How a physical metaphor—stacking paper branches into an “analog merge tower”—can help you understand, visualize, and ultimately tame complex Git histories shaped by email-era workflows and modern collaboration.
How visual “weather maps” of your codebase—powered by static analysis, LLMs, and incident learnings—help you spot and shrink bug storm systems before they become production outages.
How “shrinking” your system onto paper, visualizing dependencies, and using connascence can expose harmful couplings and guide safer, incremental refactors.
How working within paper-based constraints can unlock clearer layouts, better collaboration, and more creative software design than starting directly in digital tools.
Discover how a simple, paper‑first system—the Analog Flow Bench—can help you break through coding blocks, minimize friction, and consistently reach a deep flow state.
Learn how to use AI coding tools like a flight deck of instruments to manage complex refactors with more precision, visibility, and control—favoring incremental change over risky big-bang rewrites.
How to treat refactoring as a continuous, low‑risk part of software development by thinking like a kid laying out paper train tracks—small, sequential changes that add up to big improvements over time.
How to use tabletop, paper-token exercises to model distributed systems, explore microservice designs, and expose failure modes before you commit to Kubernetes and complex infrastructure.
Discover how a simple paper “mission log” can act as your control center for deep work—helping you time-block sessions, focus on single tasks, track attention, and build a repeatable ritual without complex software.
How to use a “field compass” mental model, AI tools, and living maps to navigate and debug huge, unfamiliar codebases without getting lost or overwhelmed.