tunesmith

I use it for lilypond for notation. I always considered it superior to Finale and Sibelius anyway. Not sure how it stacks up against the more modern commercial notation apps. But I love having a real trackable version history of my pieces.

What's also cool is the more advanced llms know lilypond and music theory too, so they can do things like... I don't know, check for counterpoint errors. I've used it with limited success to expand my jazz lead sheets into two-hand piano arrangements just for practice exercises.

I just had Sol Ultra read the proof and create a graph of it using Concludia (my side project) so you can explore it visually/graphically. I certainly don't understand it though so I have no idea if it's helpful. :)

https://concludia.org/graph/g_2ecb8083-52ec-3448-8c30-2f9bc7...

I think it doesn't prove much that it hasn't happened yet. Companies might just be moving slower than you think, and are still planning on doing it. And, in many corners, "don't manually write code" is being joined by "don't manually read code" as an attractive principle.

How do you mean OpenAI lets you use your own harness? I'm under the impression that a custom harness requires the OpenAI SDK, which requires api tokens rather than plus/pro accounts.

Wow, didn't know this existed until today. Thanks!

It's interesting to think about how difficult it is to define "fair" with the current system. If a state's population is 60/40 and there are ten districts, do you want them each to be representative at 60/40, and thus 10-0? Or do you want six districts one way and four the other way? In other words, there's a real tension between "competitive districts" and "representative outcomes".

I experience the same with OpenAI, on the $100/month plan. GPT-5.4 is something I still have to challenge: it can bullshit me with bad implementation and add a lot of cruft that costs more time later. GPT-5.5-xhigh is something I have almost complete faith and trust in, it's just smooth. And yet I know the actual token cost of that fully utilized is exorbitant, like as much as an entire salary for a senior developer.

So maybe our CEOs are responding with a lot of foresight and inside information and know that that level of quality is going to be cheap really soon. But barring that, they're going to experience either sticker shock or a slowdown.

I think the real endgame is probably more accurate "models of models" (model routers) that know exactly how to split prompts between expensive frontier and cheap/free local models.

I'm building a website that allows friends to write branching fiction novels together, and another website that allows people to argue and conclude together using first-principles thinking. I've abandoned a project that allows people to register percentage certainty of sports outcomes to improve their calibration - it wasn't fun enough for the users. A few other things besides. I recently wrote a gpl TUI to edit dags here: https://github.com/tunesmith/dagim - that one as something of an experiment since it's in a language I don't know, that one took 3-4 days of steady prompting.

I feel like I must have plateued and don't know what to do next to level up. I'm currently on the $100/month codex plan and it seems fine using 5.5-xhigh all the time. I think of what to do next, have a chat session to determine exactly what to ask for up to the point of being ready to implement, and then codex churns on a commit-sized task whereupon I briefly check it on my local dev server. If necessary I ask for a change. Then I ask it to commit and recommend the next step based off the spec. Oftentimes I have to "approve" an out-of-sandbox request anyway.

I haven't found anything that requires running all night. I could tell it to one-shot a big plan but given how often I realize I want an intermediary thing to be slightly different it seems like a waste of effort.

I'm guessing the next thing I should probably look into is some sort of machine vm I can tunnel my codex-gui requests to so I don't have to deal with the sandbox approvals (I don't want to give it "dangerous" access to my entire mac).

I don't understand what people are doing with their side projects that is leading them to churn through tokens so quickly, to the point of requiring two $200/month subscriptions and a bunch of token charges besides.

I went through the dotcom crisis and never heard of GFC until today. I've always seen it as the finsys crash.

compared to almond farms? You phrase that as if almond farms use a reasonable amount of water.

as opposed to what, though? You don't get a 3% discount for using cash.

It was the "honest" stuff that hit my radar, but maybe Claude is just ruining that word for "honest" usage.

That's not what OP was saying, they were saying they experienced multiple instances of LLM handling some tricky problem. But just because it can solve Tuesday's problem doesn't mean it can solve Thursday's.

And this:

the number of people in that role can be reduced in proportion to the amount of work automated.

Components of humans are not fungible. If one fifth of my job is easier but the other 4/5ths require my specialized human judgment, you can't remove one person out of five and pretend everything will be okay. That's what I mean by the two-step; you just did it yourself.

How do you determine that this will incorrectly lead to a reduction in the workforce?

This gets back into Theory of Constraints. Identify the constraint. Alleviate the constraint, not the symptom. If you're in a factory, and transmogrifiers are building so many widgets that your whatchamaflorpits starts falling behind, you don't scuttle 20% of your transmogrifiers, you buy more watchamaflorpits!

Instead people are like, "oh gosh, my developers are idle, guess we have to lay them off."

In practice, it's more like companies want to spend money on AI because they believe it will save money somewhere else. If instead they see extra cost, then they get all confused. They can't bring themselves to believe that in their particular case maybe the benefit isn't worth the cost; they're axiomatically conditioned to believe they have to keep using it, and so therefore they have to make cuts somewhere else. It's insane.

I went through this personally. I had a glut of project ideas I wanted to get through. I signed up for the $200/month thing. I caught up. My agent sat idle. It was hard to decide to cut my plan. I felt initial pressure to search and hunt for other ideas to code, ideas that were pretty stupid. I finally downscaled my plan; I got hold of myself. But that's easier to do for an individual than it is for a company.

In normal economic theory it's easier to understand. You're at a particular scale. You have the opportunity to automate, but does it make sense for you? I could go out and buy a riding mower right now, but my lawn is less than a quarter acre. The riding mower lets me scale up, but I don't have something that can benefit from it.

I think there's something else psychological going on. What you describe is a rational approach based off of bad values. But I think I'm also seeing something weirdly irrational.

It's like an (emotional) depression or something. Scarcity thinking, the inability to think expansively. People are so sure that everything around them is shrinking that they feel an instinct to hunker down, shrink, and cut as well. Like it doesn't occur to them that they don't have to feel that way. The execs I work with, none of them strike me as spreadsheet-driven greedy people. They seem more freaked out than that.

I've had multiple instances now where AI left to it's own devices has solved a tricky problem that I honestly didn't think it was capable of.

Who cares that you've had multiple instances? Everyone has had multiple instances. The question is whether that happens in EVERY instance. Because when someone's laid off, that's what the exec believes, that the person isn't needed at all.

I'm not arguing that AI won't replace jobs - it's clear that jobs are already disappearing "because of AI". I'm not even arguing that it is immoral (even though it is). I'm arguing that it is short-sighted and unwise.

Unfortunately it will take longer for our bosses to walk it back. I feel like I'm fighting the battle daily, telling execs what kind of work LLMs do not replace... it's very slippery, they keep on doing the rhetorical texas two-step - I don't think they even realize they're doing it. We communicate that LLM is amplifying, they hear it can replace. "No, we need humans to help with specs" "But AI can help with that." "But only help, they can't come up with the idea." "Sure they can, we can just ask them."

It's also amazing how hidden some of these realities were before. Like, you assign a ticket to a developer - in the past they just wanted to know the developer was working on it and didn't care so much which work was what. They'd probably be so surprised to find out that a large percentage of implementation was deriving exactly what was meant by the jira ticket or the specification or the product person's intent. Which is all the stuff you have to work on before you can type in a prompt to an LLM. But now there's this pressure to believe that the developers only do the implementation part that the LLMs do, so they can pretend there will be major efficiency improvements. And it's really hard to explain to them what it is that developers even do.

I know I'm not saying anything new here, but at least where I'm working all of these matters feel much more present than they did months ago.

There should be class action lawsuits just from widespread recognition of corporate wrongdoing.

I think there's a way to define it without it either side feeling personally attacked. One of the things I like about using agents for programming is that if the spec is detailed enough, I can implement it in a number of different languages and still get the thing I intended. That means that the "art" is in the spec, not the implementation.

I think the question with AI in music is when it gets to that point. What's the musical spec? What's the implementation? If the spec is supposed to be the pure distillation of my intent, then shouldn't that mean each time I engage AI to "implement" the spec, the musical output of the AI should be the same?

At that point I'm all in favor of using AI for music. But when AI is used to replace a specific intent with vague intent, that's where I feel like something is lost in the human experience of human-created music.

I recently tried to launch a site for friends and family that allowed people to make confidence predictions on various outcomes so they could track their calibration over time. It was like "I'm 84% certain Kansas City will beat Buffalo." I had a lot of fun with it since I'm a nerd about this stuff, and I actually demonstrably improved my calibration. But the only sources I could find for rapid repeatable bets were sports predictions. And I definitely did not want to include money or betting for all the annoying legal reasons. People had fun using it once for March Madness 2025 but traffic really dwindled after that. My conclusion was that the overall subject just wasn't inherently fun enough to do it without money involved, so I made the site dormant.

Getting better calibrated really is worthwhile, I just wish there was more of an appetite to do that without involving money.

Happens to songwriters too, sometimes it goes somewhere. I had a dream once, on the morning of a July 4th, that for some reason took a really common cliche jazz/blues riff and slowed it way down while I was dreaming of a bunch of Americana images. It became this song, the riff is at the beginning: https://music.apple.com/us/album/so-beautiful/899061469?i=89...

I wish it would happen more often, that's only happened for one other song of mine. Most of the rest are a lot of gritted teeth and frustration.

How so?

I think as it becomes more common for executives to think we can replace software engineering with agents, I wonder if they might be basing their decisions off of unrealistic perceptions of the average software engineer. I guess I'm mulling two somewhat contradictory senses:

1. You get out of it what you put into it. A savvy CTO might be incredibly excited by everything they can do with agents, and improperly think that all the software engineers can do the same thing, when in reality your org's average software engineers might not have the creativity to even think of many cases where it could save them work. So by mandating agent usage, you might find that productivity hasn't improved while AI costs have increased.

2. When using AI, there are two gaps that become more obvious. First is the gap of: who tells the agent what to do? In many orgs, product isn't technically savvy enough to come up with a detailed spec/plan that LLM can use. And many cog-in-machine developers aren't positioned to come up with the spec, they just want to implement it. By expecting work to be implemented by agent-using developers, you might instead find a lot of idle workers waiting for work to show up. Second is the qa/review cycle. You've introduced a big change to the org but are you really saving cost or shifting it?

I'm all for introducing LLM as optional to help existing developers increase velocity and quality, but I think the "let's restructure the org" movement is really dicey, especially for mid-size or smaller employers.

I'm working on https://concludia.org, a site that helps groups of people collaborate on arguments and conclusions. I don't really have any revenue plans for it currently as I suspect it will be rather niche -- I certainly wouldn't mind if it tops out as a small community of users -- but I've found it super useful in various contexts at work and at home.

You can read more about it over at the site, but it allows you to construct and validate arguments in a graphical form, and it has truth/proof propagation so you can see whether a conclusion is currently considered valid or contested. You can create counterpoints where you think the argument breaks down, and strengthen arguments from there. Some upcoming plans are to allow users to validate arguments for themselves, like mark which parts they understand and agree with so they can collapse that part of the graph, and to add more mcp capability so that LLM can help you construct and validate new arguments.

It feels like an appreciation for hypotheticals or givens is missing here. One can simultaneously be against the war and the bombing in general, and also accept it as a given and then think about a certain situation being understandable within that given.

Really fascinating article. Bits of bias here and there, like "The US military has been trying to close the gap between seeing something and destroying it for as long as that gap has existed" -- you can respond to seeing and understanding something without destroying it -- but it underscores, to me at least, how much denser the "fog of war" has become. The fog of media reporting in general. Those first few paragraphs felt like a breath of fresh air.

What's your therefore??

haha. Nervous to. :)

The article says it's from first principles and looks pretty deep, and I didn't have the time to exhaustively read it myself, so I used my side project concludia to try and analyze and graph the argument... you can see the argument graph here if you're interested in that kind of thing.

https://concludia.org/step/6f3cbaa2-65d4-3c44-8c1f-23f6ddf2b...

Reading the thread below, I'm always curious where in the argument the various counterpoints would attach. Like if a counterpoint is fatal or just an offshoot. I didn't have the system try and semantically analyze it for flaws/counterpoints yet, I just tried to get it to depict the article's reasoning. Not sure yet how good a job it did.