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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.
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Today I learned that Kagi uses Yandex as part of its search infrastructure, making up about 2% of their costs, and their CEO has confirmed that they do not plan to change that. To quote: Yandex represents about 2% of our total costs and is only one of dozens of sources we use. To put this in perspective: removing any single source would degrade search quality for all users while having minimal economic impact on any particular region. The world doesn’t need another politicized search engine. It needs one that works exceptionally well, regardless of the political climate. That’s what we’re building. That is unfortunate, as I found Kagi to be a good product with an interesting take on utilizing LLM models with search that is kind of useful, but I cannot in good heart continue to support it while they unapologetically finance a major company that has ties to the Russian government, the same country that is actively waging a war against Ukraine, an European country, for over 11 years, during which they’ve committed countless war crimes against civilians and military personnel. Kagi has the freedom to decide how they build the best search engine, and I have the freedom to use something else. Please send all your whataboutisms to /dev/null.
What happens when you hand an educational robot to a group of developers and ask them to build something fun? At Arduino, you get a multiplayer robot showdown that’s part battle, part programming lesson, and entirely Alvik. The idea for Alvik Fight Club first came to life during one of our internal Make Tanks, in […] The post Alvik Fight Club: A creative twist on coding, competition, and collaboration appeared first on Arduino Blog.
It was time to upgrade Hibernate on that one Java monolithic1 backend service that my team was responsible for. We took great precautions with these types of changes due to the scale of the system, splitting changes into as many small parts as possible and releasing them as often as possible. With bigger changes we opted for running a few instances of the new version in parallel to the existing one. Then came Hibernate 5.2. Hibernate 5.2 introduced a new warning log to indicate that the existing API for writing queries is deprecated. Hibernate's legacy org.hibernate.Criteria API is deprecated; use the JPA javax.persistence.criteria.CriteriaQuery instead Every time you used the Criteria API it would print the line. Just one little issue there. Can you see it? Every time you used the Criteria API it would print the line. In a poorly written Java backend service, one HTTP request can make multiple queries to the database. With hundreds of millions of HTTP requests, this can easily balloon to billions of additional logs a day. Well, that’s exactly what happened to our service, resulting in the CPU usage jumping up considerably and the latency of the service being negatively impacted. We didn’t have the foresight to compare every metric against every instance of the service, and when the metrics were summarized across all instances, this increase was not that noticeable while both new and existing instances of the service were running. Aside from the service itself, this had negative effects downstream as well. If you have a solution for collecting your service logs for analysis and retention, and it’s priced on the amount of logs that you print out, then this can end up being a very costly issue for you. We resolved the issue by making a configuration change to our logger that disabled these specific logs. This does make me wonder who else may have been impacted by this change over the years and what that impact might’ve looked like regarding the resource usage on a world-wide scale. I’m not blaming the Hibernate developers, they had good intentions, but the impact of an innocent change like that was likely not taken into account for large-scale services. Last I heard, the people behind Hibernate are a very small team, and yet their software powers much of the world, including critical infrastructure like the banking system. I’m well aware that we’re talking about Hibernate releases that were released around the time I was still a junior developer (2016-2018). Some call it technical debt, others call it over half a decade of neglect. unmaintaned monoliths suck, but so do unmaintained microservices. ↩︎
