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  1. This is a post about LLM coding agents
    Feb 13, 2026 · original
    When I was in my classics MA program, I took a course on Latin prose composition. A thing to know about classics degrees is you spend a lot of your time in Latin and ancient Greek courses. You learn history and philosophy and so forth, but mostly via reading those works in their original languages. Another thing to know about classics degrees is that ancient language pedagogy is not like modern language pedagogy. In a modern language class, hopefully 1 , you split your time among listening, speaking, reading, and writing exercises. These are all different activities that require overlapping but distinct skills and exercise your brain in different ways, and you need all of them to operate comfortably in a place where that language predominates. In an ancient language class, though, you’re unlikely 2 to be spending much time in such a place, so your time is focused almost entirely on readi
  2. AI in the Library, round two (vs ChatGPT and the zeitgeist)
    Jan 10, 2026 · original
    Once upon a time, I taught a course on AI in the Library in an iSchool. (Check out the reading list and syllabus , which are relevant context for this post.) I haven’t taught it for a while — I got busy with other things, and then ChatGPT dropped and immediately rendered my syllabus obsolete and I wasn’t up for overhauling it. At least, that was why I wasn’t teaching it in 2022-2024. In 2025 and 2026, there are some pretty different reasons not to be adjunct faculty, particularly in a public university, if you don’t have to be. Like an enormous Project-2025-based, federally directed but decentralized system to dictate syllabi, destroy academic freedom, and fire professors who don’t toe the line. (Examples unfortunately abound, such as University of Oklahoma , Texas A&M , San José State University — the last of these being the university where I taught.) And in the midst of all this, here
  3. In which we ask Copilot to do the team a solid
    Jul 30, 2025 · original
    So, I wrote an alien artifact no one else on my team understood. (I know, I know.) I’m not a monster — it has documentation and tests, it went through code review for all that that didn’t accomplish its usual knowledge-transfer goals — and there were solid business reasons the alien artifact had to exist and solid skillset reasons I had to write it, and yet. There we were. With an absolutely critical microservice that no one understood except me. One day someone reported a bug and my creative and brilliant coworker Ivanok Tavarez was like, you know, I’m pretty sure I know where this bug is in the code. I have no idea what’s going on there, but I asked Copilot to fix it. Also I have no idea what Copilot did. But it seems to have fixed it. Knowing that I’m rather more of an AI skeptic than he is, he asked, would I entertain this code? And you know what? Let’s do it. I mean obviously we’ve
  4. how I think about classes
    Jul 8, 2025 · original
    Someone in a Slack I’m in asked for advice on how to understand classes in programming, and I wrote a mini-novel about how I understand them, so I figured it might as well be a blog post! Here we go — A class is a noun. That is to say: it is a Specific Thing which I can name. And I make a class when I have a noun that needs to know things about itself . What do I mean by “needs to know things about itself”? I mean that this Specific Thing has associated behavior and/or data. For instance, if I’m writing a circulation system for a library, an important noun might be a Checkout. Checkouts have data they need to know about themselves: for instance, what time was the object checked out, what object is it, when is it due back, what user ID has it. Every Checkout needs to know this kind of data about itself, but the specific values of the data will differ for each Checkout. Similarly, Checkout
  5. shaving yaks with gensim 3.8.3
    Mar 1, 2023 · original
    I’m updating some old code which includes a model trained under gensim 3.8.3. (Or so I hope, based on the poetry.lock file.) Current stable is 4.3.0, so…I have some updating to do. In theory I can load a 3.8.x model in 4.0.x, save it, then open that in 4.1.x, et cetera. I’d rather do that than retraining the model (which would be a festival of limited documentation, missing institutional knowledge, and additional yaks), so here I am installing gensim 3.8.3 on my 2021 M1 Mac. What could go wrong, right? First yak: Cython gensim depends on numpy, numpy depends on Cython, and Cython 0.29.14 (the version in my poetry.lock) is all, AttributeError: module 'collections' has no attribute 'Iterable' However, it turned out my version of numpy wanted a higher version of Cython: RuntimeError: Building NumPy requires Cython = 0.29.30, found 0.29.14 at [local directory structure]/lib/python3.8/site-pa
  6. Tech screens, how do they work
    May 16, 2022 · original
    I just had So Many job interviews, which means I had so many tech screens, all of which ran differently. I didn’t know what to expect going into most of them — I’ve only interviewed in the library world in the past, where many organizations don’t have tech screens at all, but some of these were industry jobs — and organizations varied in how much they explained what to expect, so I’m going to outline the varieties of tech screen I faced in hopes this will help you. All organization names are, obviously, anonymized. The Order of the Promethean Banner A screen (preceding any human interviews) via Triplebyte, which presented short blocks of code and asked multiple-choice questions about their outputs, flaws, et cetera. Questions were timed. Googling, documentation, REPLs, et cetera were disallowed. I am pretty sure I went down in flames on this one. While I got to choose a language I am fam
  7. “Just a few files”: technical labor, academe, and care
    Dec 3, 2021 · original
    I read this article 1 and was moved to furious tweetstorm and several respondents asked that I make it into a blog post. This is that post. Let’s dissect this quote, “They tired of care-taking, even though this involved little more than continuing to host the project files on a server.” Firstly, it did not involve just continuing to host the files on a server. That server needs to get upgrades to stay secure and to be a solid foundation for future software development. Those upgrades may in turn mean the files must be upgraded to remain usable. Those server upgrades are themselves labor. Ensuring that the web server configuration and file permissions remain in a state where your files can be served is labor. 2 If there are enough people who want to host “just a few files”, it’s a lot of labor. And server costs add up. The PHP or ruby or whatever programming language your dynamic files ru
  8. the great thing about deferred maintenance is everything catches fire at the same time
    Sep 24, 2021 · original
    Once upon a time in 2017, my colleague Andy Dorner, who is awesome at devops, made a magical deploy script for HAMLET . I was like, ugh, Heroku is not a good solution, I have to AWS, I regret my life choices, and he was like, never fear! I will throw together a script which will make it all test/build/deploy every time you git push, using magic. It worked great and I basically didn’t have to think about it until July of this year. And then, inevitably in retrospect, I found a deploy didn’t work because nothing worked. The presenting issue was that a URL we’d used to download a thing for certbot now 404ed. But why stop there? Travis was no longer a thing, so there goes deployment. The Amazon Linux platform had gone comprehensively obsolete, replaced by AL2 (which has a subtly but importantly different set of instructions for…everything, and they usually aren’t clearly distinguished in the
  9. I haven’t failed, I’ve tried an ML approach that *might* work!
    May 23, 2021 · original
    When last we met I was turning a perfectly innocent neural net into a terribly ineffective one, in an attempt to get it to be better at face recognition in archival photos. I was also (what cultural heritage technology experience would be complete without this?) being foiled by metadata. So, uh, I stopped using metadata. With twinges of guilt. And full knowledge that I was tossing out a practically difficult but conceptually straightforward supervised learning problem for…what? Well. I realized that the work that initially inspired me to try my hand at face recognition in archival photos was not, in fact, a recognition problem but a similarity problem: could the Charles Teenie Harris collection find multiple instances of the same person? This doesn’t require me to identify people, per se; it just requires me to know if they are the same or different. And you know what? I can do a pretty
  10. I haven’t failed, I’ve just tried a lot of ML approaches that don’t work
    Apr 16, 2021 · original
    “Let’s blog every Friday,” I thought. “It’ll be great. People can see what I’m doing with ML, and it will be a useful practice for me!” And then I went through weeks on end of feeling like I had nothing to report because I was trying approach after approach to this one problem that simply didn’t work, hence not blogging. And finally realized: oh, the process is the thing to talk about… Hi. I’m Andromeda! I am trying to make a neural net better at recognizing people in archival photos. After running a series of experiments — enough for me to have written 3,804 words of notes — I now have a neural net that is ten times worse at its task. And now I have 3,804 words of notes to turn into a blog post (a situation which gets harder every week). So let me catch you up on the outline of the problem: Download a whole bunch of archival photos and their metadata (thanks, DPLA !) Use a face detectio

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