Openτensor Foundaτion

Incentivizing intelligence

[Openτensor Foundaτion @opentensor]

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.

[Anthropic @AnthropicAI]

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.

The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.

Access to all other Claude models is not affected.

We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.

Read our full statement: https://t.co/bwn0sximKZ

[Openτensor Foundaτion @opentensor]

.@manakoai is a strong example of what Bittensor subnets can build when they turn miner intelligence into a real product

A clean, usable vision AI agent built by @webuildscore

[Manako @manakoai]

Introducing Manako, the fastest way to turn any camera into an vision ai agent.

Go on https://t.co/2H1P4KME1z. Join our waitlist.

[const @const_reborn]

Machines of anonymous engineers the optimize inference engines faster than nation state funded labs

[Openτensor Foundaτion @opentensor]

Live on the Bittensor Discord this Thursday :: 11PM CEST / 9PM UTC / 5PM EDT.
https://t.co/gtNIXHnEMW

[Openτensor Foundaτion @opentensor]

This Thursday on Novelty Search :: Root Reborn

@const_reborn breaks down “Root Reborn”, a major proposed update to Bittensor’s root economy.

The proposal would replace auto-sold dividends with validator-set reinvestment across subnets.

Live via Bittensor Discord.

[Quasar @QuasarModels]

Quasar is trending on Hugging Face.

For context, HF is the central hub for AI models. Every serious open-source model lives there.

We’re already on page two, alongside Xiaomi, Qwen, and Liquid AI and this is just from our small model experiment!

The ML community are taking notice. Not just on Bittensor but in the broader open-source world.

The open-source community already built MLX and GGUF versions so Quasar can run on MacBooks and local AI setups.

We are just beginning.

[Grayscale @Grayscale]

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:
https://t.co/oNgIXuMNIy

[Openτensor Foundaτion @opentensor]

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.
> https://t.co/KJzo9YGfcB

Chutes also became a launch partner for Respan’s new AI Gateway.
> https://t.co/pBhdJWkKYj

[ @QuasarModels - SN24 ]
Quasar released Quasar-Preview, its first public Quasar model trained on Bittensor: 18B MoE, 2B active and 5M context.
> https://t.co/ThEA0gpgzg

Quasar is preparing a 10T-token decentralized training run on SN24, starting with a 5T-token phase to produce a stronger checkpoint.
> https://t.co/vXiQdA7fHK

[ @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.
> https://t.co/rICbXX6vj4

[ @webuildscore - SN44 ]
Score showed how its 19MB vision model beat larger AI models on object detection while running much faster on CPU.
> https://t.co/kfAJ78OmR8

They also added new comparison pages on @manakoai against ChatGPT, Claude, Roboflow, SAM 3 and other vision AI tools.
> https://t.co/D5ARXilc8j

[ @vidaio_ - SN85 ]
Score is partnering with Vidaio to bring vision AI challenges to SN44 and make video archives searchable and actionable.
> https://t.co/bRHvHIhw8R

[ @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.
> https://t.co/RjqUsoyDUW

They also shared in their latest AMA that Yanez has generated $300K+ in 2026 sales, with an active pipeline over $1M and 11 clients.
> https://t.co/yHaFxPX5Oy

[ @trishoolai - SN23 ]
Trishool was accepted into Anthropic’s Claude Partner Network.
> https://t.co/zE5DKPNhYM

[ @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.
> https://t.co/LSLFP1fuqU

[ @VantaTrading - SN8 ]
Vanta Trading crossed 2000 users after launching free $1k eval accounts and cutting prices by 55% across all challenges.
> https://t.co/O0Eqp3ekvY

[ @SwarmSubnet - SN124 ]
Swarm announced SOTApilot, an open-source AI drone autonomy model with 95.34% success on its UAV navigation benchmark.
> https://t.co/UnC4cGPsS0

[ @blockmachine_io - SN19 ]
Blockmachine launched Ethereum RPC.
> https://t.co/XyTFyf8pBf

[ @TrajectoryRL - SN11 ]
TrajectoryRL is expanding SN11’s skill competition from skill packs to miner-submitted finetuned models.
> https://t.co/cLxI0uKmy0

[ @heydittoai - SN118 ]
Ditto reached 1000 users.
> https://t.co/sLdkXHXdSZ

[ @theminos_ai - SN107 ]
Minos has run over 37,000 variant-calling evaluations on chromosome 21, with submissions improving by 10.21% on average.
> https://t.co/pqGUSFfxXQ

[ @minotaursubnet - SN112 ]
Minotaur launched its website and opened beta access to its DEX Aggregator.
> https://t.co/YO8qfkEuNH

[ @ReadyAI_ - SN33 ]
ReadyAI launched a revenue dashboard showing real-time demand for SN33’s structured data pipeline.
> https://t.co/6DB9XqVWdE

[ @say_gm_ - SN28 ]
Good Morning published the roadmap for its AI gateway running in a TEE, now live on testnet with mainnet beta next.
> https://t.co/AqePX5wEAA

[ @EndureNet - SN30 ]
Endure is integrating @SynthdataCo's forecasts into its DeFi risk engines.
> https://t.co/1xU2bpDdpF

[ @eirel_ai - SN36 ]
Eirel released its first product, offering deep research, image generation, web search and agent tools across multiple model families.
> https://t.co/XvD7V1uzIq

[ @adtao_ppcrebel - SN21 ]
@dsvfund took an OTC position in the SN21 alpha token.
> https://t.co/FwRoiRTOuA

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.
> https://t.co/bTJIiR7JlE

PODCASTS & ARTICLES
@opentensor Novelty Search hosted by @const_reborn with @zipcodenetwork
> https://t.co/ueU7v8gnpy

@TAO_dot_com Episode 14 with @Carrot_____1 and @KeithSingery
> https://t.co/aFN9si9eU4

@gordonfrayne podcast with @josercaldera from Yanez
> https://t.co/6pXNdSHPkV

@gordonfrayne podcast with @knakamor from Vocence
> https://t.co/FKBYJ69YWv

@AltcoinMillie podcast with @MaxScore from Score
> https://t.co/fw2EymKeLO

@AltcoinMillie podcast with @zeussubnet
> https://t.co/j54g9qxzwa

@TAO_dot_com article “The Impact of Conviction”
> https://t.co/E3gjm7ZuG4

[METANOVA @metanova_labs]

Decentralized AI isn't just philosophically better, it's functionally necessary for science to move at the speed required.

Anthropic already made Claude structurally useless for serious biotech work before any government pressure.

When they launched Claude Opus 4, they activated ASL-3 — their internal Responsible Scaling Policy's highest deployed tier. The trigger? Internal testing showed the model could assist someone with a basic STEM background in synthesizing dangerous pathogens. Their chief scientist called it explicitly: COVID-like agents, pandemic-level risk.

Their response was to apply broad biological research restrictions across the board.

The problem is that the same guardrails that block novice bioterrorists also block legitimate computational drug discovery, protein engineering, nanobody design, and molecular pathway analysis. You can't surgically remove "dangerous biology" from "useful biology" at the model level — the underlying science is the same. So they blunted the whole thing.

This is what centralized AI control looks like in practice. One company's risk tolerance — shaped by liability, regulatory pressure, and investor optics — becomes the ceiling for what an entire scientific field can access.

Open source isn't just an ideological position — it's infrastructure for the next era of drug discovery.

[Affine @affine_io]

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

[Openτensor Foundaτion @opentensor]

Watch the full Novelty Search E076 on Youtube @zipcodenetwork @sebyrubino
>> https://t.co/xbSta0lhTH

[Openτensor Foundaτion @opentensor]

“The bank of Bittensor”

That’s how @zipcodenetwork framed the long-term vision for SN46 on the latest Novelty Search.

Zipcode started with real estate appraisals.

The bigger vision is on-chain credit, real-world assets, and a new financial layer on Bittensor.

Hosted by @const_reborn
Full episode in the first comment

[Score - Subnet 44 @webuildscore]

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.

[Score - Subnet 44 @webuildscore]

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.

[ORO @oroagents]

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.

[Jon Durbin @jon_durbin]

Here's a draft of the tech report on the model training method I've been experimenting with, "Parallax".
https://t.co/0ebFkATteC

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.

[Exploit Summit @ExploitSummit]

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 👇

[Quasar @QuasarModels]

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 👇

[Openτensor Foundaτion @opentensor]

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

[METANOVA @metanova_labs]

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.