>Even if its not "artisticallly worthwhile", the process is rewarding to the participant at the very least
I think that's the point though. What op did was rewarding to themselves, and I found it more enjoyable than a lot of music I've heard that was made by humans. So don't be a gatekeeper on enjoyment.
How am I a gatekeeper? I provided my own opinions; you are free to enjoy what you want or disagree with me. If you want to get into an objective discussion of why you find it enjoyable more than human works or what is art, we can do that but I do not like the personal slights.
I’m genuinely curious how you feel about LLMs being trained on pirated material. Not being snarky here.
Your comment reflects the old “information wants to be free” ideals that used to dominate places like HN, Slashdot, and Reddit. But since LLMs arrived, a lot of the loudest voices here argue the opposite position when it comes to training data.
I’ve been trying to understand whether people have actually changed their views, or whether it’s mostly a shift in who is speaking up now.
Personally, my opinion doesnt matter. I'm a nobody who doesnt work in AI fields.
But as a pirate, I specialize in finding hidden, hard to find, or otherwise lost sources. They're not making anybody any money, and I absolutely do not sell anything thats not mine (freely given).
But having every commercial work available for ingestion into an LLM is an amazing way to train an AI. However if you're going to use piracy at scale to train, you should also not be able to sell the LLM or access to it.
And yeah, that wrecks every corporate LLM strategy. Boo fucking hoo.
Do creators need paid for content they create? Ideally, yes! Do they deserve iron-fisted control of your hardware (DRM) to enact their demands? Fuck no!
Ideally, the LLMs would be FLOSS, full weights published, lists of content used to reproduce, etc. We could prune bad content and add more good. But the problem again is whoever does this must violate copyright cause copyright in the way its implemented is terrible.
In reality, I like the RIAA's congressional solution. You send a check for how many plays you did to BMI/ASCAP and you're good. That could be extended to books and shows. If that were done, you could have a New-Flix service that literally has every show and movie in existence. You just pay a reasonable cost per month to access the whole of video humanity.
why would that change anything? copyright is still a tax on the whole of society for the benefit of rich people and corporations. it opposes innovation, evolution and progress
maybe a short copyright would be fine (10 year fixed?) but copyright as-is seems indefensible to me
> copyright is still a tax on the whole of society for the benefit of rich people and corporations. it opposes innovation, evolution and progress
The original reason for copyright, patents, and trademarks made sense.
We want people to create and share. And unlike the old guild solutions from Europe, copyright and patents were a tradeoff to encourage the arts and science.
But what's a good tradeoff? Thats a big copyright question. 17 years? 34 years? Life of author? 75 years? How about individual non-commercial use? Or abandoned works?
And patents aren't even in scope, but we see similar abuses against the raison d'etra of them. Patents were supposed to entail a full reproduction of invention. Now, its a game of how incomplete can we make the filing while still getting protection. Or worse yet, really dumb shit has been patented like 1 click or the XOR patent, or that asshole Chakrabarty who patented living organisms.
There were good reasons for a fair copyright and patent law for furtherance of the art and sciences. That narrative was lost long ago. Now, only the violators can really push ahead. And they can't talk about it.
(Trademark law has never really had much complaints, aside trademarking a color. If you buy from XYZ company, you want to buy from them, not a counterfeit. And it relates back to coats of arms, again, representing a family or a charge.)
We recognize slop because it's slop. Just because a bunch of people are submitting slop to open source projects doesn't mean that AI can only generate slop.
His argument is basically a tautology "People who don't know how to code write bad code. Therefore, tools that help people who don't know how to code produce bad code"
I would love to see the US drone industry thrive, it's a major gap in both the consumer and military market.
At the same time, several businesses have and are trying to compete in this business. The amount of capital required is enormous if anyone is going to compete with DJI and the like. I personally know someone in this situation. They have a great product and some traction, but going from low quantity bespoke solutions to cost competitive large scale manufacturing costs hundreds of millions.
And the problem is, investors don't trust that the ban is going to last forever. The government could reverse the ban at any time, and that puts the US company back in a position where they can't compete with DJI, so the investors lose money. And they know that.
"And with no American-made drones comparable to the category leaders, it’ll be a while before any company steps up to offer one."
The problem is that it would be extremely risky for a US company to spin up a comparable US built drone. Even if they can match the price/quality point, at any given time the government could remove the ban, killing the entire business model.
Have you considered that maybe you aren't using it well? It's something that can and should be learned. It's a tool, and you can't expect to get the most out of a tool without really learning how to use it.
I've had this conversation with a few people so far, and I've offered to personally walk through a project of their choosing with them. Everyone who has done this has changed their perspective. You may not be convinced it will change the world, but if you approach it with an open mind and take the time to learn how to best use it, I'm 100% sure you will see that it has so much potential.
There are tons of youtube videos and online tutorials if you really want to learn.
> Have you considered that maybe you aren't using it well?
Here we go, as I said, and again and again and again it's always out fault we're not using well. It is impossible to counter argument. Btw to reply to your question, yes many times and proved to be useful in very small specialized tasks and a couple of migrations. I really like how LLMs are helping me in my day to day, but still so far away from all this astroturfing
Hi, Author here! I wrote this piece after some conversations with friends and realizing how we all had some levels of cognitive dissonance towards AI, IP, etc. I noticed I was moving my own goalposts both when criticizing AI or defending it.
I have to admit that I'm less of a skeptic than most, but there are some coherent skeptical arguments. I'm especially interested in what people think about their own skepticism on the technical side, as there seems to be a pivot towards the social lately.
Trying to one-shot large codebases is a exercise in futility. You need to let Claude figure out and document the architecture first, then setup agents for each major part of the project. Doing this keeps the context clean for the main agent, since it doesn't have to go read the code each time. So one agent can fill it's entire context understanding part of the code and then the main agent asks it how to do something and gets a shorter response.
It takes more work than one-shot, but not a lot, and it pays dividends.
Is there a guide for doing that successfully somewhere? I would love to play with this on a large codebase. I would also love to not reinvent the wheel on getting Claude working effectively on a large code base. I don’t even know where to start with, e.g., setting up agents for each part.
This isn't meant as a criticism, or to doubt your experience, but I've talked to a few people who had experiences like this. But, I helped them get Claude code setup, analyze the codebase and document the architecture into markdown (edit as needed after), create an agent for the architecture, and prompt it in an incremental way. Maybe 15-30 minutes of prep. Everyone I helped with this responded with things like "This is amazing", "Wow!", etc.
For some things you can fire up Claude and have it generate great code from scratch. But for bigger code bases and more complex architecture, you need to break it down ahead of time so it can just read about the architecture rather than analyze it every time.
Is there any good documentation out there about how to perform this wizardry? I always assumed if you did /init in a new code base, that Claude would set itself up to maximize its own understanding of the code. If there are extra steps that need to be done, why don't Claude's developers just add those extra steps to /init?
Not that I have seen, which is probably a big part of the disconnect. Mostly it's tribal knowledge. I learned through experimentation, but I've seen tips here and there. Here's my workflow (roughly)
> Create a CLAUDE.md for a c++ application that uses libraries x/y/z
[Then I edit it, adding general information about the architecture]
> Analyze the library in the xxx directory, and produce a xxx_architecture.md describing the major components and design
> /agent [let claude make the agent, but when it asks what you want it to do, explain that you want it to specialize in subsystem xxx, and refer to xxx_architecture.md
Then repeat until you have the major components covered. Then:
> Using the files named with architecture.md analyze the entire system and update CLAUDE.md to use refer to them and use the specialized agents.
Now, when you need to do something, put it in planning mode and say something like:
> There's a bug in the xxx part of the application, where when I do yyy, it does zzz, but it should do aaa. Analyze the problem and come up with a plan to fix it, and automated tests you can perform if possible.
Then, iterate on the plan with it if you need to, or just approve it.
One of the most important things you can do when dealing with something complex is let it come up with a test case so it can fix or implement something and then iterate until it's done. I had an image processing problem and I gave it some sample data, then it iterated (looking at the output image) until it fixed it. It spent at least an hour, but I didn't have to touch it while it worked.
I've taken time today to do this. With some of your suggestions, I am seeing an improvement in it's ability to do some of the grunt work I mentioned. It just saved me an hour refactoring a large protocol implementation into a few files and extracted some common utilities. I can recognise and appreciate how useful that is for me and for most other devs.
At the same time, I think there's limitations to these tools and that I wont ever be able to achieve what I see others saying about 95% of code being AI written or leaving the AI to iterate for an hour. There's just too many weird little pitfalls in our work that the AI just cannot seem to avoid.
It's understandable, I've fallen victim to a few of them too, but I have the benefit of the ability to continuously learn/develop/extrapolate in a way that the LLM cannot. And with how little documentation exists for some of these things (MASQUE proxying for example) anytime the LLM encounters this code it throws a fit, and is unable to contribute meaningfully.
So thanks for your suggestions, it has made Claude better and clearly I was dragging my feet a little. At the very least, it's freed up a some more of my time to work on the complex things Claude can't do.
To be perfectly honest, I've never used a single /command besides /init. That probably means I'm using 1% of the software's capabilities. In frankness, the whole menu of /-commands is intimidating and I don't know where to start.
You don't need to do much, the /agent command is the most useful, and it walks you through it. The main thing though is to give the agent something to work with before you create it. That's why I go through the steps of letting Claude analyze different components and document the design/architecture.
The major benefit of agents is that it keeps context clean for the main job. So the agent might have a huge context working through some specific code, but the main process can do something to the effect of "Hey UI library agent, where do I need to put code to change the color of widget xyz", then the agent does all the thinking and can reply with "that's in file 123.js, line 200". The cleaner you keep the main context, the better it works.
/commands are like macros or mayyybe aliases. You just put in the commands you see yourself repeating often, like "commit the unstaged files in distinct commits, use xxx style for the commit messages..." - then you can iterate on it if you see any gaps or confusion, even give example commands to use in the different steps.
Skills on the other hand are commands ON STEROIDS. They can be packaged with actual scripts and executables, the PEP723 Python style + uv is super useful.
I have one skill for example that uses Python+Treesitter to check the unit thest quality of a Go project. It does some AST magic to check the code for repetition, stupid things like sleeps and relative timestamps etc. A /command _can_ do it, but it's not as efficient, the scripts for the skill are specifically designed for LLM use and output the result in a hyper-compact form a human could never be arsed to read.
> In frankness, the whole menu of /-commands is intimidating and I don't know where to start.
claude-code has a built in plugin that it can use to fetch its own docs! You don't have to ever touch anything yourself, it can add the features to itself, by itself.
This is some great advice. What I would add is to avoid the internal plan mode and just build your own. Built in one creates md files outside the project, gives the files random names and its hard to reference in the future.
It's also hard to steer the plan mode or have it remember some behavior that you want to enforce. It's much better to create a custom command with custom instructions that acts as the plan mode.
My system works like this:
/implement command acts as an orchestrator & plan mode, and it is instructed to launch predefined set of agents based on the problem and have them utilize specific skills. Every time /implement command is initiated, it has to create markdown file inside my own project, and then each subagent is also instructed to update the file when it finished working.
This way, orchestrator can spot that agent misbehaved, and reviewer agent can see what developer agent tried to do and why it was wrong.
> if you did /init in a new code base, that Claude would set itself up to maximize its own understanding of the code.
This is definitely not the case, and the reason anthropic doesnt make claude do this is because its quality degrades massively as you use up its context. So the solution is to let users manage the context themselves in order to minimize the amount that is "wasted" on prep work. Context windows have been increasing quite a bit so I suspect that by 2030 this will no longer be an issue for any but the largest codebases, but for now you need to be strategic.
I think everything you said was true 1-2 years ago. But the current LLMs are very good about citing work, and hallucinations are exceedingly rare. Gemini for example frequently directs you to a website or video that backs up it's answer.
I think that's the point though. What op did was rewarding to themselves, and I found it more enjoyable than a lot of music I've heard that was made by humans. So don't be a gatekeeper on enjoyment.
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