Subnets / SN2
DSperse SN2
DSperse (subnet 2) is a Bittensor subnet held by 1,830 unique coldkeys. Its liquidity pool holds 8.7k τ, with the alpha token trading at 0.0039 τ. Roughly 25% of tracked holders are in profit — most bought in above today's price. Seller exhaustion is elevated: 79% of recent sellers have emptied their wallets, thinning the supply overhang. Full insider flow, whale tracking and the complete proprietary factor set for DSperse are available to SubnetStats subscribers.
ALPHA PRICE
0.0039 τ
IN TAO
HOLDERS
1,830
UNIQUE COLDKEYS
POOL DEPTH
8.7k τ
TAO IN POOL
7D MOMENTUM
+9.6%
ALPHA vs TAO
Project activity
1/ Computer vision creates an unusual enterprise problem.
The original event may disappear immediately. A worker moves, a bag passes, an aircraft changes position, a defect leaves the line.
You may never get the exact same physical moment again.
Thousands of AI agents can now work in parallel on scientific data.
That creates a new research problem: which lead came from which agent, dataset, tool call, and intermediate result?
Scale discovery, but preserve lineage. That principle sits at the heart of IL.
1/ A bounding box is not an operational outcome. The useful workflow is:
detect → evaluate → route → notify → review.
That is the difference between a computer vision demo and something a real operations team can use. https://t.co/3oS6ECdhp9
1/ The FAA is already being asked to show how new AI decision-support tools were tested before wider use.
That is what production AI looks like in aviation: the model may advise, but the evidence around the advice still matters.
Synthetic data is moving inspection setup into the virtual world before the first physical product reaches the line.
That makes data lineage critical.
If training shifts between real and generated examples, teams need to know what shaped the model they deploy.
A model escaping an evaluation environment is no longer hypothetical. Recent disclosures show exactly why sandboxing alone is not enough.
Containment limits damage. Evidence reconstructs what happened.
Autonomous systems need both.
https://t.co/91z1UYbDP9
AI can now help formally verify security properties across 100+ real software libraries.
That is a big shift: AI can help generate proof, not just code.
At Inference Labs, we think verification should become a normal part of machine-produced work.
https://t.co/91z1UYbDP9
1/ Operational problems are cheapest to solve before they become incidents.
Congestion starts building. Snow coverage leaves a gap. A baggage position slows down.
Computer vision can surface the pattern while there is still time to act. https://t.co/PxpW8Ezvau
Computer use is getting cheaper and faster. That means agents can take far more actions for the same budget.
The bottleneck moves again: not “can AI click the button?” but “can we prove which action happened and why?”.
That is the runtime layer Inference Labs is building for.
Low visibility does not have to mean low awareness.
Sertn can keep tracking ferries, cargo vessels, and other marine traffic through haze or poor conditions, helping port teams maintain a clearer operational picture when visibility drops. https://t.co/6BDAEzZmzB
1/ A lot of airport events are impossible to recreate. A tow crosses the wrong zone. A stand entry is off-line. Baggage backs up for six minutes.
By the time someone investigates, the physical moment is already gone. https://t.co/9xrFOQqm0w
Air-gapped AI is becoming more relevant in defense, manufacturing, and regulated environments. When data stays on-site, trust cannot depend on a remote service.
Sertn is built for verifiable AI that can operate where the data lives.
https://t.co/pchEKDmwBF
Self-hosted inference is becoming more common as teams want lower latency, more privacy, and more control. But local deployment should not mean less accountability.
Sertn is built to keep inference verifiable even when it runs close to the edge.
https://t.co/pchEKDmwBF
1/ Operational dashboards are good at averages.
Airports often care about the exception.
The one aircraft that entered the wrong stand. The one person who stopped moving. The one baggage position that suddenly backed up. https://t.co/EqlBgSg98U
Computer vision is moving from object detection to understanding scenes, relationships, motion, and context. That opens much richer operational workflows.
Sertn is built for that shift from “what is here?” to “what is happening?”.
https://t.co/pchEKDmwBF
Crowd problems usually become visible only once they are already disruptive.
Sertn can monitor density at choke points and detect unusual stillness or posture, helping airport teams respond to congestion or a person down before someone has to report it manually. https://t.co/ff1
An AI policy can say what a system should do.
Runtime evidence shows what actually happened.
That distinction is becoming important enough that new standards are emerging around independently verifiable AI governance and execution records.
Once computer vision can trigger actions, the useful question changes. It is no longer only “what did the model detect?” but “what happened because of it?”.
Sertn helps connect inference, action, and evidence in one workflow.
1/ Traditional enterprise auditing is retrospective.
Collect logs, interview people, reconstruct events, then decide whether controls behaved as expected.
AI operating at machine speed makes that model increasingly expensive.
Most enterprise integrations expose an API. The next generation may also expose evidence.
Input accepted. Model executed. Output produced. Proof attached.
At Inference Labs, we think verification can become an interface between AI vendors, customers, auditors, and machines.
1/ Enterprise AI often has two things it cannot freely expose:
customer data and proprietary models.
That creates an awkward trust problem. A third party may need assurance that the right computation happened without being allowed to inspect everything inside it.
Ports are full of mixed traffic: vessels, launches, bulkers, cargo ships, and navigation markers all moving at once.
Sertn can classify and track activity, turning live camera feeds into structured operational data for traffic monitoring, safety, and port operations. https://t.c
The model is becoming only one component inside a much larger AI runtime. Memory, tools, routing, actions, and state all matter too.
Inference Labs is building for that broader system, where trust has to survive beyond the model call.
https://t.co/91z1UYbDP9
It’s time for a new chapter for Inference Labs and SN2.
The developers and community behind SN2 have produced more than 6 billion cryptographic proofs and found new ways to improve zkML performance. Thank you to everyone who helped build and push this work forward.
Dan joined o
Baggage congestion becomes expensive long before it becomes obvious.
Sertn can monitor bag accumulation and clearance rates at each make-up position, helping airport teams see bottlenecks forming early and identify exactly where the pressure is building. https://t.co/LrXWTwLBNQ
SELLERS DRAINED
79%
28D SELLERS EMPTIED OUT
CAPITULATION
-0.74
BOUNCE COMPOSITE (z)
HEAT 7D
0.65×
VOLUME ÷ POOL DEPTH
HOLDERS IN PROFIT
25%
TRACKED WALLETS · ENTRY BELOW PRICE
WHAT'S CARRYING THE PRICE · 30DORGANIC
30D RETURN
+29.9%
ALPHA vs TAO
WITHOUT CHAIN BUYS
+29.9%
SAME 30 DAYS, PROTOCOL BID REMOVED
CHAIN SHARE OF BUYING
0%
CHAIN BUYS vs TRADERS' NET
CHAIN BUYS / DAY
0 τ
PROTOCOL BUYING ITS ALPHA
See who's buying and selling DSperse — every trade classified miner / validator / owner / outsider, plus holder cost basis and the full 23-factor screener.
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