About a month ago, the CPython project merged a new implementation strategy for their bytecode interpreter. The initial headline results were very impressive, showing a 10-15% performance improvement on average across a wide range of benchmarks across a variety of platforms. Unfortunately, as I will document in this post, these impressive performance gains turned out to be primarily due to inadvertently working around a regression in LLVM 19. When benchmarked against a better baseline (such GCC, clang-18, or LLVM 19 with certain tuning flags), the performance gain drops to 1-5% or so depending on the exact setup.

Earlier this month, I used Claude to port (parts of) an Emacs package into Rust, shrinking the execution time by a factor of 1000 or more (in one concrete case: from 90s to about 15ms). This is a variety of yak-shave that I do somewhat routinely, both professionally and in service of my personal computing environment. However, this time, Claude was able to execute substantially the entire project under my supervision without me writing almost-any lines of code, speeding up the project substantially compared to doing it by hand.

Suppose we have a large collection of documents, and we wish you identify which documents are approximately the same as each other. For instance, we may have crawled the web over some period of time, and expect to have fetched the “same page” several times, but to see slight differences in metadata, or that we have several revisions of a page following small edits. In this post I want to explore the method of approximate deduplication via Jaccard similarity and the MinHash approximation trick.

I worked at Stripe for about seven years, from 2012 to 2019. Over that time, I used and contributed to many generations of Stripe’s developer environment – the tools that engineers used daily to write and test code. I think Stripe did a pretty good job designing and building that developer experience, and since leaving, I’ve found myself repeatedly describing features of that environment to friends and colleagues. This post is an attempt to record the salient features of that environment as I remember it.

I was recently introduced to the paper “Seeing the Invisible: Perceptual-Cognitive Aspects of Expertise” by Gary Klein and Robert Hoffman. It’s excellent and I recommend you read it when you have a chance. Klein and Hoffman discuss the ability of experts to “see what is not there”: in addition to observing data and cues that are present in the environment, experts perceive implications of these cues, such as the absence of expected or “typical” information, the typicality or atypicality of observed data, and likely/possible past and future time trajectories of a system based on a point-in-time snapshot or limited duration of observation.