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Why AIMake Uses Evergreen Notes
How AIMake uses evergreen notes to maintain durable company knowledge with source links, clear scope, and AI-assisted review.
Andy Matuschak’s evergreen notes framework describes notes that evolve, accumulate, and contribute across projects. Its core principles include atomic scope, concept-oriented writing, dense links, and associative organization.
AIMake uses Evergreen Notes as the product term for durable, revisable company knowledge.
Definition
An Evergreen Note is a maintained knowledge unit for one coherent concept, decision, workflow, or open question. A useful note should:
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cover a clear and limited subject;
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be understandable without replaying the original conversation;
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link to supporting evidence and related notes;
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distinguish facts, interpretations, assumptions, and open questions; and
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be revised when the underlying evidence or decision changes.
Why AIMake Uses This Model
The label Docs describes a storage format. Evergreen Notes describes a maintenance model.
Company knowledge changes as repositories, customer evidence, implementation details, and decisions change. The Evergreen Notes model makes that lifecycle explicit: notes should be scoped, linked, reviewed, and updated rather than treated as finished records.
This supports AIMake’s practices for making decisions in context and communicating enough context to act.
Sources and Notes Are Different
Repositories, pull requests, customer conversations, uploaded artifacts, research links, and channel transcripts are source material. They preserve evidence about what happened or what was observed.
Evergreen Notes preserve the current operating meaning derived from those sources.
Source material
Evergreen Note
Preserves original evidence
States the current concept, decision, or workflow
May be long, incomplete, or chronological
Should be concise and concept-oriented
Usually remains unchanged
Is revised as knowledge changes
Establishes provenance
Links back to supporting sources
In AIMake, GitHub evidence, conversations, and artifacts remain available as sources. Notes link to those sources instead of replacing them. Artifact-bound channels preserve discussion and decision history alongside the note.
Role of AI
AI can help maintain Evergreen Notes by:
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retrieving related notes and source evidence;
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proposing links or identifying duplicate concepts;
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summarizing long discussions;
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detecting contradictions or stale statements; and
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drafting updates for review.
AI should not:
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promote an unsourced claim to a known fact;
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remove links to original evidence;
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merge unrelated concepts into one broad note; or
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silently rewrite a decision without preserving what changed.
The AIMake agent model requires agents to search existing context, separate known facts from exploratory ideas, and report missing knowledge rather than inventing it.
Recommended Structure
A practical Evergreen Note can use the following structure:
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Title: the concept, decision, or workflow;
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Summary: the current operating meaning;
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Evidence: links to repositories, conversations, research, or artifacts;
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Status: known facts, assumptions, and open questions;
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Related notes: adjacent concepts and dependencies; and
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Review trigger: the event or cadence that should prompt an update.
Create a sibling note when new information has a different purpose, owner, audience, evidence boundary, or update lifecycle. Link the notes instead of forcing them into one hierarchy.
Maintenance Workflow
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Capture the original source.
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Create or update the smallest coherent note.
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Link the evidence and related notes.
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Record discussion in the artifact-bound channel when collaboration is needed.
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Review the note when new evidence or implementation changes the current understanding.
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Preserve the reason for material revisions.
Further Reading
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Andy Matuschak’s notes on evergreen notes - The source framework for evolving, atomic, concept-oriented, and linked notes.
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Decision-Making for Startups - A practical framework for making and revisiting decisions with appropriate speed.
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Communication for Startups - How written context supports alignment, action, and trust.
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AIMake Agents - How AI agents should use project evidence, notes, tasks, and proof without inventing context.
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