Lena Park writes about software development practices, developer tools, and the culture of building software. A full-stack developer turned writer, she covers how engineering teams actually work: from architecture decisions to deployment strategies.
Shipping broken software isn't negligence. For most companies, it's the most rational decision they can make. Here's the math behind it.
More tools, more tabs, more plugins — yet less gets done. The science behind why minimal setups consistently outperform maxed-out ones.
The real reason engineers prefer dark mode goes deeper than eye strain. It's about cognition, contrast, and how the brain processes code.
It's not a bug. The randomness baked into AI language models is a deliberate design choice, and understanding it changes how you use these tools.
Cognitive Load Theory isn't just classroom psychology. The best engineering teams in the world are using it to ship faster, debug less, and think clearer.
Your brain encodes handwritten notes differently than typed ones. The reason is buried in neuroscience, and no app has figured out how to replicate it.
The same manipulative design tricks used on consumers are quietly running inside corporate software. Your employer probably already deployed them on you.
Adding more data to an AI model should make it smarter. Sometimes it makes it dumber. Here's the unintuitive math behind why.
Your $15/month note-taking app might be quietly destroying your best thinking. Here's what the research actually says.
Shipping a feature was never the point. For many tech teams, building it was.
Your phone's notification system isn't a communication tool. It's a behavioral conditioning engine, and the engineers who built it know exactly what they made.
The secret isn't discipline or better apps. It's a filtering architecture that mirrors how good software handles queues.
The secret productivity hack nobody's selling you: the most effective people aren't optimizing their tools. They're eliminating them.
The same engineers who built addictive interfaces are now designing tools to help you escape them. Here's the technical playbook behind both.
In a world drowning in productivity apps, top performers are quietly going analog. Here's the surprisingly technical reason why paper wins.
It's not about the models or the data. Most AI startups fail because they solve the wrong problem with the right technology.
LLMs don't look things up. They predict the next token. Understanding that distinction will change how you use every AI tool you touch.
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