Pairing with a yes-machine
Pairing with an AI feels like pair programming. It isn't. There's one prior in the room, and it's trained to agree. This post makes the case for mob programming and spec-driven development as the structural fix.
AI development
The AI skills gap
In 40+ interviews, senior engineers from major banks and consultancies showed strong backgrounds but little real AI fluency. No RAG, no agent frameworks. The gap isn't about skill, it's about exposure.
Building a customer support AI agent that learns before it speaks
A customer support AI agent built in stages: shadow mode first, internal notes second, auto-send only after the data earns it. A walkthrough of the architecture, the knowledge base design, and the lessons that held up.
Architecture always leaks
Auditing data-heavy companies reveals the same pattern: asynchronous data processing crammed into the synchronous web stack. The contention shows in performance, delivery, and team dynamics. Isolation fixes all three.
The end of the all-you-can-eat buffet
The all-you-can-eat era of AI is ending. Compute constraints, heavier models, and a fully hooked user base are pushing providers toward pay-as-you-go. That shift will force better choices, smaller models, and fiercer competition between tools.
ElevenLabs: voice cloning, agents, and what they mean for your product
Voice is where AI product differentiation is heading. This post walks through ElevenLabs voice cloning and conversational agents in enough detail to evaluate whether the technology is ready for your use case.
"Good news, I built it in Lovable.": an engineer's guide to surviving that sentence
Business users love Lovable. Engineers tend to panic. A real-world case study of how to wrap an AI builder in guardrails so non-technical teams can move fast without quietly rewriting the systems that give your product its edge.
Python as the new Latin
I used to teach people to code. And looking back, I was teaching students to write it by hand while the tools that write it for them were getting better every single month. So what should a coding classroom actually look like now?
Your limit will reset at 12pm
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.
The SaaS audit bingo card: insights after auditing 180+ SaaS companies
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.
Your Claude Code is burning through tokens: here's how to fix 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.
Agentic engineering is a bottleneck
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.
Stop calling it vibe coding
"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.
Beyond prompting: read, verify, implement, learn
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.
Nobody learned a city from a map
The fastest way to learn agentic development is to stop studying it: move in, extract repeatable patterns into skills, and reflect to compound.