Learning with something that remembers the whole path
Learning rests on continuity: today's step leans on last year's. These analyses are about what provides that continuity and what it costs.
What exactly vanishes between sessions
Separating the verifiable from the interpreted: a model really is stateless between calls, and that is not a metaphor. Choice and awareness cannot be checked — and the engineering does not depend on that answer.
The PADAM cadence
The protocol read through a single quantity: how often memory is pushed into permanent storage. The famous quarter-hour turns out to be the rhythm of the middle tier, and the roadmap keeps the finished carefully apart from the promised.
Permanence without search is a warehouse
Immutable storage answers for a record surviving and promises nothing about whether you will ever find it again. This analysis follows the second guarantee: an HNSW graph cut along meaning on top of a DHT, a Groth16 proof of distance with dimensionality squeezed from 1536 to 128, the pricing formula for a query, and the half of every fee set aside in advance for permanent storage.
Twenty dollars for forever
Where the claim comes from that one payment covers permanent storage: 10 GB of memory a year, $2 per gigabyte, disk prices falling 30 % annually. Plus epistemic drift and the node vote at the fifty-one per cent rule.
Checking Someone Else's Training Without Repeating It
Verification usually costs as much as the result itself: to be sure a node really ran the gradients you would have to repeat the whole pass. Trust in someone else's GPU rests here on three separate lines of defence, and confusing them is where inflated expectations come from. What each one actually answers for.
Bytes on a Disk Are Not Yet Memory
Storage-network proofs answer a question about bytes: the sectors hold the right content. Whether what is stored is fit for use is a different question entirely. What the Proof-of-Memory testnet showed in two weeks — a hundred nodes, a million cycles, a proof in 1.2 seconds — and which of those numbers belong to the 2026 roadmap.
The forgetting curve
Move the number of reviews and their schedule and see how much of what you learned remains after a month. The model is simple, but it shows the key point: it matters not how often, but when.
Keep it in mind
Memory bars melt along the forgetting curve. Review too early and it barely helps; too late and the word is gone. Catch the moment when a review makes memory strongest.
The analyses are published on codeofdigitaleternity.ink. All analyses