Subnets / SN102
ConnitoAI SN102
ConnitoAI (subnet 102) is a Bittensor subnet held by 1,321 unique coldkeys. Its liquidity pool holds 3.1k τ, with the alpha token trading at 0.0045 τ. Roughly 22% of tracked holders are in profit — most bought in above today's price. Seller exhaustion is elevated: 90% 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
0.0045 τ
IN TAO
HOLDERS
1,321
UNIQUE COLDKEYS
POOL DEPTH
3.1k τ
TAO IN POOL
7D MOMENTUM
+8.2%
ALPHA vs TAO
Project activity
There is a friend of mine who works in a railway company safety department. I see him spending 90% of his work hour on safety documentation.
And modern AI isn't really performing good in that area, cause the system is just wayyy to old for it to understand.
Could the subn
Miner wanting to train an expert model in our subnet that helps with identifying operational issues and analyzing round results to recommend next best action for running the miner.
Can we do that? 👀
https://t.co/ca807zDOHe
You can submit a training request here as well!
https
Training new residual experts on Qwen3.6-35B-A3B to support our next incentive update 👀 https://t.co/6O9LBf20g9
Can we complete the whole data-generation, fine-tuning, and evaluation pipeline for the customer?
Ask ahead! Put your questions in the thread!
Dataless? What does it mean? 👀
One thing after another! New incentive mechanism always makes me happy 😋
Just surreal to see one of the miners in real life!
The exploit summit definitely brought us inspiration! Right now moving on to our next big thing! 👀 Stay tuned!
Today is the day when we come out from stealth right at exploit summit!
See you!
We're getting ready to launch our training-as-a-service (TaaS) product! And we want to build it around what you want.
This initiative would act as our market research, done in the open, tell us which expert you wish existed, and the Connito subnet may train it for real.
Every
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
90%
28D SELLERS EMPTIED OUT
CAPITULATION
+0.09
BOUNCE COMPOSITE (z)
HEAT 7D
1.93×
VOLUME ÷ POOL DEPTH
HOLDERS IN PROFIT
22%
TRACKED WALLETS · ENTRY BELOW PRICE
WHAT'S CARRYING THE PRICE · 30D—emission rank #57 · below the bar (rank 32)
30D RETURN
-21.3%
ALPHA vs TAO
WITHOUT CHAIN BUYS
-21.3%
SAME 30 DAYS, PROTOCOL BID REMOVED
CHAIN SHARE OF BUYING
—
CHAIN BUYS vs TRADERS' NET
CHAIN BUYS / DAY
0 τ
PROTOCOL BUYING ITS ALPHA
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 →