This week’s reading list is about making complexity legible. Stage points toward a future where code review has to become more structured because AI will make code production cheaper than code comprehension. Milanović’s Laws of Software Engineering is a reminder that org charts and incentives shape systems more than we admit. And Shri Khalpada’s GPS explainer turns the blue dot on your phone back into physics, geometry, and clock precision. Three very different reads, one shared question: what happens when the hard part is no longer making more, but understanding what we have made?

From Process to Outcomes. The shift happening now is clear: we’re moving from hiring for process to hiring for results. AI accelerates this transformation through what Karpathy calls the autonomy dial, your ability to set how independent AI agents are in driving real business impact.

How I migrated this blog from a DIY stack to a new setup using Nobelium, a Notion-based starter that pairs Notion with Next.js. This new setup, deployed on Vercel, automates many tasks, including SSL certificate renewal and site deployment, allowing me to focus on writing.

LLMs as operating systems for computational intelligence, with prompts serving as the new system calls. This Software 3.0 approach allows for free-at-the-margin intelligence, with coding emerging as the first killer application due to its repeatable and verifiable nature.

Engineers often follow a pattern of approaching problem-solving in three ways: minimalist, maximalist, and pragmatic. The minimalist approach favors simplicity, while the maximalist approach embraces complexity. The pragmatic approach strikes a balance, using complexity only when necessary to deliver value.

In software engineering, perfection is often the enemy of progress. We chase grand rewrites and sweeping overhauls, only to get trapped by dependencies, shifting priorities, and endless delays. The real superpower of high-performing teams isn’t bold reinvention — it’s incrementality.

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