Documentation Index
Fetch the complete documentation index at: https://docs.linksee.app/llms.txt
Use this file to discover all available pages before exploring further.
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superseded | Replaced by a newer decision or approach |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"
}
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.
