QED: AI Development Patterns
Getting Started
1.
Introduction
2.
Philosophy and Mindset
3.
Pattern Template
The Six Laws of AI-Era Software Engineering
4.
Overview
5.
Law 1: Context Is the Universal Bottleneck
6.
Law 2: Human Judgment Remains the Integration Layer
7.
Law 3: Architecture Matters More Than Model Selection
8.
Law 4: Build Infrastructure to Delete
9.
Law 5: Orchestration Is the New Core Skill
10.
Law 6: Speed and Knowledge Are Orthogonal
Patterns by Domain
11.
Architecture Overview
12.
Core Architecture
13.
System Architecture Diagram
14.
AMP Architecture Overview
15.
Thread Management at Scale
16.
Real-Time Synchronization
17.
Tool System Evolution
18.
Multi-Agent Orchestration
19.
Ink Yoga Reactive UI
20.
Emerging Patterns
21.
Collaborative AI Ecosystem
22.
Implementation Overview
23.
Execution Flow in Detail
24.
Initialization Process
25.
Real World Examples
26.
Claude Code vs Anon Kode
27.
Framework Selection Guide
28.
Framework Wars Analysis
29.
System Prompts and Model Settings
30.
Feature Flag Integration
31.
Building Your Own AMP
32.
Migration Strategies
33.
Operations Overview
34.
Parallel Tool Execution
35.
Lessons Learned
36.
Performance at Scale
37.
Observability and Monitoring
38.
Deployment Guide
39.
Performance Tuning
40.
Security Overview
41.
The Permission System
42.
Authentication and Identity
43.
Sharing and Permissions
44.
Team Overview
45.
Team Workflows
46.
Enterprise Integration
47.
From Local to Collaborative
48.
Quality Overview
49.
Risk Assessment
Patterns by Risk Profile
50.
Safe Starting Points
51.
Patterns Requiring Safeguards
52.
Critical Patterns
Patterns by Context
53.
Context Overview
54.
Agile Patterns
55.
Scaling Patterns
56.
Governance Patterns
57.
Compliance Patterns
Learning Paths
58.
Getting Started with AI Development
59.
Enterprise Adoption Journey
60.
Agency Playbook
61.
Migration from Traditional Development
Case Studies
62.
AMP Implementation Cases
Reference
63.
API Reference
64.
Taxonomy Guide
65.
Pattern Index
Analysis Queue
66.
Research Overview
67.
PRewrite: Reinforcement Learning Prompt Optimization
68.
Cloudflare Code Mode MCP
69.
Building Agents for Small Language Models
70.
Building Better Agentic RAG Systems
71.
Core Architecture - Agentic Systems
72.
Lessons Learned - Production Implementation
73.
Multi-Agent Research System
74.
Parallel Tool Execution
75.
Real World Examples - Claude Code
76.
Slash Commands vs Subagents
77.
The Lethal Trifecta for AI Agents
78.
The Permission System
79.
The Rise of Computer Use and Agentic Coworkers
80.
Two Experiments on AI Agent Compaction
81.
Beyond Chunks: Context Engineering
82.
Collecting All Causal Knowledge
83.
Command System Deep Dive
84.
Execution Flow in Detail
85.
Ink Yoga Reactive UI
86.
System Prompts and Model Settings
87.
Systematically Improving RAG
88.
AI Will Change How We Build Startups
89.
How To Become A Mechanistic Interpretability Researcher
90.
Understanding LLMs: Mechanistic Interpretability
91.
Evaluation Overview
92.
Psychology of Trust in AI
93.
ACE-FCA Context Engineering
94.
AI Coding Efficiency
95.
Google Gemini Nano
96.
Distributed Systems Patterns
97.
PRewrite: RL Prompt Optimization
Archive
98.
Previous Structures
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AI Development Patterns: A Practitioner's Guide
Command System Overview