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Brian Weisberg edited this page Feb 21, 2026 · 14 revisions

Welcome to the Knowledge Base Wiki

This wiki is a curated collection of research, analysis, and strategic guides covering AI agents, competitive intelligence, premium branding, and behavioral science.


⭐ Featured Resources

A comprehensive, 25-lecture knowledge base on experimentation methodology, statistical techniques, and organizational implementation. This collection synthesizes academic research, industry best practices, and real-world case studies from Netflix, Airbnb, Spotify, Microsoft, Amazon, and Vanguard. Each lecture provides rigorous statistical foundations and practical implementation guidance for analytics professionals, data scientists, and business stakeholders.

Key topics covered:

  • Statistical foundations: Sequential testing, CUPED/CUPAC, power analysis, variance reduction
  • Email experimentation in the privacy era: Apple MPP, incrementality measurement, fatigue analysis
  • Advanced methods: CATE analysis, multi-armed bandits, causal inference
  • Organizational excellence: Operating models, governance, stakeholder communication
  • Regulated experimentation: Model Risk Management (SR 11-7), Fair Lending, Conduct Risk
  • Future directions: AI-powered design, privacy-preserving techniques, continuous optimization

67 detailed documents covering statistical methods, organizational frameworks, regulatory compliance, and industry best practices.


The most comprehensive synthesis of Anthropic's engineering blog and research publications. This definitive reference covers 14+ articles spanning December 2024 through January 2026, plus official documentation on prompt engineering, evaluations, and deployment. Essential reading for data analysts and teams building AI-focused analytics workflows.

Key topics covered:

  • Context Engineering & Prompt Design
  • Agentic Systems & Architecture Patterns
  • Tool Use, MCP & Advanced Orchestration
  • Agent Skills & the SKILL.md Pattern
  • Multi-Agent Systems & Research Architecture
  • The Claude Agent SDK & Claude Code
  • Evaluation Frameworks & Benchmarking
  • Safety, Alignment & Interpretability Engineering
  • Inference, Deployment & Cost Optimization
  • Practical Templates, Prompt Examples & Code Snippets

The definitive technical deep-dive into Claude Code's autonomous development platform. This exhaustive guide covers the complete evolution from Sonnet 4.5 to 4.6, Model Context Protocol (MCP), programmatic tool calling, Agent Teams architecture, and advanced orchestration patterns. Essential reading for developers, platform architects, and technical leaders implementing AI-powered development workflows.

Key topics covered:

  • Timeline of innovations: Sonnet 4.5 β†’ Opus 4.5 β†’ Sonnet 4.6 (Feb 2026)
  • Autonomous orchestration: Skills, Sub-Agents, and Agent Teams
  • Claude Cowork desktop automation and browser integration
  • Model Context Protocol (MCP) deep dive and tool discovery
  • Programmatic Tool Calling (PTC) and sandboxed execution
  • State management: Checkpointing, worktrees, and rewind mechanics
  • Context compaction algorithms and session memory
  • Peer-to-peer mesh architecture for multi-agent coordination
  • Event hooks, SDK extensibility, and CI/CD integration

Part I: Strategic guide for technical leaders and LLM experts Part II: Deep architectural specification for developers


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