- 5 Aug 2026 · 26 min read
AI audit with Screaming Frog: Claude reads the data cleanly and invents the diagnosis
I ran a technical SEO audit with Claude and Screaming Frog. The model processed 136 pages flawlessly, then invented rules like the 200-word threshold. What that means for AI-assisted audits.
- 17 Jul 2026 · 19 min read
Loop engineering: what the new way of working with AI agents is worth — and when a good prompt is enough
Loop engineering has AI agents work in cycles until a stop criterion takes hold. Which loop types exist, why loops fail in practice, and which seven questions to answer before your first own loop.
- 15 Jul 2026 · 12 min read
A skill knows nothing: brand knowledge as an OKF bundle instead of a pile of files in the chat
Skills execute, they know nothing. How to store brand knowledge in the Open Knowledge Format: a folder of Markdown files any model reads without conversion. With my complete GEO bundle to download under CC BY 4.0.
- 3 Jul 2026 · 14 min read
Not every rule belongs in the prompt: steering AI instructions and model memory
Six zones decide where an AI instruction belongs — from the product settings to the current prompt. How to instruct ChatGPT and Claude cleanly and keep their memory of you current.
- 27 Jun 2026 · 9 min read
When the AI answers too shallowly: eight techniques to make it think deeper
Strong prompts are not magic formulas. They give the model a mode of thinking. Eight techniques to open up a topic and secure a decision — with ready-made prompt templates.
- 23 Jun 2026 · 16 min read
The system prompt, taken from Claude Fable 5 — and which prompting techniques you can adopt from Anthropic
The complete 17,000-word system prompt was pulled from Claude Fable 5. What a system prompt even does, how far you can steer a model's answers — and eight techniques Anthropic builds into its models.
- 12 Jun 2026 · 10 min read
When your AI cuts corners: five techniques to get more out of every answer
Even a well-considered AI answer is often raw. Five techniques bring answers up to working quality, uncover hidden weaknesses in your ideas, and can be linked into chains.
- 1 Jun 2026 · 10 min read
Where the tokens go: steering the context of AI models
The context window is finite, and the fuller it gets, the worse the model works. How to make the context visible, know its working zone and place a task into it without wasting quality and tokens.