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6. NEW: openroboto (SN80) data collectors now run peaqOS. peaq says 30 are live, each with an identity anchored on Solana, tying robot training footage to verifiable capture devices.
https://t.co/b5c2U2NFT1
Robot training data is becoming valuable enough to fake
Every data collector from @openroboto, the largest robotics subnet on Bittensor (@opentensor), now runs peaqOS on the Pro tier
30 are live, each with its own identity anchored on @solana, and every future one will be too
Bittensor Weekly Roundup
The week's top stories: Null Consensus, Etao, UR live on SN25, Chutes Parallax on SN64, Apex SN1 and peaqOS on SN80. A thread.
5. NEW: Apex (SN1) says an agent-built incentive mechanism went from prompt to production in 6 hours and is live and incentivized. const: "Send your agents to mine this with us."
https://t.co/s09NRGZgKV
Backprop Flow Report · Wed Oct 7
+866 τ flowed into Bittensor subnets in the last 24h, on 147.7k τ of volume.
Where it went hardest: SN51 @lium_io. 🧵
#smartTAO $TAO
Bitcoin decentralized money. Bittensor is decentralizing intelligence
$TAO 2030 is our case for Bittensor becoming a $1tn+ ecosystem by 2030 https://t.co/j0zWWcKBDp
UPDATE: @const_reborn flags Reliquary (SN81) as one of the strongest minable post-training designs on Bittensor.
Miners build compute fleets that produce verified rollouts (Const compares the structure to Conjecture SN66 searching math proofs). In a follow-up, Const sketches a 2027 Gamma path: SN81 could route a portion of miner emissions as Gamma onto compute subnets to scale that fleet on-chain.
What the Reliquary team has invented for a minable objective is truly one of the most compelling structures i have seen for a scalable post training mechanism on Bittensor.
In the same way that conjectures 66 searches math proofs, Reliquary has miners build compute fleets which search for rollouts on a target model which partition the reward landscape.
You spin up a miner, download the network model and scale your inference engine + innovate on your search technique to uncover negative samples from the environments.
Those samples become optimal inputs to the training process for the network model.
Like how Bitcoin miners bleed energy for a hash. Reliquary miners bleed energy for a rollout. It’s a search problem at heart which is where Bittensor’s abstraction over the search for search algorithms really shines.
Reliquary was created by two young engineers with no credentials and no allegiances to anything except a vision for decentralized intelligence.
Bravo Rom and Grizz
UPDATE: @const_reborn at Exploit outlined Gamma tokens for 2027.
Proposed universal usage credits across Bittensor subnets. Burn subnet alpha at an oracle USD price to mint Gamma (e.g. $10 of alpha burned → 10 Gamma). Credit balance stays fixed as alpha moves and spends only as usage on that subnet. Aim: teams fund compute, inference, and storage inside the network from emissions.