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this post was submitted on 21 Sep 2026
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Open source, but made with a copyright infringing LLM.
oh no, how awful of them to release the code back into the wild for free with a copyleft license. truly an awful thing to do.
i love how anti ai people have become the wardens of copyright somehow.
Statement of fact. Claude was engineered with copyright material.
whats wrong is not copyright theft. whats wrong is labour that was stolen.
Yes and no. As an author of open source projects, they were made available for people to fork, improve and conrribute under permissive licenses. Then come LLMs that uses code like mine to generate code or slop. My choice about license was made before LLMs. Now they use derivatives of my work without attribution as required by the license.
Even if I change the license, it won't be respected, and nothing else will change. To me, that's copyright infringement on me, ignoring the hours I put in.
On a separate note, LLMs only produce more of the same, ignoring the environmental and economic impacts. Often, more of the same is not what we need. It leads to the same mistakes being made. I've never used LLM to code, never will.
Whisper is not an llm, just because it is trained on big datasets does not make it an LLM. Copyright infringement still happened during the training.
no, but claude is, and it's the main contributor to the repo.
Which software are we talking about? Unfortunately no one links to it, so I will guess. Whisper: website | source is an Ai model, specifically powered by OpenAi.
Brother, I don't think you know what a large language model is if you think it being trained on a human lifespan's worth of audio transcripts isn't enough.
Its job is NLP. It is an LLM, if not as general-use as something like ChatGPT.
LLM is a subset of NLP. All LLMs are NLP but not all NLP are LLMs. To be more precise, LLM is a method of achieving NLP while NLP is the goal. Whisper is just a specialised speech-to-text engine. It doesn't have the text reasoning engine that is the core of LLMs. Yes, it has a dataset to inform it's encoder and decoder, but that doesn't make it an LLM. If you strip out the encoder, then you're getting closer.
That's flat-out not true. BERTs, for example, are LLMs and are encoder-only, just like GPTs are decoder-only. Whisper is trained on a large corpus of text and does NLP tasks. That's all it is to be an LLM.
A transformer model being encoder-only, encoder-decoder, or decoder-only has fuck-all to do with whether it's an LLM. In fact, it being a transformer model at all isn't required for it to be an LLM.
BERT is a special case. The academic consensus is rightly considering it an LLM since it operates on text but the industry doesn't consider it an LLM since LLM in its colloquial meaning has drifted to mean a model generating text (which BERT doesn't). Whisper is not an LLM since it's working on audio, so it is an Automatic Speech Recognition model (ASR).
Text in = LLM in academic circles (BERT and GPT)
Text in, text out = LLM in all circles (GPT only)
Audio in, text out ≠ LLM (Whisper)
Now, we can argue whether we want to accept the colloquial meaning of LLM, but the fact is that Whisper is not an LLM. And neither ASR nor BERT causes even a fraction of the damage GPT is doing, but that's another discussion.