Deep memory: why your AI co-worker needs to remember

Most AI tools forget everything between sessions. This fundamentally limits their usefulness.

The stateless problem

When your AI assistant doesn't remember that you prefer TypeScript over JavaScript, that your project uses Supabase, that you decided last week to deprecate the old API — it starts every conversation from zero.

You end up repeating yourself. Context is lost. Decisions are re-debated.

Three layers of memory

Daco Work implements a 3-layer memory system:

Chat history The immediate conversation context. What you said, what Daco said, what decisions were made in this session.

Semantic facts (pgvector) Long-term factual memory. "The project uses Next.js 15." "Dax prefers direct communication." "The VPS has 8 cores and 32GB RAM." These facts are extracted from conversations and stored as embeddings.

Working memory Active project context. Current phase, open issues, recent decisions, deployment state. This is the "what am I working on right now" layer.

Why it matters

With deep memory, Daco doesn't just generate code — it generates code that fits your project. It remembers your naming conventions, your architectural decisions, your deployment preferences.

It's the difference between an AI that writes a generic React component and an AI that writes a component using your design system, following your file structure, with the testing patterns you established.

Continuity across sessions

When you close your laptop and come back tomorrow, Daco picks up where you left off. No re-explaining. No lost context. Your co-worker remembers.