I recently used Claude to do the annual report for my holding company. It's an extremely simple company. There are no meaningful decisions to be made here. One invoice this year, and it's obvious how it should be handled. Still, Claude saved me probably an hour of searching for the correct way of doing things. I
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
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.
Install a Claude Code skill from any GitHub repo: the desktop steps, the two CLI commands, and why Claude keeps asking for permission afterwards.
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
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
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.
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&
I committed to sharing something every day in March about our AI transformation at Pactum. The pace is high, not just internally but in the entire world, so there's a lot that happens and a lot to share. This is the week 1 recap, pulling together what I've learned across the posts, the meetings, and the
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
