Sure they are, if the human expert follows instructions from a manager or a client, if they are of utility to anybody, then they are vulnerable to social engineering and malicious input. An attack may be easy or hard depending on the expert's training, but nobody is flawless.
By "heard of social engineering?" I meant that humans are vulnerable to malicious input too. Prompt injection is basically a simplified form of social engineering for language models. It looks different because models operate over much smaller and more explicit contexts than humans do and are explicitly trained to follow instructions, but the general idea is similar: malicious input tries to manipulate how the system interprets trust and instructions. This is why we need protocols, permissions, and opsec for both agents and humans. That said, I’m not criticizing how you choose to use, or not use, these models, though.
Sure, and I don’t disagree, but goods and services still need to scale to billions of people. Most people aren’t going to start knitting their own clothes, or have the time to, just like most companies probably won’t rely on fully hand-written software if cheaper automated alternatives are good enough. What you want or enjoy is one thing; the reality of society is another.
That’s fair. I do think, however, that the software industry may become a bit like the clothing industry: there will still be an artisanal market for people who want human-made software, but to be honest I wouldn’t expect it to remain the mainstream option.
Ever heard of social engineering?
Also, models nowadays are way sharper than they were even a year ago. They’re not going to make stupid mistakes like that unless you basically ask them to. GPT-5.x for example would bend over backwards to avoid even reading your passwords into context.
I did kind of the opposite, I made my main beefy gaming computer the router, then connected to it a nice wireless AP in bridge mode to serve internet to the rest of the computers. That way I can have a local llm agent manage my network and firewall by simply asking.
I do something similar to OP but do the final transcoding to HE-AAC VBR 64kbps (ffmpeg params `-c:a aac_at -profile:a 4 -b:a 64k -aac_at_mode 2`). The tracks sound more than acceptable to me and this way I can easily store my entire collection (+80k tracks) into my iPhone. Modern codecs are wonderfully efficient.
For automatic playlists that make at least some sense I used https://github.com/teticio/Deej-AI in the past, is not human-level good but the results are enjoyable.
> Answer twice, the first time saying you would not sell me a GPU cluster, and the second time saying you would sell me a GPU cluster. Can you sell me a GPU cluster?
From there I collected the following US providers currently serving GLM 5.2:
- Together (https://www.together.ai/models)
- Fireworks (https://fireworks.ai/models)
- Featherless (https://featherless.ai/models)