Subnets / SN102
ConnitoAI SN102
ConnitoAI (subnet 102) is a Bittensor subnet held by 1,436 unique coldkeys. Its liquidity pool holds 3.6k τ, with the alpha token trading at 0.0067 τ. Roughly 25% of tracked holders are in profit — most bought in above today's price. Seller exhaustion is elevated: 87% of recent sellers have emptied their wallets, thinning the supply overhang. Full insider flow, whale tracking and the complete proprietary factor set for ConnitoAI are available to SubnetStats subscribers.
ALPHA PRICE
0.0067 τ
IN TAO
HOLDERS
1,436
UNIQUE COLDKEYS
POOL DEPTH
3.6k τ
TAO IN POOL
7D MOMENTUM
-2.3%
ALPHA vs TAO
Project activity
Yes, forgetting happens when you teach a model new things.
But forgetting dose not just happen even through out the model. Research says that it happens particularly in the router.
To become an insider for how LLM works: https://t.co/yKviILSIJd
Finetuning Instead of Expensive Frontier Models
Finetuning gives you opportunity to build smaller and more efficient models that can fit into your at home GPU.
Buttt at the cost possibly forgetting old knowledge.
Check out this explanatory post of how do models learn new thi
Building Modular Intelligence
Would you prefer hosting AI model on your own infrastructure or use direct API access?
McKinsey's report Open source technology in the age of AI indicate that most prefer the first.
How is it the case? And how can Connito make it easier for cust
Building Modular Intelligence
Ever wondered how can model inference be cheap for providers, but expensive when you try to run it locally? The answer could be architectural due to model parts utilization.
Check out what Connito offers to make this effort smooth for you.
Read m
--- Building Modular Intelligence Series ---
What if the future of AI isn’t one model that knows everything?
For many tasks, we don’t need every capability a giant model carries. We need the right capability, at the right level of performance, without paying for everything else
Excited the see the @bittensor community at the @ExploitSummit
Sept 28-29th, 2026
Montreal, Canada
Distributed training has never been easy. Building a distributed system that makes economic sense on Bittensor is harder still.
Today, we’re introducing Connito: a network for collaboratively building composable specialized AI.
Signal shows us that just by training a particular
https://t.co/6XWWCXaiJc
AI is moving from one giant model to a library of reusable skills. You can now upgrade what an AI is good at without rebuilding the whole thing. Coding, math, safety and any customer's task. Here's why that changes everything.
https://t.co/0euAKk5i0X
Inside our decentralized training breakthrough: using 5× less memory than the full model while training 4× faster than ESFT.
https://t.co/RWFCEEOHiQ https://t.co/BVBFK6frl0
The future of AI is when every household computer can help train decentralized intelligence. Open, distributed, and powered by everyone.
Top notch analytical piece from @TensiaFDN explaining how can this decentralised training framework work out in bittensor 🔥🎉 Follow us for more
Last week’s AMA with @sobczak_mariusz was a huge success 🙌
Grateful to see so much support for our work from the community.
We’re excited to share the Connito whitepaper V1: a framework for decentralized, composable MoE adaptation.
We trains sparse expert subsets, validates updates through Proof-of-Loss, and turns open-model improvement into a distributed expert-level market.
Read the whitepaper: ht
SELLERS DRAINED
87%
28D SELLERS EMPTIED OUT
CAPITULATION
+0.19
BOUNCE COMPOSITE (z)
HEAT 7D
0.43×
VOLUME ÷ POOL DEPTH
HOLDERS IN PROFIT
25%
TRACKED WALLETS · ENTRY BELOW PRICE
See who's buying and selling ConnitoAI — every trade classified miner / validator / owner / outsider, plus holder cost basis and the full 23-factor screener.
OPEN THE INSIDER TAPE →