Agree with you on exercise. But staring at a painting is to meditation what curls are to building your core. People all recommend the same type of meditation for a reason--so that you can close your eyes, focus on something, notice when your mind drifts, and bring the attention back. You didn't mention anything about awareness or noticing when you lose focus, so your advice seems to completely miss the point.
It's crazy that you could actually use the excuse that since it's all vibe-coded, there's no way a human could have written it, so Anthropic bears no responsibility.
Meanwhile humans can pop in and leave little morsels like this and blame it on the model.
>These AI images also add to the public mistrust of AI, a growing problem for innovation in a field that is sometimes seen as biased, opaque and extractive.
Oh my, how would anyone ever have gotten that impression?!
Your instinct is correct, and in a lot of cases it's true. However, I've heard from enough doctors by now (a cardiologist, psychiatrist, and epidemiologist/former physician) that they use medical LLMs and find them extremely helpful, mostly as a way to either bring up knowledge they'd forgotten about or as a way to learn something new and then verify it. I'm extremely skeptical about LLMs in general and the connection to Gell-Mann Amnesia is apt, but I wouldn't necessarily write them off completely like that. There are experts using the models that find them genuinely helpful in their field.
I'm asking genuinely, is there a connection between housing, education, and healthcare becoming so much more expensive and them also being the three parts of the economy that have the most government interference (in the US)? If so is it causal?
I don't want to be cynical, but maybe spending hours every day using Claude has made some of us particularly attuned to picking this up. For some reason as soon as I read "The trap was in app/test/index.js," I instantly knew it was Claude. It's too bad, because there will obviously be some false positives, but it makes me immediately disregard the author.
I wouldn't agree with that. The issue with software is that the people you make things for are usually anonymous and you'll never meet them, but if you've ever built software that helped someone and you witnessed it, it feels really good.
This is such a tired, meaningless argument. I've never seen a human in 10 years of professional software engineering at a large company ever so confidently, consistently create and send out seemingly well-reasoned code that's as wrong as what SOTA models using CC or Codex do. If a human did this, they would be fired or perpetually remain a junior who no one wants to work with.
Also, if a human does this, you can replace them and get a human who will not do it. The default for an LLM is to generate plausible-looking text that may or may not be completely incoherent. That is not the default for a human. Again, if you find that your colleague consistently fabricates APIs, you can hire someone who isn't crazy instead, but you cannot do the same with LLMs.
This is commonly known as "LLM-as-a-judge" and anecdotally multiple people I know who write code using OpenRouter or using multiple models say it's surprisingly effective. It's strange that there don't appear to be any major papers on it since ~early 2025, which at this point is basically ancient history.
Ah yes, the magical equivalent of "you are a senior software engineer who writes bug-free code".
IME people would benefit greatly from the process, albeit tedious and time-consuming, of testing out the same prompt sequence/session with the exact same model multiple times. It becomes clear extremely quickly how capable but unreliable and inconsistent a model can be even when given the same context. If you have ever completed a long, complicated task with an agent and then lost the session and tried doing the same thing again from scratch you may have had the experience of seeing the subtle changes that come up in the model's thinking which lead it to accept or reject certain paths and ignore or incorporate prompt instructions like the one you've provided.
Do you know if anyone has trained, say, a pre-2017 model and tried to get it to come up with Attention Is All You Need? If it did, would you say that was only because it's a synthesis of prior art? If so, what isn't?