Software

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.

Week 43 of 2024. Week 11 of RC.

Week 42 of 2024. Week 10 of RC.

How I built a command-line LLM on Apple Silicon for RC’s ‘Impossible Stuff Day’

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

A dynamic realtime raytracer in pure JavaScript you can run in your browser.

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

There is a trifecta of variables that just about everyone building generative AI systems cares about — cost, quality, and latency. As is often the case in computing, there is a trade-off between these properties. And all three are bought with the currency of performance.