We’ve Been Here Before: AI, Calculators, and the Shifting Floor of “Real” Skills
March 05, 2026
Executive Summary: Every generation of tooling redraws the line between foundational knowledge and mechanical busywork. When calculators arrived, educators panicked that students would forget how to think mathematically — yet the engineers who learned on calculators built systems their predecessors couldn’t have imagined. The current worry that AI will leave programmers helpless if it goes down deserves scrutiny, because it’s essentially the same argument. The real question isn’t whether to use the tool — it’s whether you understand enough to know when the tool is wrong.
The Failure Points We Build Into Our Systems: A Field Guide to Preventing Tomorrow’s Outages
February 01, 2026
Executive Summary: Every system failure you’ll experience next year already exists in your architecture today—embedded during design, implementation, or the last “quick change.” Drawing from real incidents including global medical system outages from single-line network errors and service account deletions causing cascading failures, this analysis presents a systematic approach to identifying and eliminating failure points before they manifest. By categorizing risks through the Cynefin framework and implementing structured identification processes, teams can transform from reactive firefighting to proactive resilience engineering.
The Three-Tier Strategy: Beyond the Reality vs. Fitness Debate
December 30, 2025
Executive Summary: The choice between deep understanding (reality perception) and quick fixes (fitness perception) is a false dichotomy that limits how we manage complex systems. This article presents a three-tier strategy that synthesizes both approaches: Tier 1 handles common issues through automated responses and pattern recognition; Tier 2 performs deep analysis when problems persist or novel issues emerge; Tier 3 focuses on continuous evolution, turning challenges into system improvements. This integrated framework provides clear guidance on when to use which approach, aligns technology and organizational culture, and creates systems that don’t just survive stress but actively strengthen through it.
Machine Learning as Cognitive Extension: When Computers See What We Can’t
December 27, 2025
Executive Summary: Machine learning and deep learning represent more than automation tools—they function as cognitive extensions that transcend human perceptual limits. These technologies address fundamental constraints in both reality and fitness perception frameworks: extending pattern recognition beyond human thresholds, enabling causal analysis across massive interaction networks, and discovering optimal strategies without experiential limitations. The most powerful implementations create cognitive partnerships where human contextual understanding complements machine high-dimensional pattern recognition, though challenges around data requirements, explainability trade-offs, and system complexity remain significant barriers to adoption.
Systems Thinking and Complexity Theory: Why Complete Understanding Is Impossible
December 23, 2025
Executive Summary: Reality perception approaches—trying to completely understand how systems work—hit fundamental limits in complex systems. These aren’t practical limitations we can overcome with better tools or more time. They’re inherent to how complex systems behave. Non-linearity means small causes create huge effects. Emergence means system behaviors can’t be predicted from understanding components. Feedback loops create unpredictable dynamics. The most effective approach isn’t choosing between deep understanding and practical action, but recognizing when each applies and integrating both thoughtfully.
Building Organizations That Learn from Chaos
December 20, 2025
Executive Summary: The strongest technical organizations aren’t those that prevent all failures—they’re the ones that learn fastest from them. This article explores how Resilience Engineering, DevOps practices, and High Reliability Organization theory combine to build teams and systems that genuinely improve through adversity. I examine the gap between how we think work happens versus how it actually happens, and show how service level objectives, error budgets, and blameless postmortems translate theory into practice. The key insight: incidents aren’t anomalies to be eliminated but inherent properties of complex systems that smart organizations convert into competitive advantages.
Cognitive Biases in Troubleshooting: When Our Minds Mislead Us
December 07, 2025
Executive Summary: Our brains evolved as prediction machines optimized for quick decisions in ancestral environments, not for diagnosing modern distributed systems. This creates systematic blind spots—confirmation bias, availability heuristic, anchoring, and hindsight bias—that significantly impair troubleshooting effectiveness. Rather than fighting these limitations through willpower, effective engineers implement structured processes: multiple hypothesis protocols, devil’s advocate roles, pre-mortem analysis, diverse response teams, and decision journals. The most successful approach recognizes that both reality-based and fitness-based troubleshooting approaches have their own bias vulnerabilities, requiring different countermeasures for each.
Beyond the Observer: Humanity as Elements Within Systems
December 05, 2025
In my ongoing exploration of reality perception versus fitness perception frameworks in system architecture, I’ve examined how these contrasting approaches shape our technical strategies, organizational structures, and cognitive processes. Today, I’ll expand perspective by incorporating a literary lens that beautifully illustrates these concepts: George R. Stewart’s pioneering eco-novel “Storm,” a work that offers profound insights for modern system thinkers.
The Cynefin Framework: Matching Your Approach to Problem Complexity
December 03, 2025
Executive Summary: Different problems need different approaches. The Cynefin Framework provides a practical way to match your response to the problem’s actual nature: simple issues need established procedures, complicated ones require expert analysis, complex problems demand experimentation, and chaos requires immediate action. Understanding which domain you’re in prevents costly mistakes like over-analyzing straightforward issues or trying to engineer solutions for emergent problems.
The Next Step in Automation
December 01, 2025
Executive Summary: When you automate system responses, you’re encoding your philosophy about knowledge into code. Some automation focuses on quick fixes without deep understanding (fitness-based), while other automation tries to model reality comprehensively (reality-based). But there’s a third way: antifragile systems that don’t just survive stress—they get stronger from it. This article explores how these approaches manifest in automation and how antifragile design transcends the apparent choice between speed and understanding.
Two Ways Engineers Think About Problems: Reality vs. Fitness Perception
November 29, 2025
Executive Summary: Engineers approach technical problems in fundamentally different ways. Some prioritize deep understanding—building comprehensive models of how systems actually work, tracing causal chains, and reasoning from first principles. Others focus on pragmatic outcomes—applying proven patterns, leveraging experience-based shortcuts, and getting systems working quickly. This article explores these two frameworks (reality perception vs. fitness perception), when each approach excels, and why the most effective teams integrate both rather than choosing one. A related tension between correctness (producing right results or failing) and robustness (maintaining operation despite failures) further shapes how we design systems. Understanding your natural tendencies helps you recognize when to deliberately switch modes—because the engineer who always seeks perfect understanding sometimes needs to ask “is good enough actually good enough here?” while the pattern-matcher occasionally needs to ask “is this the third time we’ve applied this same band-aid?”