I've personally started focusing a lot more on code quality and communication skills over correctness of solving some leetcode problem. If I could get the infrastructure in place for it in the interview, I would have candidates generate something via AI and watch their process for that (how do they evaluate a plan, how do they review the code, etc.).
It will also cause a lot of trouble for companies with specific data access policies (probably most large companies). My money is on this new thing getting gutted very quickly as they figure out how much this constraint cuts into their bottom line.
That's definitely the case as model distillation is one of the explicit safety carveouts they mention. Though TBF, model distillation is also a big concern for general safety as distillation could allow you to have the model without the other guardrails. It's sort of a master key to the model.
While less necessary with AI, Excel is still the king of data entry and basic data manipulation (sorting, filtering, updating, etc.). I’d say that SQLite with a GUI for visualization is a far stronger competitor than Jupyter at those sorts of things. You can do that stuff in Jupyter, but it’s easier in Excel.
Jupyter also has a janky execution model. It doesn’t track dependencies so you have to be very careful in how you separate cells from one another and just running the whole notebook every time seems kind of pointless vs just writing a pure Python script.
std::array can sometimes give you the best of both worlds for stack allocation in that you statically constrain the stack allocation size (no alloca) while guaranteeing that your buffers are large enough for your data. You can also do a lot of powerful things with constexpr that are just not possible with arrays. It is very convenient for maintaining static mappings from enums to some other values.
That is not what they should do according to microeconomics because luxury goods are Veblen goods. Decreasing price would lower demand, at least until they lowered it enough that it was no longer a Veblen good.
Basic microeconomics is just that: basic and thus an oversimplification.
This already happens a lot for used clothes with the thrift store->poor country->landfill pipeline. That third step will likely be a lot less rare with new clothes.
A vector of boxes is beneficial if you need to move objects around. If each T is 1000B or something, you really don’t want to copy or even do true moves in memory.
Hell, even if you’re not moving things around explicitly, don’t forget that vectors resize themselves. If you use Box, then these resizes will be less painful with large objects.
I enjoy both and have ended up using AI a lot differently than vibe coders. I rarely use it for generating implementations, but I use it extensively for helping me understand docs/apis and more importantly, for debugging. AI saves me so much time trying to figure out why things aren’t working and in code review.
I deliberately avoid full vibe coding since I think doing so will rust my skills as a programmer. It also really doesn’t save much time in my experience. Once I have a design in mind, implementation is not the hard part.
I think the examples others have highlighted show the problem with just making it a library. They’re all lacking a lot of features from Prolog, particularly in terms of optimization. Just use Prolog if you need its features and invoke SWI-Prolog like you’d call any other language’s interpreter.
I might need to try it out. However, I haven't really found a use case yet where the speed of Python has been a major factor in my day job. It's usually fast enough and is a lot easier to optimize than many languages.
I actually sped up a script the other day that had been written in bash by 200x by moving it over to Python and rewriting the regexes so they could run on whole files all at once instead of line by line. Performance problems are most often from poorly written code in my experience, not a slow language.
And it needs to be said that you generally cannot tell if a vulnerability is critical for a given application except by evaluating the vulnerability in the context of said application. One that I've seen is some critical DoS vulnerability due to a poorly crafted regex. That sort of vulnerability is only relevant if you are passing untrusted input to that regex.
You absolutely don't need extensions for JS development. It is absolutely NOT notepad level. In my experience with beginners, installing an extension is also incredibly easy compared to getting them to edit some vim/emacs config.
I think it depends on the music. Most people will have a greatly improved experience when listening to opera if they have access to (translated) lyrics. Even if you know the language of an opera, it can be extremely difficult for a lot of people to understand the lyrics due to all the ornamentation.
I'm pretty sure you could even have lyrics with a separate copyright from the composition itself. For example, you can clearly have lyrics without the music and you can have the composition alone in the case that it is performed as an instrumental cover or something.
I haven’t used Codex a lot, but GPT-5 is just a bit smarter in agent mode than Claude 4.5. The most challenging thing I’ve used it for is for code review and GPT-5 somewhat regularly found intricate bugs that Claude missed. However, Claude seemed to be better at following directions exactly vs GPT-5 which requires a lot more precision.
My favorite is when someone is demoing something that AI can do and they have to feed it some gigantic prompt. At that point, I often ask whether the AI has really made things faster/better or if we've just replaced the old way with an opaque black box.