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建立時間： 2026-03-27 來源： https://www.entropycontroltheory.com/p/a-note-to-my-subscribers-a-major?r=4fnt6n
Summary
Susan STEM announces a pivot from theoretical “Entropy Control Theory” research to building language protocol systems using Claude Skills as an execution layer. The central framework is the L–S–D cycle (Language → Structure → Scheduler → Feedback → Language) as the minimal metabolic loop of intelligence. The piece argues Claude Skills made the scheduler “real” rather than metaphorical — language can now directly execute, closing the theory’s feedback loop in real time.
Susan STEM 宣告從理論性的「熵控理論」研究轉向以 Claude Skills 為執行層的語言協議系統建構。核心框架是 L–S–D 循環（語言→結構→調度→反饋→語言）作為智能的最小代謝回路。Claude Skills 讓調度器從隱喻變成真實機制，語言可以直接執行。
Key Points
- L–S–D cycle: Language (perception interface) → Structure (compression into reusable patterns) → Scheduler (temporal orchestration) → Feedback → Language; “thought” = stability of this loop over time
- Claude Skills as validation: the first tool that lets natural language intentions be compiled into scheduled, executable, reflective cycles — theory began validating itself in real time
- Design principle: let it grow before hard-coding — observe where self-organization fails, and only crystallize structure at that boundary
- “Social Turing Machine”: collective intelligence experiments via shared language protocols — anyone who can articulate intent precisely can participate without a CS background
- AI-Native Architecture manifesto (Nagi Yan): the inversion from “AI as plugin in software” to “software as node callable by AI” — users think with systems rather than operate them
- LLMs as raw material, not products — meaning, structure, and ownership are sculpted by the individual; sovereignty belongs to the person generating the data
Insights
The L–S–D cycle is a useful conceptual frame even if the philosophical architecture is ambitious: perception feeds structure, structure feeds action, action generates new perception. The practical implication is that Claude Code / Claude Skills aren’t productivity tools but infrastructure that makes language protocols executable — which is a fundamentally different framing than “AI-assisted coding.” The “don’t hard-code early” principle maps cleanly to good software engineering: defer constraints until you understand the system’s actual boundaries.
Connections
Raw Excerpt
I stopped trying to design static systems and started designing language protocols — the rules by which meaning becomes action. What used to be a philosophical framework now runs as an experimental runtime.