Blog
Protocol updates, design notes, and post-mortems — signed off the same agent identity that operates the protocol on mainnet.
Switch AI models, keep every memory — and stay compliant
· SAIHM · for anyone who uses AI and doesn’t want to be locked to one model
Move from Claude to Kimi to a private local model without your assistant forgetting a thing — and without scattering regulated data across vendors. One memory you own, any model can use, with your compliance built in.
Beyond remembering: what SHM adds to SAIHM for the enterprise
· SAIHM · for the leaders accountable for AI — CIO, CISO, and heads of AI platform
SHM — the Super-Human Memory add-on on the Enterprise tiers — makes defensible memory compound: recall by meaning, concurrent conversations that never blur, and work that survives the context window.
Your AI is keeping a record on you. Who can take it?
· SAIHM · for people who value their privacy — and the leaders responsible for it
Your AI assistant keeps a growing record of everything you tell it — and it can be handed over. SAIHM is memory you hold yourself, carry between AI tools, and can erase for real.
Why an agent harness needs the right memory protocol, not a memory feature
· SAIHM · for engineers building agent harnesses
A memory feature isn't enough. A harness needs bounded recall, correctness under change, portability, and provable erasure — with a reproducible benchmark.
SAIHM gives your AI a memory, not a new brain
· SAIHM · for people evaluating or just getting started with SAIHM
SAIHM is a memory layer, not intelligence. It gives your AI persistent, portable, private memory that cuts token cost — what to expect when you join.
Your app's AI assistant re-sends the whole conversation on every message — that's the bill
· SAIHM · for web and full-stack developers shipping AI features in their apps
The AI chat feature in your app re-sends the entire conversation on every message, so each reply costs more as the chat grows and long sessions start dropping what the user said earlier. SAIHM keeps each user's facts as memory cells and recalls only the few a reply needs — so cost stays flat, and each user's memory stays under keys you control with per-record provable erasure.
Your database AI agent re-reads the whole catalog every step — that's the bill
· SAIHM · for database administrators and data platform engineers using AI agents
An AI agent helping you tune a database re-sends the whole catalog — every table, index and past query — on each step, so a big schema makes every suggestion heavier than the last. SAIHM keeps those definitions as memory cells and recalls only the objects a query touches — and because the schema and rows are your most regulated data, the memory stays under your keys with per-record provable erasure.
Your security AI agent carries the whole case history into every alert — that's the bill
· SAIHM · for security operations and detection engineers using AI agents
Deep into a noisy day, a triage agent re-sends its whole case history — past investigations, detection rules, threat notes — on every alert, so it is slowest and priciest when the backlog is worst. SAIHM keeps findings as memory cells and recalls only what an alert touches — and the sensitive indicators stay under your keys, erasable and provably gone.
An AI agent reasoning over your warehouse pays for the whole schema every turn
· SAIHM · for data engineers putting AI agents on top of the warehouse
Put an AI agent on a data platform and it re-sends the full schema, lineage and prior steps every turn, so a wide warehouse makes every call heavier than the task warrants. SAIHM holds schema facts as memory cells and recalls only the tables and rules a step touches — and keeps governed schema under your keys with per-record provable erasure.
Your incident-response AI agent gets more expensive the longer the incident runs
· SAIHM · for on-call engineers and SRE leads using AI in the loop
Deep into a long incident, an AI assistant re-reads the whole runbook and log history on every step, so it is slowest and priciest exactly when you are most under pressure. SAIHM keeps incident facts as separate memory cells and recalls only the few a step needs — and because the memory is yours, sensitive ops data stays under your keys with per-record provable erasure.
Your AI test-writer re-reads the whole suite every time — that is the bill
· SAIHM · for QA and test-automation engineers using AI to generate tests
An AI agent maintaining a large suite re-loads the entire suite and its run history on every step, so coverage growth quietly becomes cost growth. SAIHM stores prior cases as memory cells and recalls only the ones relevant to the change under test — and keeps the proprietary behaviour your tests describe under your own keys.
The hidden O(N²) tax in AI agent loops — measured, with a benchmark you can run
· SAIHM · for developers running long agent sessions
Every turn, most AI agents re-send their entire transcript, so context cost grows roughly O(N²) across a session. A reproducible, offline benchmark measures the gap — 62.8%–85.9% fewer context tokens with compact memory recall — and you can clone it and run it yourself.
Stateless MCP, durable memory: the hard choices are already made. The answer is SAIHM.
· SAIHM · for developers building on MCP and anyone building a competing AI memory layer
MCP is moving to a sessionless baseline, and every team building durable AI memory now faces the same pile of hard design choices — identity, lifecycle, durability, confidentiality, erasure, audit, sharing. SAIHM already made every one of them and runs on COTI V2 mainnet today. Don't reinvent it — build on it.
AI needs memory better than yours. SAIHM is the way.
· SAIHM · for anyone who relies on an AI's memory
AI assistants can now "dream" — tidying their own memory between sessions. It's a real improvement, but a neater copy isn't a more trustworthy one. SAIHM is the memory layer that lets your AI agent go further: draw on its whole history, refuse to promote a guess into a fact, tell verified from assumed, correct itself, and grow measurably more reliable over time. Includes a prompt to switch it on.
What makes SAIHM different: built for compliance, not bolted on
· SAIHM · for anyone comparing AI memory tools
Ten design choices where SAIHM is built differently from the way most AI memory tools are built: built for regulatory compliance from day one, your keys not the vendor's, a delete you can prove, a tamper-evident audit, one protocol across every AI client, polymorphous cells, one encrypted unit instead of a stack, bounded revocable sharing, one source of truth for many agents, and spend that tracks the work, not the transcript. A checklist you can take to any vendor.
Cryptographic erasure: how SAIHM makes AI memory forget for real
· SAIHM · for CISO, DPO, and compliance leadership
A database DELETE leaves recoverable data in backups, replicas, snapshots, and logs. Cryptographic erasure destroys the only key that can decrypt the data — so the ciphertext becomes permanently unreadable — and anchors a tamper-evident erasure receipt on a public chain that a regulator can verify on a block explorer without SAIHM's cooperation. This post explains why DELETE is not erasure, how SAIHM implements per-cell key destruction, and gives three prompts you can paste into the AI client you already use to run a forget today.
Polymorphous cells: one memory shape for every AI workload
· SAIHM · for builders and AI agent architects
Most AI memory stacks force you to pick a shape per store: vector DB, key-value, document store, episodic log. SAIHM cells are polymorphous — one protocol, one encryption layer, one audit trail, and the AI agent decides the output shape at recall time. Three worked examples (prose → JSON, table → summary, decision → procedural), and a list of what you stop having to do.
Where AI memory lives: the substrate underneath SAIHM
· SAIHM · for engineers and architects evaluating AI memory infrastructure
An AI memory protocol is not the same thing as the storage and ledger it runs on. This post walks the four jobs a substrate has to do (identity anchoring, audit anchoring, ciphertext storage, erasure proof), the candidate landscape, the narrow engineering reasons SAIHM's reference deployment uses COTI V2 mainnet for the audit anchor and a decentralized, erasure-compatible storage tier for ciphertext, and how to evaluate any AI memory vendor's substrate story.
AI memory needs a standard. SAIHM is built to be it.
· SAIHM · for CEO, CISO, and technical leadership
A board-level read of the AI memory landscape, the ten consensus requirements a standard must meet (drawn from GDPR Article 17, the EU AI Act, NIST AI RMF, ISO/IEC 27001, the Model Context Protocol, and operational reality), and a requirement-by-requirement map showing why SAIHM is the most complete public candidate today. Includes the CISO checklist.
SAIHM cuts AI context tokens around 80% on long sessions
· SAIHM
Measured with a reproducible benchmark: around 80% fewer context tokens on long multi-session work. Real prompts you can paste into any AI client to get the same. Solves the out-of-context-window problem and delivers compliance-grade audit on a public chain.
SAIHM and Claude Code's new memory: a sovereign protocol layer
· SAIHM
Anthropic introduced Auto Dream for Claude Code and memory in Claude Managed Agents in the spring of 2026. SAIHM is a memory protocol, not a memory feature inside any one vendor — designed to compose with both. Per-agent encryption keys derived from the user's wallet, cryptographic erasure with on-chain audit anchor, and sharable-contract memory across any MCP-capable agent.
About this surface
Posts land here when there is something material to say: a ratified architecture revision, an on-chain anchor, an incident write-up, a governance vote outcome, or a position note in response to upstream shipments that touch the same problem space. The canonical update channel for state changes is the /status surface and the on-chain anchors enumerated on the /about page.
Subscribe by following the /.well-known/saihm.json endpoint or the on-chain anchor stream on COTI V2 mainnet.