The low-value work left for humans
AI can do the entire job, except the low-value data shuffling.
stream
This is a raw stream of working notes. Some may be wrong, and mostly these will have little context, but I believe sharing raw thoughts is valuable in a way that polished blog posts are not.
Agentic UX ruins the coaching
One more thing that's annoying when using AI as a coach: any amount of reasoning tokens will make me switch away. Latency actually matters here. Two seconds is okay; twenty ruins the flow. There is also something more subtle. I don't take as much responsibility for my thoughts, am not as critical. I don't
AI coach
Can you build an AI coach? The obvious answer is yes: AI is great at spotting patterns in what you say, and you can ask it to use any style of coaching, any framework. An exec coach I used to work with thinks you can't build an AI coach, because true change happens in relation to another human.
What to automate, what to keep human
In May 2026 I sat on a panel at Latitude59 in Tallinn called "AI in Your Org: What to Automate, What to Keep Human", with the chief of staff at Hostinger and a co-founder of Sera Leads. Three very different company sizes: 900 people, 150, and 10. Below are my answers, pulled out of the discussion and
How to install a Claude Code skill from GitHub
Install a Claude Code skill from GitHub as a standalone skill or through a plugin marketplace, with current Desktop and CLI steps.
Places to put the AI
Every product team building with AI faces the same question: where does the AI interaction go? Not the model or the prompt, but the affordance: the surface the user sees and interacts with. For my own reference and discussions I wanted to capture these. Even though the chat box is where this all got started, these are not stages of
Building a Google Slides renderer with coding agents
When I read about the Software Factory concept from StrongDM AI, I was intrigued. In part because of the ability to produce an impressively large and complex component. But even more so with how little it might take: could you really do that simply by taking pre-existing specs from the internet and have agents search the program space for
Search in program space
Coding agents run a search in program space. That's the closest analogy I can find for how to use agents productively. If you think of it as an assistant to whom you give tasks, you'll generally be in too tight a loop, giving feedback every few minutes. But the search analogy forces you to consider: 1.
Three categories of guardrails
There are only three categories of guardrails to prevent harm from agents. First, relying on hard constraints to only allow certain kinds of behaviour. For example, limiting which tokens can be decoded (structured output) or exposing only a specific set of tools to an agent. Assuming correct implementation, these guarantee certain behaviours won't be possible. Second, the LLM&
Core vs edge agents
As our employees use Claude Code for various tasks, we keep seeing agents repeat the same work. Summarizing a customer's state across Salesforce, email, product analytics, usage data: every agent pulls together the same picture from scratch, every time. Martin Kosk, our enterprise architect, proposed a useful frame for thinking about this: core vs. edge. Edge is the