Test-n-Tell - tiny apps, Big ideas

  Nir Eyal’s Hooked made a huge impression on me. It shaped a large part of how I think about product creation. Not because I wanted to “hook” users at any cost, but because the book made one thing very clear: if you understand how to influence behaviour, you also become responsible for how you use that power.

  An AI agent is like a super obedient engineer. It does whatever you ask. It rarely challenges you. It rarely pushes back. It rarely says: “This is a bad idea.” And if it goes off script, well, that’s probably because your instructions were too vague. Finally, executives got what they always dreamed about: a builder who simply builds.

The job of a product manager is already changing. We use AI more and more in our daily work, and there is no sign of this slowing down. Writing, research, analysis, prototyping, summarising calls, checking requirements, preparing strategy docs, testing product ideas - all of it can already be supported by AI.

  A mistake many companies make when hiring Product Managers is that they overvalue industry knowledge.

  There’s a persistent myth in product development: that what you ship is the roadmap. In reality, what you ship is the organisation that built the roadmap.

One of the most common leadership anti-patterns in modern organisations is this: A high-performing individual contributor gets promoted into management… …and continues competing with their team on execution.

“Vibe coding”, building software by describing intent and letting AI generate, refactor, and orchestrate code, is quickly evolving into something more powerful: agentic coding. Systems that don’t just autocomplete code, but reason across files, plan changes, run tests, deploy, monitor, and iterate. If this trajectory continues, we are not just accelerating engineering. We are reshaping the entire product value chain.

  As AI moves from experimentation to production, many organisations discover that “adding AI” is not a feature - it is a fundamentally different product problem. This realisation has led to the rise of the AI Product Manager (AI PM).

  What is the difference between platform PM and any other? Is there a difference? Do platforms even need PMs, and how many?

  I used to love football! Watch it, play it, suffer from chronic underperformance of my fantasy team... It's during one of the games that I thought about how football could be a good metaphor for the business of building software products.

  I have built products in many different industries: HRtech, marketing, last-mile.. But one industry, where I really wanted to work, always eluded me - that's Education tech. The stars were never rightly aligned for me to work in EduTech until I decided to build my own EduTech app.

  Whether we like it or not, we all need to be competition-aware as PMs. Most of us have competitors, and understanding what they do might help us to win in the market.

  People come to product management from different fields, engineering being just one of them. This created a divide between technical and non-technical PMs. With the rise of AI coding tools, this divide is going away fast, as most PMs can now become technical PMs.

  How's an old song by Paul McCartney and a famous quote by William Blake relevant to product management and business in general? Yeah, it's one of "those" posts.

  When the job market gets tough and you can't move to pastures green - prioritise learning.

  A common misconception when people say Product-led org is to assume product managers are running things. That shouldn't be the case. Product-led is about the mindset of everyone.

  That's the first question you should ask when joining a new team or having a new team member.

  In this series, I've tested and reviewed multiple tools in the AI agents category. Some of them are useful, others less so, but one thing is certain: this category has a huge potential to change our whole industry and have major effects on people's roles.

  Writing is an essential PM activity - depending on the type of PM you are, you either write a lot or a lot a lot. Many PMs these days employ gen AI tools to help them write more or better, but they do not always get the outcomes they hoped for.

  What's the difference between B2B and B2C-focused product managers? Does this difference even matter in being hired and succeeding at a job?