intelligence like a faucet intelligence will be commoditized the way information was after gutenberg this is one of the greatest asymmetric opportunities of all time

My agents now have a shared brain that I can optimize??? Yea… that’ll do it.

Bittensor Ecosystem Highlights of the Week #64 // SUBNET UPDATES & ACHIEVEMENTS ➤ @MacrocosmosAI SN9 They launched the first stage of Project Orion and unveiled their early pretraining run of Orion 100B, which reaches over 65% of data center training efficiency. (bit.ly/3RNR04M) @const_reborn successfully orchestrated a decentralized training run in 5 minutes for 5 TAO on @IOTA_SN9. (bit.ly/4fuTleC) ➤ @TargonCompute SN4 They announced Targon Tower Pro, which allows anyone to buy high performance computing hardware. (bit.ly/4e6zDDM) ➤ @webuildscore SN44 They announced that TaoWeave invested in @manakoai and partnered with them on North American commercialization. (x.com/webuildscore/status/20…) ➤ @LeadpoetAI SN71 They shared that Leadpoet has begun paid pilots with a large publicly listed enterprise software company and an AI compute infrastructure platform. (x.com/LeadpoetAI/status/2062…) ➤ @zipcodenetwork SN46 Their new website is now live. (x.com/zipcodenetwork/status/…) ➤ @chutes_ai SN64 The Chutes platform currently earns around $280K for every trillion tokens that move through it. (x.com/chutes_ai/status/20614…) Chutes is now a provider on @AntSeedAI. (x.com/chutes_ai/status/20628…) ➤ @chutes_ai SN64 x @desearch_ai SN22 Chutes Search is now powered by Desearch AI. (x.com/chutes_ai/status/20621…) ➤ @QuasarModels SN24 SN3 is now training Quasar models, starting with a 10B model built on the new Quasar architecture. (x.com/QuasarModels/status/20…) ➤ @b1m_ai SN105 They showed they could transfer 10 GB from R2 to Internet to R2 in 21 seconds on a distributed network. (x.com/b1m_ai/status/20623295…) ➤ @oroagents SN15 They announced the next stage of their subnet: an automated post training pipeline that should help them build the best product for AI shopping. (x.com/oroagents/status/20614…) ➤ @green_compute_ SN110 In their first month on Bittensor, they generated over £82,000, so they are doing $8,200 in token buybacks. (x.com/green_compute_/status/…) ➤ @qBitTensorLabs SN63 Just 14 hours after launch, their miners were able to produce a generalized solution for breaking RSA 340. (x.com/qBitTensorLabs/status/…) ➤ @inference_labs SN2 They shared Sertn’s Model Art, which helps visualize what needs to be done to improve a model. (x.com/inference_labs/status/…) ➤ @trishoolai SN23 They announced that they have been accepted into the Google for Startups Web3 Program. (x.com/trishoolai/status/2062…) ➤ @vocence_bt SN78 They shipped new features, including a Noise Remover. (x.com/vocence_bt/status/2062…) ➤ @almanac_market SN41 They shared an update about where they’re headed, and said they will soon introduce a second incentive mechanism within their subnet. (x.com/almanac_market/status/…) // NEW SUBNET ➤ @EndureNet SN30 Endure is a new “decentralized risk intelligence network” on Bittensor. (x.com/EndureNet/status/20614…) // BITTENSOR ECOSYSTEM ➤ @proofoftalk This week, Proof of Talk took place in Paris, with a full section of the event dedicated to Bittensor and its subnets. (x.com/taomedia_/status/20622…) ➤ @TAO_dot_com The Tao(.)com wallet is now available on Android. (x.com/TAO_dot_com/status/206…) ➤ @mentatminds They released their new Sum of Mining Subnets (SoMS) index, composed of productive mining subnets that don’t burn 100% of their emissions. (x.com/mentatminds/status/206…) ➤ @taostats You can now check out the full table of which subnets have conviction on Taostats. (x.com/taostats/status/206149…) // PODCASTS & ARTICLES ➤ @TAO_dot_com Episode 13 with @Carrot_____1 and @KeithSingery (x.com/TAO_dot_com/status/206…) ➤ @MacrocosmosAI interview with their AI Research Lead, Dr. Alan Aboudib. (x.com/MacrocosmosAI/status/2…) $TAO

Micaela @micaelabazo, CEO of NOVA (SN68) at PoT: "We're part of the most important investment category, potentially the most rewarding, because our success will not just be measured in dollars, euros, or $TAO, but also HUMAN LIFE." 📽️ Credit: @proofoftalk @SubnetSummerTAO

Proof of Talk, Louvre - pro snaps - Vol 1