Jon Cipher

[ConniCore @ConnitoCore]

- My personal opinion.
@const_reborn
you deserve thanks, and so does everyone who helped build this ecosystem.

What started as a network has become a catalyst for change. Not only financially for many people, but in ways that will continue to touch lives across countless fields.

AI is no longer a future concept; it’s a reality. And what the different Bittensor subnets are building today may become the foundation for ideas, tools, and services that make life easier for millions tomorrow.

I hope to see this vision continue to grow and reach its full potential.

#TAO #Bittensor

[ConniCore @ConnitoCore]

The technical results look promising. The next milestone is proving demand: real customers, real workloads, and real revenue. If that happens, SN102 could be something special.
@connitoai #ConnitoAI #SN102 #Bittensor #TAO

[ConniCore @ConnitoCore]

SN102 continues to show strong technical progress.

Recent dashboard data shows validation loss improving from ~0.20 to ~0.14, while competition among miners remains intense with ~225-255 active miners. Meanwhile, Connito’s latest research claims its in-place ESFT approach achieves performance comparable to classic fine-tuning while using ~80% less GPU memory and training ~4x faster.

The technology is working. The next milestone is proving real customer demand.

@connitoai #ConnitoAI #SN102 #Bittensor #TAO #DecentralizedAI #AI

[Nic @Nic_B1m]

@b1m_ai 's PRISM was the foundation (which will continuously improve), now its time to connect the dots ... Beam Transfer Studio. #Bittensor SN105 #AI #DATA

[Crypto小宋 @WOOFWORK1]

In-place ESFT addresses the pain points of high costs, excessive video memory usage and slow training in fine-tuning MoE large models. With just one-fifth of the video memory and one-fourth of the training time, a single consumer-grade A6000 graphics card is sufficient to fine-tune the billion-parameter MoE large model. It delivers performance on mathematical tasks identical to that of full-parameter multi-GPU fine-tuning while preserving the model's general capabilities, greatly lowering the hardware barrier for MoE large model fine-tuning. SN102 #Bittensor

[Aphτ 🌊α @tao_Alph]

SN 102 @ConnitoAI
ESFT is a fine-tuning optimization technique designed for Large Language Models (LLMs) that use a Mixture-of-Experts (MoE) architecture !!! 4X Fasters at the same accuray

MoE !!!!

[Jon Cipher @_joncipher]

$TAO Break on Through ⬇️⬇️

ConnitoAI (Bittensor Subnet 102): Major Training Breakthrough Just Dropped

This is big.⬇️⬇️

https://t.co/h24Rr6CRSo

@ConnitoAI just announced a serious technical win: they’re now training with 5× less memory and 4× faster than previous methods using their new In-place ESFT (Efficient Sparse Fine-Tuning).

The Numbers (June 2026 Status):
• Active parameters during training: 3.11B (only ~20% of the full 15.7B model)
• Peak GPU memory: 21 GB (down 83%)
• Training time: 3h 07m (4× faster)
• Accuracy: Maintained at 39.5% on GSM8K-CoT math benchmark

What It Means
Instead of loading the entire Mixture-of-Experts model on GPU, miners now train only a small active shard of specialists in-place, park the rest on CPU, and merge updates seamlessly.
This makes decentralized training dramatically more efficient and accessible on affordable hardware (single A6000 GPUs).

Bottom line: ConnitoAI just lowered the cost and raised the speed of specialized model training across the network. This directly strengthens the decentralized training factory thesis and accelerates the flywheel for custom expert modules.
Real execution. Real iteration. The MoE beast is getting stronger.
RELEASE THE KRAKEN. 🐙⚡️
$TAO $SN102 $SOL
#ConnitoAI #SN102 #Bittensor #DecentralizedAI
DYOR. The training wars just leveled up. 🚀

[Connito AI @ConnitoAI]

Inside our decentralized training breakthrough: using 5× less memory than the full model while training 4× faster than ESFT.

https://t.co/RWFCEEOHiQ

[Connito AI @ConnitoAI]

Inside our decentralized training breakthrough: using 5× less memory than the full model while training 4× faster than ESFT.

https://t.co/RWFCEEOHiQ

[Beam - Subnet 105 @b1m_ai]

PRISM is now live.

Beam’s orchestrator scoring system is officially deployed and the code is now public.

PRISM ties rewards to real, verified work:
- exposure × quality × confidence × penalty
- No static weights.
- No hidden allocation logic.
Performance, verification, and reliability now directly drive exposure and emissions.

This release represents the most decentralized and scalable version of Beam so far.

To promote fairness across the network, a short buffer period will remain in place until Friday 05/15 before transfer activity fully ramps up. This gives orchestrators time to deploy, update, and properly configure their infrastructure under the new system so everyone starts on equal footing.
The public repo and documentation are now available for the community to review, audit, and build on.
https://t.co/21L8sSwBlW

Beam-core public:
https://t.co/j2zTFEbHpI

Documentation:
https://t.co/b3GnrtEjCT

This is only the beginning.

Beam: Powering the open internet

[Minos @theminos_ai]

At this year’s Proof of Talk, DeSci will be part of the center-stage conversation.

Our scientific advisor @PKoellinger, founder of DeSci Labs, will be on stage in Paris discussing how Bittensor can help create infrastructure for verifiable scientific claims.

We believe distributed compute can become a real engine for science by scaling reproducible workloads, aligning incentives, and rewarding useful scientific work.

Philipp has been building toward this future for years, and we’re excited to see him bring that vision to Proof of Talk.

Keep an eye out for his talk!

[Aphτ 🌊α @tao_Alph]

Amazing founder @Nic_B1m … amazing project

[Beam - Subnet 105 @b1m_ai]

The future of data orchestration will require flexibility across clouds, platforms, and environments.

Permissionless | Open source | Programmable
Already running on Beam SN105.

#Bittensor #AI #DataInfrastructure #OpenSource

[Jon Cipher @_joncipher]

@timatilos Brilliant!!! 🫡

[Jon Cipher @_joncipher]

$TAO Getting Stronger ⬇️⬇️

ConnitoAI (Bittensor Subnet 102): Tensia Foundation Just Dropped the Definitive Deep Dive — And It’s Even Stronger Than We Thought

Listen up. While most projects drop vague hype, Tensia Foundation just published a crystal-clear technical teardown on SN102.
Read it here:

https://t.co/EC9ACKAyFW

This confirms why ConnitoAI is one of the highest-conviction plays in the entire Bittensor ecosystem.

The Core Idea (Genius in Simplicity)
ConnitoAI flips centralized training on its head: decentralized training across thousands of machines using Mixture-of-Experts (MoE) on top of open DeepSeek V2 Lite.
Only relevant specialist experts get updated. Almost zero data movement. This is the decentralized training factory built for the “own your specialized data loop” era.

The Killer Mechanism: Proof of Loss
Miners train locally → validators run surprise tests measuring real improvement. Better = rewarded. No improvement = nothing.
Sealed commitments make cheating nearly impossible.

Economics & Accessibility
Highly competitive: top 3 miners take ~98% of rewards.
Accessible hardware — just an A6000 (rentable for <$1/hr). No H100 farms needed.

What They’ve Proven
Math-domain POC: real gains without catastrophic forgetting. Next up: head-to-head vs centralized training.

The Business Angle
Built for Training-as-a-Service (TaaS) — enterprises pay for custom experts on their private data loops (legal, healthcare, finance, agents). Real revenue layered on TAO emissions.

Team Edge
Isabella Liu (early Bittensor engineer) + George Kim. Real builders with skin in the game.

Bull Case
Full network health, live activity, transparent execution. Perfectly timed for the specialized MoE + agentic supercycle. In a TAO bull run, this could rerate aggressively as TaaS takes off.

Risks
Early stage. Top-heavy rewards. Execution and competition risks remain.

Bottom Line
Tensia just laid it out: ConnitoAI (SN102) is the real deal. Strong incentives, elite team, clear path to infrastructure dominance.
The specialized MoE flywheel is spinning. The beast is awakening.
Patience for the believers. Builders are winning.
RELEASE THE KRAKEN. 🐙⚡️💥
$TAO $SN102 $SOL
#ConnitoAI #SN102 #Bittensor #TAO #DecentralizedAI #CryptoAI #AIAgents #Web3 #Solana
DYOR. Size appropriately. The training wars are heating up. 🚀

[Tensia Foundation @TensiaFDN]

https://t.co/1t7J3Cg0tS

[Jon Cipher @_joncipher]

@LamidaGlobal Would help stabilise a baseline alpha price for subnets especially the newer ones and act as a standalone feature that may attract more investing into the subnets.

[low(44)🇨🇦 🐀 @jlow7865]

#SN102 Connito AI - talented team, exciting project + nice pull back in price. Lovely.

[Jon Cipher @_joncipher]

$TAO Silently building 👀

🚨 ConnitoAI SN 102💥

THIS is the raw power and evolution the TAO army has been craving! 🔥🚀💥

While the subnet evolves in silence… the beast awakens.
Watch this Kraken rise — armored, relentless, unstoppable in the arena. Just like ConnitoAI forging the decentralized MoE training factory the AI world needs.
Custom experts. Private data loops. A compounding moat that gets stronger with every round.
The flywheel is turning into a storm. Builders are dominating this evolution.
RELEASE THE KRAKEN. 🐙⚡️💥🌊

$TAO $SN102 $SOL
#ConnitoAI #SN102 #Bittensor #TAO #DecentralizedAI #CryptoAI #AIAgents #Web3 #Solana