The blog · since 2015
Essays, playbooks and field notes from our CTOs, architects and leads. Written for people shipping real product.
Ambient music started with a quiet record, a dead speaker and rain. Synthesis has always meant connecting what's already there; the hard part was making it. With setup decided once and Claude shaping the pieces, the gap between seeing an idea and running it has closed.
Every legacy codebase is a palimpsest: layers of decisions written on top of each other, none fully erased. Geoffrey Dhuyvetters argues that what looks like technical debt is often stratigraphy, and you read it before you rewrite it.
Most AI-powered customer support is optimised for deflection, not resolution. The problem isn’t bad agents, it’s architecture: no shared context, no real permissions, no escalation path that works.
AI removes bottlenecks until it reaches the one that doesn’t move: human cognition. The faster AI makes your system, the more your team’s mental capacity becomes the constraint. You can’t add more of it.
The claude -w flag spins up an isolated git worktree in seconds, so you can keep coding while a long-running task occupies your main session. No conflicts, no context pollution, no waiting.
When AI closes the execution gap, taste becomes the differentiator. Curation, judgement, and the willingness to say “not this” compound over time in ways that models can’t replicate.
In 2010, every business convinced itself it needed a mobile app. Fast forward to 2025, and the script is identical, just with AI replacing mobile as the technology everyone insists they can't afford to be without.
AI-generated content has made polished writing look suspicious. The deeper cost is the thinking you skip when you outsource the words that define your position.
The quick fix isn't cheaper. It's cheaper today. Bram Devries traces how deferred fixes compound into emergencies, and argues that naming the trade-off out loud is the only way to break the cycle.
Half of today's AI best practices are coping mechanisms for temporary scarcity, not timeless engineering insights. Geoffrey Dhuyvetters traces the arc from SMS bundles to token limits, and argues the price curve only goes one direction.
After auditing 190+ SaaS companies, the same patterns keep showing up: a CTO who does everything, documentation nobody updates, a backlog from 2019. Here's what the bingo card looks like, and what AI is changing about it.
Five idle plugins can burn 55,000 tokens before you type a word. Here's how to diagnose token consumption in Claude Code and cut overhead through plugin management, profiles, and context hygiene.
The standard AI-assisted dev loop has created a new bottleneck: us. Peter Eysermans describes how deterministic orchestration via n8n, with GitHub as shared memory, gets the human off the loop without sacrificing quality.
When an AI agent gets a requirement wrong, the mistake lives in the test assertions, not the implementation. Domain knowledge catches it, not coding skill.
The processes that got you here will eventually constrain you. Like a lobster shedding its shell, scaling companies must periodically dismantle what worked intentionally, not in panic. Constraints aren’t a failure; they’re proof you’ve outgrown your current structure.
AI has removed the junior work that used to build software engineers. Seven other industries faced the same disruption and didn't agree on an answer.
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