Every case study, system breakdown, and field note, newest first and filterable by topic. No theory, no demos. Only systems that run in production and what it took to get them there.
This is the complete archive: all 347 posts across 40 topics, newest first, filterable by category. It holds the case studies, the model and tool comparisons, and the field notes from systems I built and run. For the curated view, where the writing sits alongside the free Claude Code skills, start at the Library instead.
The archive leans in four directions: AI Search Visibility (73), AI Agent Architecture (52), AI Tools (27), and AI Coding Agents (21). Those four account for most of what I publish, because they are where most of the client questions land.
Three places to start. The AI search visibility guide is the hub for the largest cluster and links out to every spoke in it. Grok Imagine vs Midjourney is the image model comparison, rebuilt against live leaderboard data rather than left to go stale. OutreachIQ is the longest running system breakdown here, from first prototype through to a public repo.
A full breakdown of the Camille Guillain AI Operating System: architecture, agent roles, memory, and how 70 to 80% of manual work was eliminated without losing the human voice.
A practical, opinionated comparison of Claude and ChatGPT from an AI systems builder with Anthropic certifications who still uses GPT models regularly. When each one wins, when neither fits, and how to decide for your business.
A clear definition of multi-agent AI systems in 2026: what they are, how the agents talk to each other, what they look like in production, and when one agent is enough.
A clear, practical definition of prompt engineering in 2026: what it is, what it is not, and what good prompt engineering looks like compared to writing a clever prompt.
A clear, business-focused explanation of the Claude API: what it is, how it differs from ChatGPT.com, what it costs, and what you can actually build with it in 2026.
A clear, practical definition of AI agents in 2026: what they are, how they differ from chatbots and traditional automation, and what they look like in real production systems.
A clear definition of AI workflow automation in 2026: what it is, how it differs from traditional workflow automation and RPA, and what it actually replaces inside a business.
Most people use AI reactively: you open a chat, you ask something, you get an answer. But the bigger time savings come from AI that runs without you. Here is how a scheduled Monday briefing replaced two hours of manual sector research every week.
Generic AI does not know your industry. It knows the surface of it. Here is the method I use to embed deep domain knowledge into a Claude system so the output reasons and writes like a practitioner, not a generalist.
Not every AI tool needs to live in the cloud. Here is how I built a local Python tool that turns raw CSV exports into formatted Word reports, with no internet connection, no subscription, and no manual formatting.
Generic AI content is not a model problem. It is a prompt problem. Here is exactly what makes AI writing sound like AI, and the system for fixing it at the source.
Every post here is about a system that actually shipped. Book a free call and let's talk about what could ship for you.
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