The blog · since 2015
Essays, playbooks and field notes from our CTOs, architects and leads. Written for people shipping real product.
The bottleneck in software engineering has shifted from typing to understanding. As AI handles implementation, engineers who thrive will be those who recognise patterns, curate guardrails, and connect technical decisions to business outcomes.
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 180+ 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.
"Vibe coding" has become shorthand for bad engineering to some people, but does the label hold up? This post unpacks how a playful term coined by Andrej Karpathy became a verdict, and why that's costing teams more than they realise.
LLMs generate code fast, but knowledge debt accumulates quickly. The fix is living documentation, and this post shows how to turn your LLM into the partner that maintains it automatically.
The fastest way to learn agentic development is to stop studying it: move in, extract repeatable patterns into skills, and reflect to compound.
Ever wanted to build your own Google Docs-style document service? This post introduces WOPI, the open protocol that lets web applications embed Office document editing directly in the browser, while keeping your app in control of storage and permissions.
A mobile app, past its usefulness, was days from being phased out. One email reversed the decision. No discussion. No input from engineering. This is what happens when decision-making drifts too far from the work.
In part three, we look at both sides of the AI contribution debate. A working patch, real demand, never submitted, rejected because AI was involved. But maintainers are unpaid volunteers, and AI halved the cost of contributing without touching the cost of review. Both sides have a point.
We smile at the 18th-century crowd for being swept up by a box with a man inside, yet today it's easy to hand ChatGPT a vague idea and treat the PRD it returns as gospel. Generative AI is genuinely powerful. We get the best from it when we bring both enthusiasm and a critical eye.
Developers don't skip standards because they're careless, they skip them because there are fifteen things to remember and the code was the hard part. The real question isn't which tasks your LLM handles well. It's what's still slipping through ungated.
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