I know this is contrary to your advice but mechanical pencil, get a Kuru Toga, it's a really nice improvement and not very expensive. It auto-rotates the pencil tip, so you don't have the sharp end at some point.

For permanent writing get a Uni ball gel writer, it's supremely smooth to write with. Also not hugely expensive.

They're both not the cheapest option, but an amazing writing experience without being the fancy kind of expensive.

Can we use this instead of grout in the bathroom somehow?

Yes, the math department.

In any case linear algebra, stochastics, calculus; plenty of engineering and science applications for all these.

It makes you feel that way because it is that way. They're not self-sufficient.

Especially in a filesystem I know where I placed something, but not always the title, so even if the search function was ok, which it mostly isn't, having to know the wording used for the title is really inconvenient.

I still can't wrap my head around how people find their files in the non-filesystem world. Whenever I need to work with files I take out my laptop.

At least codex listens to me telling it to use rg instead of grep, cause grep is often so slow. But when adding rtk it uses grep through rtk which is kind of annoying.

It's easier to find a needle in the haystack if the haystack is 50% needles.

Besides OO -> Functional this applies everywhere else in Computer Science. If you understood the fundamentals no new framework, language or paradigm can shock you. The similarities are clear once you have a fitting world model.

Even if it's once off, some people handle a lot of once-offs, that's exactly where you need good CLI tooling to support it.

Sure jq isn't exactly super slow, but I also have avoided it in pipelines where I just need faster throughput.

rg was insanely useful in a project I once got where they had about 5GB of source files, a lot of them auto-generated. And you needed to find stuff in there. People were using Notepad++ and waiting minutes for a query to find something in the haystack. rg returned results in seconds.

Still if you go blue collar you have to build your own business.

But that's the thing, in other languages you don't need a workaround to work on primitives directly.

If you ask them in isolation they may write a script to solve it "properly", but I guess this is because they added enough of these to the training set. But this workaround doesn't scale.

As soon as I give the LLM a proper problem and a small part of it requires numeric reasoning, it almost always hallucinates something and doesn't solve it with a script.

If the logic/math is part of a larger problem the miss rate is near 100%.

LLMs have massive amounts of knowledge, encoded in verbal intelligence, but their logic intelligence is well below even average human intelligence.

If you look at how they work (tokenization and embeddings) it's clear that transformers will not solve the issue. The escape hatches only work very unreliably.