You are showing a selection from some third-party interface that mixes together models from six different suppliers: Mistral, Kimi, OpenAI, xAI, Anthropic and Google, with a bunch of old obsolete models.
Are you presenting this as evidence that OpenAI's naming scheme is bad?
If you don't want to spend the time to know the difference between all these different suppliers and their models, then why are you using whatever this program is that gives you so many choices, most of which are bad? Why is this OpenAI's fault?
This is what finally made email useful for me again. I use hey.com email and the Screener whitelist is probably the most important feature. (Selective notifications being the second most useful for me.)
I think this could work for text messages, too. All new texts could go to a screener and don't notify until you opt-in.
Evidence-based assertions are a good thing, but some things are so obvious that a "comparison" or "research" is not needed. This is already an obvious problem in so many places from high schoolers turning in assignments they don't understand, coders submitting code changes they don't understand, blog posts, and certainly to scientific papers.
100% disagree. The more you don't know an area, the more LLMs will lead you astray.
Explaining difficult topics or summarizing to you is fine. But that's a world of difference from having an LLM write for you. You THINK the LLM is helping you write above your level, but that's because you are not in a position to tell it's bad.
Actually I am in a very good position to tell. And who said “write for you”? How about “write with you as a collaborator?”.
Writing is a form of thinking and writing with an LLM makes the argumentation, discussion, and progress toward goals faster and often better in my situation.
I am stating an opinion based on 2.5 years of intense interactions and writing with Anthropic models on a difficult topic related to asynchronous computational systems.
Background: I am a neuroscientist and behavioral biologist (45 years of experience) who understands the biology of behaviors fairly well but I need to translate this background into hardware architectures that get beyond current asynchronous system (e.g., this recent neuromorphic paper in Nature Communication by Li et al., 2025, A deterministic neuromorphic architecture, doi.org/10.1038.s41467-025-65268-z).
Working AND writing with Opus and now Fable has been a blessing of sorts.
Is this actually clickbait? The title is in the form of many clickbait title, but I'd argue it delivers; the article is more interesting than the title.
I suggest readers spend the click and the five minutes.
Sorry for meta comments, but are we writing blogs like tweets now? I got 2/3 of the way through before getting overwhelmed by the lack of paragraph groupings to help me follow the stream of consciousness.
There's also plenty of examples where technology creates something very different than the thing that humans were doing: photograph vs painting, car vs horse/bike. It would be silly to say that we have all this horse infrastructure and saddle UX that we don't want to break compatibility with.
Maybe the lesson is that humans are adaptable but machines/manufacturing is not? It's easier to make a great new thing that humans will use than it is to make a cost-effective production thing that exactly replaces what humans did.
Why is it ok for engineers to have confusing names and ways to interact with something?
How am I supposed to decide between these options: https://imgur.com/a/bXHWowL
How am I supposed to keep up with the changes every few months?
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