The "Goldfish Memory" Problem in AI—and How $VANRY Plans to Fix It
We’ve all been there: You spend two hours feeding an AI model context, documents, and rules, only for the session to glitch or time out. Suddenly, you’re back at square one. It’s a massive productivity sink—a literal "grind" that wastes hours on redundant inputs.
I’ve been tracking @Vanar chain lately, and their roadmap (Neutron & Kayon) addresses this exact "rebuilding context" nightmare. Think of it as moving from a messy desk to a shared, structured filing cabinet.
The Architecture: Plumbing Over Flash
While most projects chase hype, Vanar is building infrastructure that actually sticks:
Neutron (The Memory): Instead of re-uploading data, Neutron compresses inputs into verifiable "seeds" stored on-chain. It’s capped at 1MB to prevent storage bloat, ensuring that your core data is organized once and accessible forever without the "vanished session" drama.
Kayon (The Brain): This is where it gets interesting. Kayon applies reasoning rules over those seeds. Because it happens on-chain, the decisions are auditable. No more "black box" logic or relying on flaky external oracles.
The Economy: $VANRY isn’t just a ticker; it’s the gas for these smart transactions. It pays the query fees for the stack, making the ecosystem self-sustaining.
The Reality Check: Early Traction vs. Execution Risk
I’m seeing 15K+ seeds in early testing, which shows real dev appetite. However, the shift to the myNeutron paid model and recent query latency spikes show that scaling isn't without its growing pains.
My Take: I’m skeptical of a perfectly smooth Kayon integration—slips are almost guaranteed in modular builds. But I’d rather have reliable plumbing than a flashy front-end that breaks. If Vanar solves the "structured memory" problem for AI builders, the app layer will follow naturally.