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I've long been convinced that AI focus needs to be balanced and look at the processes upstream of sw development. There's incredible potential to improve product and strategy work and very little evidence of any effort going into it. This isn't entirely OpenAI or Anthropic's fault. Product work requires crossing organizational boundaries, querying fragmented tooling, talking to people, and continuously defining and refining strategy. None of this context is readily structured for an AI model to consume; it remains siloed, undocumented, or locked inside people's heads. Melissa Perri talks about this (and other topics) in the latest episode of her Product Thinking podcast. The most interesting part is just 3 minutes into the episode (emphasis mine): [...] "Delivery of designs and code got very fast. Delivery of good decisions became the new bottleneck." Now, I don't think this bottleneck is new. It's exactly what I talk about in the build trap. Wh...

A while back I listened to this conversation between Martin Fowler and Luca Rossi on the refactoring podcast. Since then I made a note to write down the most important things I learned (or re-discovered), but always postponed until today I was able to make some time for it. Here's my take on the two most important things in that one hour long conversation. Dosage The concept of dosage is very familiar and very important to me. I've already written about it, under the name quantity . I appreciate how Martin Fowler was able to find a better (and fancier) name for it. Context Software engineering, being as elusive as it is as a proper engineering discipline, is perhaps one of the most studied and written about fields of engineering . And yet, we struggle to come up with truly universal practices that work everywhere. One way to understand why some things work sometimes and don't work or fail miserably other times is by considering the context that was present when the solu...