Add Context Engineering reading workspace

MEMORY.md

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 # Reading Memory Log
 
+## Context Engineering: Engineering Information for AI Systems — J.C. Ködel
+- **Status**: In Progress
+- **Current Position**: Chapter 1 of 36 read; active-recall review awaiting reader responses
+- **Folder**: /library/Context Engineering/
+- **Source File**: /library/Context Engineering/source-file.pdf
+- **Structure**: 36 top-level sections; Sections 1–2 are orientation and the technical argument begins in Section 3; contents indexed in /library/Context Engineering/book-structure.md
+- **Current Notes**: /library/Context Engineering/Chapter-01/chapter-notes.md
+- **Last Updated**: 2026-10-01
+
+### Chapter Index
+
+#### Chapter 1 — About the Author
+- Notes File: /library/Context Engineering/Chapter-01/chapter-notes.md
+- Core Thesis: Pending post-reading review.
+- Key Concepts: Author credibility; maintaining systems; production evidence; information supplied to AI.
+- Action Item: Pending post-reading review.
+
+### Running Threads
+- The author frames long-term system maintenance—not merely initial code production—as the source of the book's practical perspective.
+- The central claim to test is that the difference between consistent AI output and an expensive guess usually lies in the information supplied to the model.
+- Distinguish evidence of the author's experience from evidence that the book's general claims are correct.
+- Reader finished Chapter 1. Three active-recall questions are saved in /library/Context Engineering/Chapter-01/chapter-notes.md; responses are pending.
+
 ## Your Best Year Ever — Michael Hyatt
 - **Status**: In Progress
 - **Current Position**: Chapter 2 of 15 read; active-recall review awaiting reader responses

library/Context Engineering/Chapter-01/chapter-notes.md

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+# Context Engineering: Engineering Information for AI Systems — Chapter 01: About the Author
+- **Date Created**: 2026-10-01
+- **Status**: In Progress
+
+---
+
+## 1. Pre-Reading Briefing
+- **Core Question**: What experience and evidence standard does J.C. Ködel present as the basis for teaching context engineering?
+- **Key Points to Watch For**:
+  - The kinds of software systems the author has built and maintained.
+  - The distinction between producing code and sustaining a system over time.
+  - Why the author uses a long-running independently operated product as evidence of practical credibility.
+  - The claim that AI output quality depends heavily on the information supplied to the model.
+  - How the author distinguishes production experience, attribution, and unsupported theory.
+- **Context & Thread from Prior Chapters**: This is the opening section, so there is no earlier argument to connect yet. Treat it as a statement of scope, credibility, and evidentiary standards rather than as proof of the book's central claims.
+
+---
+
+## 2. Reading Review & Reflections
+- **Prompt Questions**:
+  1. Which parts of Ködel's background does he use to establish credibility, and what do those experiences suggest he values in software engineering?
+  2. In your own words, what does he claim usually separates a consistent AI result from an expensive guess?
+  3. What does the author's experience give you good reason to trust—and what does it not yet prove about the book's claims?
+- **User Key Takeaways**:
+- **Scaffolding & Feedback**:
+
+---
+
+## 3. Chapter Synthesis
+- **Core Thesis**: Pending post-reading review.
+- **Key Concepts / Mental Models**:
+- **Notable Arguments & Evidence**:
+- **Updates to Prior Understanding**:
+- **Weekly Action Item**:

library/Context Engineering/book-structure.md

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+# Context Engineering: Engineering Information for AI Systems
+
+- **Author**: J.C. Ködel
+- **Format**: PDF
+- **Length**: 371 pages
+- **Source File**: /library/Context Engineering/source-file.pdf
+- **Top-Level Sections**: 36
+- **Note**: Sections 1–2 are orientation/front matter; the main technical argument begins in Section 3.
+
+## Contents
+
+1. About the author
+2. Map of the trilogy
+3. How LLMs use context
+4. Tokens and context windows
+5. Memory and limits
+6. The context cycle
+7. Context rot: why large contexts degrade quality
+8. Token economics: the real cost of bad context
+9. Parametric calculation: cost of irrelevant context
+10. Prompt engineering vs context engineering: why the prompt became a second-order variable
+11. Specifications
+12. Living documentation
+13. ADRs
+14. Conventions
+15. Persistent context files
+16. Project organization
+17. Modularization
+18. Context for brownfield projects
+19. Context layers
+20. Context packing
+21. Context recovery
+22. Context validation
+23. Context compression
+24. Context isolation
+25. RAG vs direct context
+26. MCP and tools as dynamic context
+27. Context security and trust
+28. Where to start
+29. Development loops with AI
+30. Measuring context: how to evaluate whether your context improves results
+31. Principles applied: chat, IDE, terminal and CI
+32. Teams: context as a repository asset
+33. Preparing a project (from scratch and from a legacy system)
+34. A complete AI-guided implementation
+35. Post-mortem: where the context failed and how it was recovered
+36. References