Notes / tag / appendices

#appendices

64 notes across 7 topics

# Agentic Ai Projects And Mastery

All Agentic Ai Projects And Mastery notes →

A. Agent Framework Comparison Matrix

A reference matrix comparing agent frameworks (LangGraph, AutoGen, CrewAI, custom) across state management, tool-calling model, and production-readiness, for quick lookup rather than narrative reading.

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B. Prompt Engineering Cheat Sheet

A condensed reference of prompt-engineering techniques (few-shot, chain-of-thought, structured output constraints) with when-to-use guidance rather than the full tutorial treatment given earlier in the book.

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C. Agent Design Pattern Catalog

A condensed reference table of every architecture pattern covered in Part 00 of AI Architecture & System Design, listing each pattern's applicability criteria and trade-offs in one scannable page for interview-day review.

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D. AI Security Checklist

A checklist reference for auditing an agent system's security posture - prompt injection defenses, tool-permission scoping, secrets handling - meant for a pre-launch review rather than first-time learning.

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E. Production Readiness Checklist

A pre-launch checklist reference covering observability, rollback plan, rate limiting, and on-call ownership for shipping an agentic system to production.

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F. AI System Design Interview Questions

A bank of practice system-design prompts specific to agentic AI, organized by difficulty, for timed self-practice ahead of an L6/L7 interview loop.

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G. OpenAI, Anthropic & Google API Comparison

A reference comparison of the OpenAI, Anthropic, and Google model APIs - tool-calling formats, context window and pricing tiers, and streaming semantics - for choosing a provider without re-reading three sets of docs.

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H. MCP Reference Guide

A condensed reference for the Model Context Protocol specification - message types, capability negotiation, and server/client lifecycle - as a lookup companion to the hands-on MCP server build in Part 00.

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I. AI Engineering Glossary

A glossary defining the agentic-AI terminology used throughout the book (agent, tool, orchestrator, grounding, hallucination, context window, etc.) for quick reference rather than sequential reading.

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J. Recommended Papers, Books & Open-Source Projects

An annotated reading list of foundational papers, books, and open-source projects referenced throughout the book, for readers who want to go deeper on a specific topic after finishing it.

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1 — CI/CD Platform Reference Architecture

A reference architecture diagram and component checklist consolidating the platform layers covered across the book.

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10 — GitHub Actions: Practice Exams

Three full mock GH-200 exams with detailed answer explanations for exam-readiness self-assessment.

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11 — GitHub Actions: Troubleshooting Playbook

A troubleshooting playbook for debugging pipeline failures, performance issues, security incidents, and production outages traced back to CI/CD.

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12 — GitHub Actions: YAML Reference

A YAML syntax quick-reference for GitHub Actions workflow authoring.

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13 — GitHub Actions Expression Cheat Sheet

A cheat sheet of GitHub Actions expression functions and operators.

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14 — GitHub Actions: Context Reference

A reference of every built-in GitHub Actions context object and its fields.

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15 — GitHub Actions: Marketplace Best Practices

Best practices for choosing, pinning, and auditing third-party Marketplace actions.

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16 — GitHub Actions: GitHub CLI (gh) Reference

A gh CLI command reference for scripting GitHub Actions and repository operations.

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17 — GitHub Actions: Common Error Messages

A lookup of common GitHub Actions error messages and their root causes.

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18 — GitHub Actions: GH-200 Exam Checklist

A final GH-200 exam-day readiness checklist.

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19 — GitHub Actions: MAANG Interview Checklist

A final MAANG CI/CD interview readiness checklist.

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2 — GitHub Actions: Migration Guide

A migration guide for moving existing pipelines from Jenkins, Azure DevOps, or GitLab CI to GitHub Actions.

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3 — Argo Workflows & Tekton Comparison Matrix

A side-by-side comparison matrix of Argo Workflows and Tekton across architecture, extensibility, and operational model.

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4 — Progressive Delivery Decision Matrix

A decision matrix for choosing among rolling, blue-green, canary, and shadow deployment strategies based on risk and rollback needs.

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5 — Software Supply Chain Security Checklist (SLSA, SBOM, Sigstore)

A checklist of SLSA levels, SBOM requirements, and Sigstore signing/verification steps for hardening a software supply chain.

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6 — DORA Metrics & Delivery KPIs

A quick-reference of the four DORA metrics and related delivery KPIs — see platform-engineering-fundamentals' DORA metrics chapter for the full derivation.

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7 — CI/CD Platform Maturity Model

A quick-reference recap of the five-stage platform maturity model introduced in Part I, for use as a self-assessment checklist.

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8 — GitHub Actions: GH-200 Exam Objectives

Maps the GH-200 exam blueprint — workflow authoring, security, automation, runner administration, and governance — to the chapters in this book.

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9 — GitHub Actions: Hands-on Labs

A set of 25 guided, hands-on labs built from realistic enterprise scenarios for GH-200 practice.

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# Internal Developer Platforms

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# Kubernetes Platform Engineering

All Kubernetes Platform Engineering notes →

1 — Appendix A: UML Cheat Sheet

Quick-reference summary of UML notation — class, sequence, and relationship symbols — for fast lookup while sketching an LLD design.

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2 — Appendix B: SOLID & GRASP Cheat Sheet

Quick-reference summary of the five SOLID principles and the GRASP responsibility-assignment patterns, condensed for interview recall.

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3 — Appendix C: Design Pattern Decision Matrix

Cross-references common design problems against candidate GoF patterns so the right pattern can be picked quickly instead of pattern-matched from memory.

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4 — Appendix D: LLD Interview Checklist

A pre-interview and in-interview checklist condensing the Chapter 1 framework into a single pass/fail list to run through before finishing.

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5 — Appendix E: Java Implementation Guidelines

Language-specific guidance for translating an LLD whiteboard design into idiomatic Java — interfaces, access modifiers, and collection choices.

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6 — Appendix F: C# Implementation Guidelines

Language-specific guidance for translating an LLD whiteboard design into idiomatic C# — properties, interfaces, and access modifiers.

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7 — Appendix G: C++ Implementation Guidelines

Language-specific guidance for translating an LLD whiteboard design into idiomatic C++ — ownership semantics, virtual dispatch, and RAII.

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8 — Appendix H: Go Implementation Guidelines

Language-specific guidance for translating an LLD whiteboard design into idiomatic Go — implicit interfaces, composition over inheritance, and goroutine-safe state.

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9 — Appendix I: Python Implementation Guidelines

Language-specific guidance for translating an LLD whiteboard design into idiomatic Python — duck typing, ABCs, and dataclass-based value objects.

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# Platform Engineering Fundamentals

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