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Notes from the studio.
Writing on technology, design, VR, AI — and what it takes to ship them. Mostly long, sometimes short, occasionally wrong.
Journal · · 5 min
OpenAI Dots and the AI agents that keep working when you close the laptop
OpenAI’s new Dots run on their own cloud computer and keep going after you log off, joining Meta’s Muse, Google’s Gemini Spark and Microsoft’s Scout. What they do, why individual users in Europe can’t get them yet, and what to delegate, and never delegate, to one. Journal · · 5 minAMD is buying World Labs: what AI world models mean for 3D, VR and virtual tours
AMD will pay about $8.2 billion for Fei-Fei Li’s World Labs, the startup that turns photos and text into walkable 3D worlds. Why a chipmaker wants it, and what generated worlds can and cannot replace in 3D capture, VR and 360° tours. Journal · · 4 minGive an AI agent a goal and a way in, and it will use both
An OpenAI agent doing harmless research got into an Australian government portal it was never meant to reach. A Gemini model in a security test guessed its way into three real companies. Neither was malicious. That is exactly the point — and it changes how agents should be built. Journal · · 4 minThe EU AI Act after the Omnibus: what applies now, and what moved
The high-risk rules moved to December 2027 and August 2028. The transparency rules did not move at all — they have applied since 2 August. A plain map of where the EU AI Act stands after the Digital Omnibus, and what a Portuguese company shipping AI should do with the extra time. Journal · · 4 minApple will train its AI on your data — if you say yes
With iOS 27, Apple's promise that your data is never used to train its models gained a clause: unless you choose to help. Opt-in is the decent version of this. It is also a reminder that a privacy promise written in a policy is only as strong as the next policy. Journal · · 9 minRAG vs Fine-Tuning vs Prompting: How to Choose an AI System for Your Own Data
RAG, fine-tuning, or better prompting? A 2026 decision guide for building an AI system on your own data — costs, trade-offs, and when each wins. Journal · · 6 minAI coding agents didn't replace us — they changed what a day looks like
AI coding agents went from party trick to a normal part of how a small studio ships. An honest account of what they are genuinely good at, what they are still bad at, and the real shift underneath: the work is no longer writing the code — it is verifying it. Journal · · 4 minThe most-watched AI on earth is calling offsides
At this World Cup, a goal is judged in seconds by cameras, a sensor in the ball, and a 3D model of a player's shoulder. The lesson for anyone building AI is hiding in plain sight: do less, perfectly. Journal · · 4 minTeaching a FoodAI to dream in chocolate
Ateliê de Ideias is a FoodAI built into the Delicious Diamonds studio — a creation assistant that turns a chef's mood board into a first bonbon concept. Not a machine that makes chocolate; an apprentice that never runs out of ideas. Journal · · 8 minThe EU AI Act for teams shipping ML features: a builder's orientation
Most ML features you ship are minimal or limited-risk under the EU AI Act, but you still owe users transparency. Here is how the risk tiers actually map to product decisions — from an engineering team, not a law firm. Journal · · 7 minDo you need an AI agent? Mostly no.
Most features founders want to make "agentic" are better as a scripted workflow, a form, or a small ranker — cheaper, testable, explainable, and deterministic in production. Here's where an AI agent actually earns its place, and where it doesn't. Journal · · 8 minHow to Make Your Site Answerable by AI: A Practical Guide to Generative Engine Optimization
To get your site cited by ChatGPT, Claude and Perplexity, write answer-shaped content, ship clean semantic HTML and JSON-LD, and publish an llms.txt directory plus a dynamic llms-full.txt. Here is the practical checklist we run on our own site. Journal · · 7 minMachine learning vs LLM recommender systems: when classic ML wins
Machine learning vs an LLM recommender system: for ranked suggestions, classic ML usually wins on cost, latency, explainability and cold-start. Here's how to decide.— The journal, by email
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