Case studies, system breakdowns, and field notes from real builds. No theory, no demos. Only systems that run in production and what it took to get them there.
A curated roundup of the best Claude Code skills repositories in April 2026, organised by use case: marketing, design, web development, research, project management, and mega-collections. Twelve repos, ranked and reviewed.
Andrej Karpathy listed the top mistakes every AI agent makes. A single CLAUDE.md file codified the fixes. Over 43,000 developers installed it in a week. Here is what it does, why it works, and how to use it.
Anthropic shipped Claude Opus 4.7 on 16 April 2026. Here is what actually changed: coding benchmarks up 13%, vision resolution tripled, a new xhigh effort level, task budgets, and what it means for production systems running on the Claude API.
Two separate leaks exposed references to Claude Opus 4.7, Sonnet 4.8, a new Capybara tier, and a next-gen family called Mythos. Here is what the leaks actually contained, what is confirmed, and what matters if you build on the Claude API.
Anthropic just shipped Claude Code Routines: saved automations that run on their cloud, triggered by schedules, API calls, or GitHub events. Here is what they actually do, how they compare to what existed before, and where they fit in a real automation stack.
No-code automation tools are great until they are not. Here is the honest framework for deciding when Zapier or Make.com is enough and when you need a custom AI system.
A practical, opinionated comparison of Claude and ChatGPT from a Claude-certified consultant who still uses GPT-4o 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.
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.
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.
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 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.
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.
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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