Essay

This is a synthesis of ideas I’ve overheard, read, discussed, and learned first-hand. It draws on over three years exploring LLMs for shaping code. My initial notes for this were part of an internal workshop I hosted for our engineering team at TORTUS Health, where we use and improve these techniques every day as we work to eliminate error in medicine. The shifting bottlenecks With the explosion in LLM capabilities, producing syntax got cheap. But good judgement did not. In many ways, AI makes the easy part easier and the hard part harder. ...

We all use LLMs to make code, but there are at least two distinct modes of working with them. Both are powerful, but conflating them is dangerous. It leads to burnout, loss of agency, security breaches, and disastrous outages. Instead, we want to enable better ideas, executed faster, at higher quality.

This week, whilst troubleshooting video encoding discrepancies for a film, I was reminded of the immense wizard power of debugging: the art of systematically bisecting your way to the root cause of a problem, then devising the cleanest fix. This is an art that cannot be taught and which few professions lead you to learn. Even among those that do, such as computer programming, only the most diligent practitioners come to master it, because it is at once unnatural and primordial. We can be sure that many humans will never in their lives enact this discipline, but also that it is fundamental to our success as a species. ...

A sorcerer’s guide to wielding chaotic and powerful magicks.

Notes on the interconnection of generative AI’s two leading paradims.

A byte-sized guide to developing your taste and agency at the world’s best programming retreat.

The AGI is dead. Long live the AGI.

What Paul Graham doesn’t tell you about delighting 7 year olds.

Initial thoughts on transforming bioengineering into software engineering.

The past 18 months in AI have been a whirlwind-clusterfuck of acceleration, innovation, and chaos.