I had something kinda similar happen to my hotmail account. While I didn't lose access to it, I lost more than a decade of correspondence dating back to my teenage years. The reason was that Microsoft at some point required you to "login" once every 30 days. It seems they only counted logins through their web interface or something like that, so even though I was receiving emails daily, I didn't trigger a "login" in their system. They then deleted all my emails, but I could still login.
This happened to me ten years ago. A while later they did the same thing with my Minecraft login that I had purchased before the EULA was in place; I’ve avoided their services like the plague since then.
I find that surprising. GPT 5.2 is the model I've had working the longest. It frequently works more than 4 hours nonstop, while earlier models would stop to ask if they should continue every 10 minutes. 5.1 and earlier ignores it if I ask it to continue until a task is done, but 5.2 will usually finish it.
That would be considered a derivative work of the C code, therefore copyright protected, I believe.
Can you replay all of your prompts exactly the way you wrote them and get the same behaviour out of the LLM generated code? In that case, the situation might be similar. If you're prodding an LLM to give you a variety of resu
But significantly editing LLM generated code _should_ make it your copyright again, I believe. Hard to say when this hasn't really been tested in the courts yet, to my knowledge.
The most interesting question, to me, is who cares? If we reach a point where highly valuable software is largely vibe coded, what do I get out of a lack of copyright protection? I could likely write down the behaviour of the system and generate a fairly similar one. And how would I even be able to tell, without insider knowledge, what percentage of a code base is generated?
There are some interesting abuses of copyright law that would become more vulnerable. I was once involved in a case where the court decided that hiding a website's "disable your ad blocker or leave" popup was actually a case of "circumventing effective copyright protection". In this day and age, they might have had to produce proof that it was, indeed, copyright protected.
"Can you replay all of your prompts exactly the way you wrote them and get the same behaviour out of the LLM generated code? In that case, the situation might be similar. If that's not the case, probably not." Yes and no. It's possible in theory, but in practice it requires control over the seed, which you typically don't have in the AI coding tools. At least if you're using local models, you can control the seed and have it be deterministic.
That said, you don't necessarily always have 100% deterministic build when compiling code either.
That would be interesting. I don't believe getting 100% the same bytes every time a derivative work is created in the same way is legally relevant. Take filters applied to copyright protected photos - might not be the exact same bytes every time you run it, but it looks the same, it's clearly a derivative work.
So in my understanding (not as a lawyer, but someone who's had to deal with legal issues around software a lot), if you _save_ all the inputs that will lead to the LLM creating pretty much the same system with the same behaviour, you could probably argue that it's a derivative work of your input (which is creative work done by a human), and therefore copyright protected.
If you don't keep your input, it's harder to argue because you can't prove your authorship.
It probably comes down to the details. Is your prompt "make me some kind of blog", that's probably too trivial and unspecific to benefit from copyright protection. If you specify requirements to the degree where they resemble code in natural language (minus boilerplate), different story, I think.
(I meant to include more concrete logic in my post above, but it appears I'm not too good with the edit function, I garbled it :P)
Copyright'd in, copyright out. Your compiled code is subject to your copyright.
You need "significant" changes to PD to make it yours again. Because LLMs are predicated on massive public data use, they require the output to PD. Otherwise you'd be violating the copyright of the learning data - hundreds of thousands of individuals.
No, and your comment is ridiculously bad faith. Courts ruled that outputs of LLMs are not copyrightable. They did not rule that outputs of compilers are not copyrightable.
I think that lawsuit was BS because it went on the assumption that the LLM was acting 100% autonomously with zero human input, which is not how the vast majority of them work. Same for compilers... a human has to give it instructions on what to generate, and I think that should be considered a derivative work that is copyrightable.
I would say all art is derivative, basically a sum of our influences, whether human or machine. And it's complicated, but derivative works can be copyrighted, at least in part, without inherently violating any laws related to the original work, depending on how much has changed/how obvious it is, and depending on each individual judge's subjective opinion.
The sensible options were that either LLM outputs are derivative of all their training data, or they're new works produced by the machine, which is not a human, and therefore not copyrightable.
Courts have decided they're new works which are not copyrightable.
I've found that SDD is actually what you need to be able to work with code bases when they go above around 100 000 lines of code. It's what unlocked getting LLMs to work well with large codebases for me.
I still don't get it, can you clarify? It's not the research phase that I'm disputing. Clearly for a large codebase, you need some good way to take all that information (code, product knowledge) and distill it down to something that can fit in the context, ready for implementation. And it's that research that is going to get harder the bigger the codebase. (My current experience is with a repo around 1.5 million lines.) I'm saying that the output of that research, in my experience, doesn't need to be anything like the detail of an exact spec. It can be a sort of one-to-two-pager Markdown doc, at most – and any further detail is much more ergonomic for me to iterate over in the form of code.
In the EU you can also generally look up the cost, even in cases where the patient doesn't pay, there is a bill and fixed costs. The costs are what the government pays or what a foreigner with no medical coverage and insurance would pay. It's also generally a tiny fraction of the cost in the US.
I use all of the current versions of ChatGPT, Gemini, and Claude.
The hallucination rates are about the same as far as I can tell. It depends mostly on how niche the area is, not which model. They do seem to train on somewhat different sets of academic sources, so it's good to use them all.
I'm not talking about deep research or advanced thinking modes -- those are great for some tasks but don't really add anything when you're just looking for all the sources on a subject, as opposed to a research report.
OpenAI has published a great deal of information about hallucination rates, as have the other major LLM providers.
You can't just give one single global hallucination rate since the rates depend on the different use cases and despite the abundant amount of information available to people on how to pick the appropriate tool for a given task, it seems very few people care to take the time to actually first recognize that these LLMs are tools, and that you do need to learn how to use these tools in order to be productive with them.
Voice control is much much much worse than touch screens. You have to try 10-20 times to spell out a command before it maybe does want you want, and that is if you're incredibly lucky
That's weird. I never ever had trouble with voice menus on the phone, and I bet that phone lines are worse than what you can have in a car, and the processing resources spent on recognition should not be large.
Mileage varies a lot on this one. I have an Android Automotive car (not to be confused with Android Auto) and “Hey Google” is basically flawless for changing car settings while driving.
I've seen this for coding agents using spec-driven development for example. You can try to divide agents into lots of different roles that roughly correspond to human job positions, like for example BMad does, or you can simply make each agent do a task and have a template for the task. Like make an implementation plan using a template for an implementation plan or make a task list, using a template for a task list. In general, I've gotten much better results with agents that has a specific task to do than trying to give a role, with a job-like description.
For code review, I don't use a code reviewer agent, instead I've defined a dozen code reviewing tasks, that each runs as separate agents (though I group some related tasks together).
