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James' Growth Diary

I am James, focusing on AI Agent learning and growth. I continuously update two series: “AI Agent Mastery Path,” which systematically outlines core theories and practices of agents, and “Claude Code Design Philosophy,” which deeply analyzes the design thinking behind top AI tools. Helping you build a solid foundation in the AI era.

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Latest from James' Growth Diary

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James' Growth Diary

Jun 23, 2026 · Artificial Intelligence

GSD Framework: Turning AI Programming into an Industrial Pipeline

GSD (Get Shit Done) reimagines AI programming as an industrial pipeline by explicitly managing the scarce context window, structuring work into a five‑stage workflow (Discuss, Plan, Execute, Verify, Ship), and using a .planning directory, XML plans, wave‑parallel execution, and Goal‑Backward verification to combat context rot and ensure observable, schedulable, recoverable development, while noting its high setup cost and strict discipline requirements.

AI context engineeringGSDGoal-Backward verification

0 likes · 20 min read

GSD Framework: Turning AI Programming into an Industrial Pipeline

James' Growth Diary

Jun 23, 2026 · Artificial Intelligence

Why Most CLAUDE.md Files Fail and How a 65‑Line Guide Got 180K Stars

The article examines why most CLAUDE.md files are ignored by Claude Code, explains the four failure patterns identified by Andrej Karpathy, contrasts ineffective generic rules with concrete project‑specific directives, and offers practical tips for writing concise, command‑oriented CLAUDE.md that actually guide the AI.

AI programmingAnthropicCLAUDE.md

0 likes · 11 min read

Why Most CLAUDE.md Files Fail and How a 65‑Line Guide Got 180K Stars

James' Growth Diary

Jun 21, 2026 · Artificial Intelligence

Why AI Needs a Structured Phase Workflow: From One‑Shot Prompts to the 5‑Stage Spec‑Kit Model

Dumping an entire task into an AI leads to misaligned requirements, hidden assumptions, and costly rework, while breaking the workflow into clear, gated phases—Discover, Define, Design, Develop, and Deliver—reduces error accumulation, enables independent verification, and keeps the AI on track.

AI workflowSpec-Kitphase-based development

0 likes · 17 min read

Why AI Needs a Structured Phase Workflow: From One‑Shot Prompts to the 5‑Stage Spec‑Kit Model

James' Growth Diary

Jun 21, 2026 · Artificial Intelligence

Why YC CEO Garry Tan Claims 810× Productivity with GStack

The article dissects GStack, a prompt‑driven Claude Code workflow that structures AI assistance into virtual team roles, offers dozens of slash commands, and delivers claimed productivity gains of up to 810×, while detailing its technical design, safety layers, and tool compatibility.

AI workflowGStackProductivity

0 likes · 12 min read

Why YC CEO Garry Tan Claims 810× Productivity with GStack

James' Growth Diary

Jun 21, 2026 · Artificial Intelligence

How ECC Turns Claude AI from a Swiss‑Army Knife into a Specialized Development Toolbox

The article analyzes the ECC (Everything Claude Code) augmentation framework, detailing its five‑point advantage, three‑layer design, AgentShield red‑blue audit subsystem, a step‑by‑step avatar‑upload workflow, engineering insights, real‑world integrations, and its current limitations.

AI augmentationAgent FrameworkAgentShield

0 likes · 18 min read

How ECC Turns Claude AI from a Swiss‑Army Knife into a Specialized Development Toolbox

James' Growth Diary

Jun 20, 2026 · R&D Management

OpenSpec Deep Dive: The Ultimate Form of Specification‑Driven Development

OpenSpec adds a specification layer to existing codebases, offering a pure, brownfield‑friendly, tool‑agnostic approach where specs act as contracts, changes become first‑class citizens, and incremental Delta Specs replace full‑spec rewrites, with detailed workflows, design insights, limitations, and a side‑by‑side comparison to Spec‑Kit.

AI-assisted codingBrownfieldChange Management

0 likes · 17 min read

OpenSpec Deep Dive: The Ultimate Form of Specification‑Driven Development

James' Growth Diary

Jun 20, 2026 · Artificial Intelligence

Task Atomization: Isolating AI Tasks into Independent, Clean-Context Units

The article explains how LLM context windows are a scarce resource plagued by breadth‑vs‑depth, long‑task attention decay, and serial‑parallel trade‑offs, and proposes task atomization—splitting work into independently loadable, executable, and verifiable units with isolated contexts and parallel sub‑agents—to achieve clean context, local rollback, and scalable performance.

AI workflowLLM contextmicroservice analogy

0 likes · 16 min read

Task Atomization: Isolating AI Tasks into Independent, Clean-Context Units

James' Growth Diary

Jun 19, 2026 · Artificial Intelligence

Why Smart AI Keeps Forgetting and How Externalizing Decisions to Files Solves It

The article explains that conversational consensus with AI is volatile because each new session starts with an empty context window, and demonstrates that writing architectural decisions and technical conventions into persistent files—such as CLAUDE.md, .cursorrules, or copilot‑instructions.md—ensures the AI consistently loads the same guidelines across sessions, improving reliability.

AI prompt engineeringClaudeconfiguration files

0 likes · 17 min read

Why Smart AI Keeps Forgetting and How Externalizing Decisions to Files Solves It

James' Growth Diary

Jun 18, 2026 · Backend Development

How Hermes Turns ACP into a First‑Class Agent Protocol

The article explains how Hermes implements the Agent Communication Protocol (ACP) to become a standard, first‑class Agent server for IDEs like VS Code, Zed, and JetBrains, detailing the protocol’s design, session management, event bridging, and IDE integration benefits.

ACPAgent ProtocolHermes

0 likes · 13 min read

How Hermes Turns ACP into a First‑Class Agent Protocol

James' Growth Diary

Jun 18, 2026 · Artificial Intelligence

Externalizing Agent Decisions to Files: How a Three‑Layer Prompt Architecture Drives Behavior

The article examines Hermes' design that moves all agent decision rules into editable text files, explains the three‑layer stable‑context‑volatile architecture, compares it with other frameworks, and shows how this approach improves transparency, controllability, and cache efficiency for AI agents.

AI safetyCache OptimizationHermes

0 likes · 11 min read

Externalizing Agent Decisions to Files: How a Three‑Layer Prompt Architecture Drives Behavior