I have been reading a lot of AI related stories in crypto recently, and they all fail as soon as one poses the simplest question: where did the agent recall anything? A chain can execute code, sure. However, when all the useful context is off-chain (databases, IPFS links, dashboards, scattered files), then the “AI” is just a fancy UI that is stapled to fragile infrastructure.
@Vanar thinks it is doing it the reverse: put memory into first-class, and reasoning and automation then rest on it. That is not a vibe statement, it is literally the presentation of their stack: base chain: Neutron (semantic memory) stack base chain: Kayon (reasoning) stack base chain: Axon/Flows (automation and apps) stack.
The actual bottleneck is dark data.
In Web3, possession becomes confusing quickly since much of what is possessed is in fact a reference. Hash here, IPFS there, middle between the clouds, on storage, and one broken link further, the asset is little more than a receipt to something you will never be able to check or redeem.
Neutron is the solution of Vanar to that issue. The concept is quite straightforward yet rather bizarre: squeeze and re-model real files into so-called Seeds which are small enough to exist on-chain, though still queryable and verifiable. Vanar explains this as the reduction of around 25MB to approximately 50KB and the compression ratio of the headlines measures around 500:1.
And this is not a marketing text in a vacuum, as in 2025, Neutron was publicly shown at Vanar Vision in Dubai (TODA) where a 25MB clip was condensed into a small “Seed” and could be restored fast once it had been embedded in a mainnet transaction.
The angle of the new update that I am really interested in: MyNeutron.
One thing that I have noticed in recent times is MyNeutron - essentially branding itself as a portable memory among tools. Vanar puts it in plain language, AI context that dies whenever you switch between assistants or work apps, MyNeutron is designed to carry that memory with you (permanently on-chain, or locally on-control).
When they act on this, then it is bigger than they imagine. Since it makes AI on-chain a daily practice: your documents, conversations, receipts, your workflows will be something you can query and act on in practice, instead of recreating the context on case by case.
This becomes dangerous in the good sense in Kayon.
A memory is not sufficient, you have to have a layer that is capable of reasoning about it. This is what Kayon has to do: ingest the semantic Seeds of Neutron (including enterprise / on-chain data and then convert them into audible insights, predictions, and workflows), and query them using natural language and integrating them using APIs.
Two details stood out to me:
• They openly sell compliance automation (monitor rules in 47 or so jurisdictions etc.). It is not a commonplace degrading AI agent promise, but a venture business-type promise.
• The examples aren’t fluffy. They are simply: Ask the chain a question which could be answered by an analyst and get a response which can be operationalized.
Where I believe VANRY would be (without the moonboy talk)
Assuming that Vanar is correct in his thesis, then the value does not lie in the fact that it is another fast L1. It lies in the fact that it can be the place where data is made usable on-chain: compressed and searchable and provable and subsequently actable upon by reasoning and automation. It is a value loop that most chains do not use.
The core chain is itself characterized by Vanar as being designed to run AI workloads, including things such as in-memory vector storage / similarity search, semantic operations, and AI-friendly validation.
Neutron includes the memory layer, (data, compressed, knowledge, activated), and Kayon, includes the reasoning interface.
Therefore, on my personal list of watchlist of $VANRY is not only price candles but:
Are individuals of actual MyNeutron use?
• Are Seeds a standardized on-chain files which do not go dark?
• Will Kayon be the default query language of explorers, dashboards, and teams to query chain history?
My bottom line
#Vanar is one of the rare projects in which the AI story does not seem to be glued on. The stack is consistent: store significance (Neutron), reason on it (Kayon), then automate activities (Axon/Flows).
Should they continue shipping at real integrations and real usage this may be less hype cycles than just something boring but valuable: infrastructure AI can actually count on.