Top Tweets for #SN81
Yesterday was a difficult day for our index, perhaps the most difficult even since the April rug pull of #SN3, #SN39, and #SN81.
We saw the Sum of Alpha Prices plummet from 1.33 to 1.28 in a matter of hours.
Our index lost approximately 3.84% in one day.
At the moment, we can tell you that most of the connected wallets have managed to completely absorb yesterday's dump, and some are even slightly higher.
Let's keep it up🚀
The index is only available on @TrustedStake
https://t.co/ANA7QzVz3L
#Bittensor #TAO #Index #dtao
April is coming to a close, experiencing a massive dump with the rug pull of #SN3, #SN39, and #SN81.
However, with the latest improvements I've made to the Bittensor Safe Index strategy, we've seen a dramatic surge in performance, finishing this month with the best performance ever on the index.
In just a few more months, the strategy will go private following the completion of this public backtest.
https://t.co/yR9Ckd7yuT
#Bittensor #TAO #AI #Crypto #dtao
Bittensor TAO #sn3 #sn39 #sn81 @covenant_ai @tplr_ai rugpull.
Accusations from @DistStateAndMe about @const_reborn explained.
And the beauty of open source means we will continue.
@covenant_ai =
Distributed Training, RL, & Compute
on $TAO
#SN3 #SN39 #SN81
https://t.co/2z2NgQdmTG
Will set @grail_ai #SN81 new benchmarks?
Like his big brother @tplr_ai from @covenant_ai
It is not to late for research ☝🏻
$TAO
Used autoresearch to make @grail_ai GRPO trainer 1.8x faster on a single B200.
I kept postponing this for weeks since the bottleneck in our decentralized framework was mainly communication. But after our proposed technique, PULSE, made weight sync 100x faster, the training update itself became the bottleneck. Even with a fully async trainer and inference, a slow trainer kills convergence speed.
A task that could've eaten days of my time ran in parallel while I worked on other stuff. Unlike original autoresearch, where each experiment is 5 min, our feedback loop is way longer (10-17 min per epoch + 10-60 minutes of installations and code changes), so I did minimal steering when it was heading in bad directions to avoid burning GPU hours. The agent tried so many things that failed. But, eventually found the wins: Liger kernel, sequence packing, token-budget dynamic batching, and native FA4 via AttentionInterface.
27% to 47% MFU. 16.7 min to 9.2 min per epoch.
If you wanna dig deeper or contribute: https://t.co/8S7MnvwxMa
We're optimizing everything at the scale of global nodes to make decentralized post-training as fast as centralized ones. Stay tuned for some cool models coming out of this effort.
Cheers!
Bittensor in Kenya // 25 Feb
We’re hosting the Bittensor Subnet Ideathon at Sankalp Africa Summit in #Nairobi
Featuring guests from
- Gareth from #SN85 // @vidaio_
- Sam from #SN3, #SN39 & #SN81// @@covenant_ai
- A guided workshop to turn real problems into subnet-ready ideas.
Africa shouldn’t need Silicon Valley-sized budgets to access frontier AI.
That’s the point of Bittensor.
Think it. Pitch it. Build it.
11:00–15:00 EAT • Pavilion 2 • Sarit Expo Centre
Register → https://t.co/6w41euwgFI
@HackQuest_ @SankalpForum
@basilic_ai @opentensor In @covenant_ai we trust ✊
@tplr_ai @grail_ai @basilic_ai
#SN3 #SN39 #SN81 $TAO
🚀 Just recently joined @tplr_ai to build decentralized + distributed post-training (RL-finetuning) infra, called Grail!
This week, we launched v1:
🔐 Verifying inference rollouts in a permissionless, adversarial network.
Think of it as pen-testing for inference verification.
Low cap $TAO subnets I’m big on right now!! 🚀
#SN38 @dstrbtd_ai
#SN42 @getmasafi
#SN75 @hippius_subnet
#SN81 @grail_ai
I have a bit of dry powder left! What am I missing?
Still DCAing into the $dTAO subnets that are low cap and high value
Just picked up more ⬇️
@hippius_subnet #SN75
@grail_ai #SN81
They are part of the tech stack that is @const_reborn and #affine #SN120 are building
Deep value here in the $TAO subnets
🔍 Novelty Search Guest Detected
This week's Novelty Search, hosted by @const_reborn, will feature guest @DistStateAndMe, custodian of three Bittensor subnets:
• Templar Subnet 3 (@tplr_ai) #SN3
• Basilica Subnet 39 #SN39
• Grail Subnet 81 #SN81
🔗Discord Event - Aug 28: https://t.co/X0F6aKgfE8
#SN81 👀 The Grail.
🧠 Bittensor In One Picture $TAO 🧠
A stack of specialised subnets that plug into each other:
Compute & Storage → Core AI Services → Alignment → Applications.
$TAO coordinates the whole flow. 🔁
🧵👇
Very interesting to see #SN81 not many jumped in yet, probably because of the weekend.
Const wallets still not holding 81 either.
Next week will be very telling to see how this unfolds… and funny enough, nobody’s talking about it 👀
Yes, I know the market is high, but this one could still run.
👀 new site who dis?
https://t.co/o4lZQC6I5t
🤝 Subnet Acquisition Announcement:
Patrol (https://t.co/nKbIF5R11r) Subnet 81 — has officially been acquired from @taodotcom by @tplr_ai.
This marks the third subnet under the control of @DistStateAndMe:
• #SN3 — Templar
• #SN39 — Basilica
• #SN81 — Patrol (https://t.co/nKbIF5R11r)
Signal concentrates. Power compounds. #dTAO
#Bittensor
#SN81 Grail ?
It might be this: ‘the grail of AI research’ = the ultimate goal of AI research ?
A model or an eagerly awaited approach, expected to finally solve a difficult problem.
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