MSP v0.8 · living seed proposal

MSP: Seed it till it grows

MSP is a seed-centered AI-native project advancement and evolution meta-framework, mainly for open problems. MSP is not a normal acronym; it is a deliberately overloaded term where Problem / Project / Product / Paper / Protocol / Artifact can coexist.

Seed-centered · open problems · AI-native evolution

One sentence

MSP is a seed-centered AI-native project advancement and evolution meta-framework, mainly for open problems. It uses seeds for strategic deferral and future recovery cues, then lets problems grow through real feedback and project/product/paper/protocol carriers.

MSP is not “a product with seeds.” It means the whole product / project / solution / skill / feature / paper / ... starts in seed form.

Seed is the primitive of MSP

Seed is a broad word. Its key function is strategic deferral: defer non-mainline complexity so the happy path can move fast while preserving future recovery cues. At the same time, seeds are future-evolution heuristics: strokes that gradually draw the project’s future.

Objects

project / product / solution / skill / feature / paper / workflow / protocol

Modifiers

speed seed / evolution seed / framework seed / surface seed / cognition seed

Function

Fold complexity, preserve growth sites, keep system integrity, accelerate project progress, and sketch the project future as growth heuristics.

Human-in-the-loop MSP process

Human gives the problem and judgment

The human provides problem/task information and selects bias / prior / hypotheses / heuristics / ... to confirm a minimal topo-complete framework.

Pick the first MVP feature

For speed, implementation may mainly use speed seeds. MVP carries concrete development tasks and real feedback.

Iterate seeds

List candidate seeds, turn selected speed seeds into evolution seeds, refine seeds for future growth, and even use framework seeds to iterate the basic framework.

Enter the next loop

Choose requirement → fast MVP → seed iteration. Seeds sprout automatically during MVP execution.

Division of labor

Seed

Future-oriented heuristics: strategic deferral, preserved possibilities, and recovery cues for later growth.

MVP

Concrete development tasks, real feature loops, and feedback.

Minimal topo-complete framework

Models the invariant bottom mechanisms behind the problem.

MSP Project is an AI-native carrier; Project itself is complete Infra

The priority of any specific product / UI / app / webpage is lower. What matters most is the complete Project Infra: object model, state, memory, seed registry, decisions, regeneration path, agent skills, feedback path, and iteration mechanism.

Project Infra

object model / memory / seed registry / decisions / regeneration path / agent skill / feedback loop

Flexible entrances

app / webpage / paper / whitepaper / social IM / API / agent workflow, even personalized interfaces.

Change process: treat ideas as seeds first

When a new idea appears, it should not automatically trigger large-scale rewrites. Put it as a seed in the right place inside the Project, operate and iterate related seeds, and let later MVP development plus agent judgment decide whether development or framework changes are needed—with human review for important changes.

Framework seeds can iterate the basic framework, but remain seeds

Framework seeds should be explained because they prevent MSP from being misunderstood as a single product process. But they remain seeds: candidates to be reviewed through MVP feedback, agent judgment, and human-in-the-loop review before changing the basic framework.

Minimal Seed ProjectMinimal Seed ProductMinimal Seed SolutionMinimal Seed SkillMinimal Seed FeatureMinimal Seed PaperSpeed SeedEvolution SeedFramework Seed

Final definition

MSP is a seed-centered AI-native project advancement and evolution meta-framework, mainly for open problems. It is a deliberately overloaded term: Problem / Project / Product / Paper / Protocol / Artifact can coexist, with Problem as the high-weight core and the other P-forms as carriers for growth.
It uses human-in-the-loop judgment, seeds for strategic deferral and future recovery cues, MVP loops to trigger real feedback, a minimal topo-complete framework to preserve system integrity, and ultimately lets open problems grow through project / product / solution / skill / feature / paper / ... into reusable, regenerable, multi-surface AI-native infra.