I’m passionate about building great products, teams and companies, using my experience across engineering, design and leadership to build and grow them.
This is where I share my thoughts and learnings on leadership, software engineering and product development.
The SaaS industry is facing its biggest disruption in decades. Stock crashes, mass layoffs, and the rise of agentic coding are changing who builds software and how. An engineering leader's perspective on what's happening and why developers need to adapt now.
6 miljoner i vinst. 6 miljoner i böter. När Sportadmin drabbades av ett dataintrång förlorade de ett helt års vinst i sanktionsavgift. Sårbarheten? En enda oskyddad variabel som legat öppen i 2,5 år. Sportadmins misstag är lätta att göra. Men om du bygger mjukvara som hanterar personuppgifter har ribban för säkerhetsåtgärder precis höjts. Vad kan vi lära oss från detta beslut?
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6 million in profit. 6 million in fines. When Sportadmin suffered a data breach, they lost an entire year's profit in penalties. The vulnerability? A single unprotected variable open for 2.5 years. Sportadmin's mistakes are easy to make, but if you're building software that handles personal data, the bar for security measures just got raised. What can we learn from this decision?
As agentic AI systems gain the ability to autonomously plan, build, and deploy software, programming is fundamentally changing. The bottleneck is no longer implementation, it's specification. This article explores why documentation and specs are becoming more valuable than coding skills, how teams can adapt to orchestrating AI agents rather than writing code, and why the practices developers have traditionally avoided are now their path to remaining relevant.
Good software needs to be easy to maintain, easy to test and is easy to scale. I've collected a guidebook of principles and best practices for how we can accomplish this.
What is an algorithm and which ones should you know about? What's a Big-O notation? This is the second step to writing better software and succeeding at technical interviews.
Solving engineering challenges efficiently require knowing your data needs. This is the first step to writing better software and succeeding at technical interviews.
