As we move from tailors to big box stores I think we have to get used to getting what we get, rather than feeling we can nitpick every single detail.

I'd also be more interested in how his 3rd, 4th or 5th vibe coded app goes.

There is something to be said about giving AIs a clean foundation on which to build their own language. This allows evolution of such systems to go all the way into the compiler, beyond tooling.

I understand the article writers frustration. He liked a thing about a product he uses and they changed the product. He is feeling angry and he is expressing that anger and others are sharing in that.

And I'm part of another group of people. I would notice the files being searched without too much interest. Since I pay a monthly rate, I don't care about optimizing tokens. I only care about the quality of the final output.

I think the larger issue is that programmers are feeling like we are losing control. At first we're like, I'll let it auto-complete but no more. Then it was, I'll let it scaffold a project but not more. Each step we are ceding ground. It is strange to watch someone finally break on "They removed the names of the files the agent was operating on". Of all of the lost points of control this one seems so trivial. But every camels back has a breaking point and we can't judge the straw that does it.

I see pseudo-scientific claims from both sides of this debate but this is a bit too far for me personally. "We all know" sounds like Eternal September [1] kind of reasoning. I've been in the industry about as long as the article author and I think he might be looking with rose-tinted glasses on the past. Every aging generation looks down at the new cohort as if they didn't go through the same growing pains.

But in defense of this polemic, and laying out my cards as an AI maximalist and massive proponent of AI coding, I've been wondering the same. I see articles all the time about people writing this and that software using these new tools and it so often is the case they never actually share what they built. I mean, I can understand if someone is heads-down cranking out amazing software using 10 Claude Code instances and raking in that cash. But not even to see one open source project that embraces this and demonstrates it is a bit suspicious.

I mean, where is: "I rewrote Redis from scratch using Claude Code and here is the repo"?

1. https://en.wikipedia.org/wiki/Eternal_September

There is a premise hidden in those speculations that there is some strong connection between the structure of the universe itself and the structures humans find pleasing when listening to music. And I detect a suggestion that studying the output of our most genius musicians might reveal some kind of hidden information about the universe, specifically related to some kind of "spirituality".

This was a sentiment shared, in some sense, by the deists of the enlightenment. They rejected the scriptures and instead believed that studying the physical universe might reveal the "mind of God".

If we are looking for correspondences between these things - why limit ourselves to Euclidean geometry? Modern physics leans on Riemannian geometry, symmetry, and topology. It appears the topology of the universe, under a wide array of experiments, is way more complicated than the old geometric ideas. Most physicists talk about Lie Groups, fiber bundles, etc.

If you take "as above, so below" seriously and you want to find connections between cosmology and music, I believe you have to use modern mathematical tools. I think we need to expand beyond geometry and embrace topology. Can we think of the chromatic scale tones as a Group? What operators would we need? etc.

It's interesting to try to get into the head of a guy like Coltrane and his mathematical approach, but perhaps we could be pushing new boundaries based on new understanding.

> ai-modified Indicates AI was used to assist with or modify content primarily created by humans. The source material was not AI-generated. Examples include AI-based grammar checking, style suggestions, or generating highlights or summaries of human-written text.

I do this in a task_descrtiption.md file and I include the clarifications in its own section (the files follow a task.template.md format).

I call this problem the "goldilocks" problem. The task has to be large enough that it outweighs the time necessary to write out a sufficiently detailed specification AND to review and fix the output. It has to be small enough that Claude doesn't get overwhelmed.

The issue with this is, writing a "sufficiently detailed specification" is task dependent. Sometimes a single sentence is enough, other times a paragraph or two, sometimes a couple of pages is necessary. And the "review and fix" phase again is totally dependent and completely unknown. I can usually estimate the spec time but the review and fix phase is a dice roll dependent on the output of the agent.

And the "overwhelming" metric is again not clear. Sometimes Claude Code can crush significant tasks in one shot. Other times it can get stuck or lost. I haven't fully developed an intuition for this yet, how to differentiate these.

What I can say, this is an entirely new skill. It isn't like architecting large systems for human development. It isn't like programming. It is its own thing.

I would guess that many (if not most) of the people attempting to contribute AI generated code are legitimately trying to help.

People who are genuinely trying to be helpful can often become deeply offended if you reject their help, especially if you admonish them. They will feel like the reprimand is unwarranted, considering the public shaming to be an injury to their reputation and pride. This is most especially the case when they feel they have followed the rules.

For this reason, if one is to accept help, the rules must be clearly laid out from the beginning. If the ghostty team wants to call out "slop", then it must make it clear that contributing "slop" may result in a reprimand. Then the bothersome want-to-be helpful contributors cannot claim injury.

This appears to me to be good governance.

I read these articles and I feel like I am taking crazy pills sometimes. The person, enticed by the hype, makes a transparently half-hearted effort for just long enough to confirm their blatantly obvious bias. They then act like the now have ultimate authority on the subject to proclaim their pre-conceived notions were definitely true beyond any doubt.

Not all problems yield well to LLM coding agents. Not all people will be able or willing to use them effectively.

But I guess "I gave it a try and it is not for me" is a much less interesting article compared to "I gave it a try and I have proved it is as terrible as you fear".

For collaboration I believe that _lineage_ is important. Not just a one-shot output artifact but a series of outputs connected in some kind of connected graph. It is the difference between a single intervention/change vs. a _process_. This provides a record which can act as an audit trail. In this "lineage" as I would call it, there are conversations with LLMs (prompts + context) and there are outputs.

Let's imagine the original topic, audio, with the understanding that the abstract idea could apply to anything (including mental health). I have a conversation with an LLM about some melodic ideas and the output is a score. I take the score and add it as context to a new conversation with an LLM and the output is a demo. I take the demo and the score then add it to a new conversation with an LLM and the output is a rhythm section. etc.

What we are describing here is an evolving _process_ of collaboration. We change our view from "I did this one thing, here is the result" to "I am _doing_ this set of things over time".

The output of that "doing" is literally a graph. You have multiple inputs to each node (conversation/context) which can be traced back to initial "seed" elements.

From a collaborative perspective, each node in this graph is somewhat independent. One person can create the score. Another person can take the score and create a demo. etc.

It created a 10 page story that stuck to the topic and was overall coherent. The main character changed color and style on every page, so no consistency there. The overall page layouts and animation style were reasonably consistent.

The metaphor it used was the character climbing a mountain and encountering other characters that represented each mode. Each supporting character was reasonably unique, although note motif was present on 3 or 4. The mountain also changed significantly and the character was frequently back at the bottom. However, in the end, he does reach the summit.

I can't say I am overly impressed but it does mostly do what they claim.

I hope when I state it that way you start to realize the error in your thinking process. You don't send trivial tasks to the GPU because the overhead is too high.

You have to experiment and gain experience with agent coding. Just imagine that there are tasks where the overhead of explaining what to do and reviewing the output are dwarfed by the actual implementation. You have to calibrate yourself so you can recognize those tasks and offload them to the agent.

I believe there is massive room for improvement over what is currently available.

However, my larger point isn't "I want to do this one particular thing" and rather: I wish the music model companies would divert some attention away from "prompt a complete song in one shot" and towards "provide tools to iteratively improve songs in collaboration with a musician/producer".

What argument is required for excitement? Excitement is a feeling not a rational act. It comes from optimism and imagination. There is no argument for optimism. There is often little reason in imagination.

> How to even find the value in living given all of that?

You might have heard of the Bhagavad Gita, a 2000+ year old spiritual text. It details a conversation between a warrior prince and a manifestation of God. The warrior prince is facing a very difficult battle and he is having doubts justifying any action in the face of the decisions he has to make. He is begging this manifestation of God to give him good reasons to act, good reasons not just to throw his weapons down, give away all his possessions and sit in a cave somewhere.

There are no definite answers in the text, just meditations on the question. Why should we act when the result is ultimately pointless, we will all die, people will forget you, situations will be resolved with or without you, etc.

This isn't some new question that LLMs are forcing us to confront. LLMs are just providing us a new reason to ask the same age-old questions we have been facing for as long as writing has existed.

I was having a conversation with a former bandmate. He was talking about a bunch of songs he is working on. He can play guitar, a bit of bass and can sing. That leaves drums. He wants a model where he can upload a demo and it either returns a stem for a drum track or just combines his demo with some drums.

Right now these models are more like slot machines than tools. If you have the money and the time/patience, perhaps you can do something with it. But I am looking forward to when we start getting collaborative, interactive and iterative models.

As for the "I thought about this problem for 10s let me tell you all the things wrong with it" - yeah. Engineers do that. I'm constantly pointing it out in relation to LLM coding agents.

Lately I've been stuck in YouTube court cases recommendations. There are live trials and many archives for all sorts of real court cases at every stage. I have grown an appreciation for what a judge does. A judge listens and makes sure all information has been provided before making a judgement/ruling/order. The patience those folks show is significant. I can only imagine how tiring that amount of active listening must be. I have found it personally inspiring and educational.