Architecture and Design Patterns

Software architecture is the art and science of making high-level structural decisions that shape how applications are built, scaled, and maintained. This collection examines architectural patterns, design principles, and the decision-making processes that separate sustainable systems from technical nightmares.

Beyond Patterns and Diagrams

Architecture isn’t about blindly applying design patterns or drawing fancy diagrams. It’s about understanding trade-offs: monoliths versus microservices, synchronous versus asynchronous communication, consistency versus availability. Effective architects make informed decisions based on constraints, not trends.

Core Architectural Concerns

Scalability and Performance drive decisions about how systems handle growth. This includes horizontal versus vertical scaling, caching strategies, and database architecture choices that emerge from understanding your actual bottlenecks.

Maintainability and Evolvability determine whether teams can ship features without constant friction. Clear boundaries, dependency management, and design principles like SOLID create systems that bend rather than break under change.

Operational Complexity is often underestimated. Sophisticated architectures that work beautifully in theory may create operational nightmares in practice. The best architecture balances technical elegance with operational simplicity.

Pragmatic Architecture

Articles in this section focus on architecture decisions grounded in real constraints: team size, organizational structure, technical debt, and business requirements. Topics include domain-driven design, event-driven architecture, resilience patterns, and the evolutionary approach to architectural decisions.

The emphasis is understanding why systems are built certain ways and making deliberate choices rather than following cargo-cult practices or defaulting to whatever framework’s creators recommend.

The Codebase Doesn't Know You Quit

The first four parts of this series treated legacy as something between me and myself: Past Self leaving code for Future Self, with an AI in the middle. That framing is incomplete. Code outlives employment, not just memory. The companies I worked for have forgotten most of what I did there. The repositories haven’t. This is the fifth part of the Code as Legacy series, about the legacy you leave when you’re not around to defend it.

The Machine Writes. The Legacy Is Still Mine.

Part three ended with me promising to stop adding to Past Self’s pile. I was already wrong. I’m not the only author anymore: Copilot and Claude finish methods before I’ve finished thinking, shipping code under my name with my git config as the committer. This is part four of the Code as Legacy series, about what changes when the author is partly a machine.

I'm Done Making Empty Promises

I’ve spent two articles in this series describing Past Self and the damage he left behind. What I haven’t said yet is the uncomfortable part: I am Past Self, right now, making the same promises he made. // TODO: fix this properly. We’ll clean that up after the release. I’ll write the tests when there’s time. This is the third part of the Code as Legacy series, and I’m deciding to stop.

My Biggest Enemy Writes My Code

Past Self wrote the code I maintain today. He had context I’ve lost, constraints I’ve forgotten, and reasons he never documented. He’s gone now, but the code isn’t. This is the second part of the Code as Legacy series, about the engineer who left me this mess, and what I’m handing Future Self in return.

The Code You Write Today Is Someone's Problem Tomorrow

The code you create is a valuable legacy — my author bio. Not marketing copy. It’s the most expensive lesson from nearly two decades in production systems. This article explains what it actually means when you’ve lived with the consequences.