3-Axis Classification - Linksee Memory

Every memory in Linksee is auto-classified along 3 orthogonal axes when stored. These are stored as queryable virtual columns, enabling precise filtering in recall.

Axis 1: Altitude (cognitive level)

How high-level is this thought?

Altitude	Description	Forgetting multiplier
`mission`	Why we exist	0 (never decays)
`strategy`	Positioning, GTM, roadmap	0.1 (very slow)
`architecture`	System design, tech choices	0.3 (slow)
`implementation`	Code, tasks, execution details	1.0 (normal)

Altitude affects forgetting speed. A mission-level memory about why the company exists will never auto-decay, while an implementation detail about a specific API call will fade naturally.

Axis 2: Type (what kind of thought)

What is this memory, cognitively?

Type	Description	Example
`question`	An open question	”Should we use REST or GraphQL for the sync layer?”
`comparison`	Evaluating alternatives	”freee vs MoneyForward — freee has better API docs but worse webhooks”
`decision`	A choice that was made	”Going with PostgreSQL over MySQL for the main database”
`work`	Work in progress	”Implementing the token-saving diff cache for read_smart”
`outcome`	Result of completed work	”Deployed v0.5.0 — all tests passing, 3 beta users onboarded”
`learning`	Insight or lesson learned	”FTS5 trigram tokenizer handles Japanese better than unicode61”
`note`	General note	”Team standup moved to Tuesdays”

Axis 3: State (lifecycle)

Where is this thought in its lifecycle?

State	Description
`open`	Not yet resolved
`decided`	Decision made, not yet implemented
`in_progress`	Currently being worked on
`done`	Completed
`stalled`	In-progress but untouched for 30+ days
`parked`	Deliberately set aside for later
`superseded`	Replaced by a newer decision or approach

consolidate automatically marks in_progress memories untouched for 30+ days as stalled.

Using axes in recall

Filter by any combination of axes:

{
  "query": "database",
  "altitude": "architecture",
  "mem_type": "decision",
  "mem_state": "decided"
}

This returns only architecture-level decisions about databases that have been decided — perfect for understanding why the current tech stack was chosen.

Practical filter patterns

I want to find…	Filter
Open questions	`mem_state: "open", mem_type: "question"`
Architecture decisions	`altitude: "architecture", mem_type: "decision"`
Stalled work	`mem_state: "stalled"`
Strategic insights	`altitude: "strategy", mem_type: "learning"`
Implementation details	`altitude: "implementation", mem_type: "work"`

Auto-inference

When you call remember, the 3 axes are automatically inferred from the content. You don’t need to set them manually — but you can override by including structured JSON in the content field.

​Axis 1: Altitude (cognitive level)

​Axis 2: Type (what kind of thought)

​Axis 3: State (lifecycle)

​Using axes in recall

​Practical filter patterns

​Auto-inference

Axis 1: Altitude (cognitive level)

Axis 2: Type (what kind of thought)

Axis 3: State (lifecycle)

Using axes in recall

Practical filter patterns

Auto-inference