architecture.
37 writings found
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The Theater of Computation: What Alan Turing's Story Still Teaches Us About Building Systems
Watching Breaking the Code reminded me that the principles Turing fought for—elegant abstraction and human dignity—still matter in system design.
AI-Assisted Development: The Taste Problem
Why coding with AI agents works brilliantly for implementation but falls apart for API design. Lessons from building real systems with Claude.
When Machines Write Code, Humans Must Learn to Judge
As LLMs generate more code, teams face cognitive surrender and debt proliferation. The future isn't about writing code, it's about verification.
When Agents Write Code, We Judge It: The Verification Economy
As LLMs generate code at scale, our job shifts from writing to verifying. What does this mean for how we organize teams and think about programming?
Treating AI Instructions as Infrastructure, Not Documentation
How encoding team standards as versioned AI instructions solves the consistency problem that plagues AI-assisted development workflows.
Making Team Standards Executable: Infrastructure for AI-Assisted Development
AI coding tools produce wildly different results based on who's prompting. Treating team standards as versioned, executable instructions solves the consistency problem.
Tests Are the Real Safety Net: Why Your AI Specs Need Executable Validation
Writing specs for LLMs is trendy. But without automated tests, you're flying blind. Here's why the spec document isn't your safety net.
Code Review, Observability, and the Cognitive Cost of AI Amplification
Rethinking code review as product judgment, observability as our new IDE, and whether AI tools extend our capabilities or replace them entirely.
The Apprentice Gap: Why Watching AI Code Matters More Than Ever
As AI agents automate more development work, we're creating a generation gap where juniors never learn the fundamentals. The ralph loop offers a solution.
AI as an Organizational Multiplier: Why Your Team's Experience Varies Wildly
AI amplifies what you're already doing. Why some teams see half the incidents while others face double, and what agent architecture teaches us about control.
AI as an Organizational Multiplier: The Case for Fine-Scoped Agents
How AI amplifies existing organizational practices, why averages deceive us, and the emerging patterns for safer agentic systems in production.
AI as an Organizational Multiplier: Why Your Company's Experience Will Be Wildly Different
AI amplifies what you already do, for better or worse. From agentic patterns to security constraints, here's what actually matters in production.
Knowledge Priming: Teaching AI Assistants Your Codebase Like You'd Onboard a Human
Stop fighting AI's generic patterns. Treat project context as infrastructure and watch code quality transform through systematic knowledge priming.
The Map That Became the Territory: AI, Specifications, and What We Mean When We Say 'I Built This'
On AI agents, observability, bespoke software, and the uncomfortable question of who actually built what when LLMs generate our code.
The Governor Is Gone: AI, Cognitive Limits, and the Mess We're Making
AI removed the natural ceiling on how much we can produce. Now the only limit is cognitive endurance, and most of us are blowing past it.