Drawn directly from the operator's own Wyrm memory, read by their CLI AI assistant, the same way Wyrm is used every working day. Identifying details withheld.
The Operator
A forward-deployed operator, running more than a dozen client and product workstreams at once, under constant deadline pressure. One person doing the work of a small team, with an AI assistant as the force multiplier.
The bottleneck was never capability. It was memory. Every new session with the AI started cold.
The Challenge: AI Amnesia at Scale
A model's context window is brilliant but ephemeral. It resets every session and compresses once it fills. Across many parallel projects, that means the thread quietly slips.
Cold starts
Every session began by re-explaining context from scratch
Lost decisions
Why a thing was done one way evaporated between sessions
Repeated mistakes
The same dead ends got tried again, days apart
Context switching
Thirteen projects, no shared memory across any of them
A Real Moment
08:00 session start
operator opens the AI. no re-briefing.
wyrm:
picking up where we left off.
63 open threads across 13 projects.
yesterday you were mid way on the migration.
a recorded failure here: do not retry the old auth path, it broke last time.
operator keeps going.
The Setup: You Talk To It, You Never Manage It
Wyrm is AI-native. There is no dashboard to tend. Your coding AI reads and writes it in plain language, automatically.
Local first
Runs on the operator's machine as an MCP server, backed by SQLite. The memory lives with them.
Plugs into the AI
Claude Code first, and also Copilot, Cursor, Windsurf and Codex. One memory across all of them.
Auto loads
A session-start hook primes Wyrm at the top of every session, before a word of work.
Reflexive
A standing instruction, consult memory first, makes the AI read and write Wyrm on its own.
Working Memory Plus Long Term Memory
Claude, the working memory
Brilliant, but ephemeral. The context window resets each session and compresses once it fills. On long or parallel work, the thread slips and the assistant loses the plot.
Wyrm, the long term memory
The durable layer beneath it. What the AI learns in a session is written down. Next session it reads it back. Decisions, failures and open threads all survive the reset.
What It Saves You
Lossless rehydration
Start each morning mid stride, not from zero
Counter-pattern memory
A failed approach is recorded and the AI is blocked from suggesting it again
Quests
Paused threads resume exactly where they stopped
Ground truths
The AI never works from a contradicted fact, cascade-aware
Recall and hybrid search
Ask in plain language, the relevant past work surfaces
The Results
77
Sessions of continuity
257
Threads resumed
13
Projects in one memory
450+
Distilled know how
Context held across 77 sessions and 13 projects, with no cold re-briefing
257 paused threads resumed exactly where they stopped
Recorded failures stopped the same dead ends from being retried
Mornings started with the full picture loaded in one call
Remembers Everything, Exposes Nothing
Wyrm is local first, and when you sync, the keys are yours. The system is built so the vendor cannot read your memory, by design. For an operator handling sensitive work, that is the difference between a convenience and a liability.
Tech Stack
ClaudeClaude CodeMCPSQLiteHybrid SearchZero-Knowledge Sync