Building free tools for the Bittensor ecosystem. Creator of SubnetRadar - real-time analytics dashboard covering all 128 TAO subnets. For the community ❤️subnetradar.com DTAOJoined March 2026
Ninja’s new agent competition is live.
We’ve moved the incentive mechanism closer to real-world agent usage. The closer the competition gets to how agents are actually used, the faster miners produce agents that get better at real-world work.
Our next release, codenamed Katana, is the next piece of the stack.
We think miners will love it. We think people who’ve never even heard of Bittensor will find it useful too.
Soon.
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
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.
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