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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.
More in technology
Twenty years ago, Git was born. How did this unlikely "information manager" take over the world?
Plus the exciting launch of the Centre for British Progress
Conversations on the Traction Heroes podcast are proving highly relevant. To wit, episode 7 delves into how the questions we ask ourselves influence our ability to act skillfully. This came up in the context of dealing with uncertainty. Specifically, Harry brought to the conversation the following fragment from Rich Diviney’s book Masters of Uncertainty: To remain grounded in the sphere of what you know and control, cultivate a habit of asking yourself better questions. The brain operates with a question-answer mechanism: it continuously assesses the surroundings with questions and then conjures answers. This typically happens subliminally and instantly, faster than you can notice. You can, however, participate consciously in this process and influence your thoughts and feelings by introducing your own questions. When you introduce a question into conscious thought, the mind is compelled to come up with answers. The problem is that it’s all too common to default to reactive, negative, and sometimes emotion-laden questions such as “Why am I so bad at this?” or “Why can’t I succeed?” Your brain will immediately begin to give you answers to these—yet the answers will not lead to enlightenment. Rather, they will spiral you into further negativity and anxiety. Masters of Uncertainty steer their thoughts with better, more effective questions — questions that center on what they can ascertain and influence. Well aware that knowledge morphs into power, they prime their minds with inquiries like “What do I know? What can I control?” The brain can’t help but answer these questions in ways that put you at the helm. These are the inquiries that empower and hone your focus on traversing uncertainty, not succumbing to it. I hadn’t heard of Masters of Uncertainty before, but I’m reading it and hope to do a book notes post soon. Working with uncertainty is especially relevant now, given the changes rocking global markets. At least part of the secret to navigating turbulence is learning to keep your cool. It’s not a new idea (the Stoics and Buddhists said this centuries ago) but it’s worth revisiting — and internalizing. Traction Heroes ep. 7: Better Questions
A conventional model rocket engine is simple combustible solid fuel (black powder or more advanced composites) molded into a cylinder that uses expanding gas to produce thrust. Though it is minimal, there is some danger there. An alternative is compressed gas, which will also expand to produce thrust — just without the explosive chemical reaction. […] The post A homemade launchpad for compressed air-powered rockets appeared first on Arduino Blog.
