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.

Obvious things first, cameras have way worse contrast and low light sensitivity than human eyes.

Humans have much more evolved logical thinking capacity, even the stupid ones can figure stuff out that modern AI struggles with.

Humans have other sensors, too that they use to plausibility check the picture they see. I.e. one of the best sensor fusion systems on the planet.

When in doubt humans can figure out whether it's a lens occlusion or a some other artifact in their vision by virtue of moving their head around.

There's probably other things I'm not thinking of. In any case to make full self driving work we should first start by using all available tech to make it safe. When you have safe tech you can slowly start removing individual sensors while verifying that safety remains high. As the experience and system evolves there will be optimization potential.

And until we have that low light thing and high contrast figured out, camera alone doesn't cut it.

I'd pay you 10€ for a TODO app that improved my life meaningfully. It would obviously need to have great UX and be stable. Those are table stakes.

I don't have the time to look at all these apps though. If somebody tells me they made a great TODO app, I'm already mentally filtering them out. There's just too much noise here.

Does your TODO app solve any meaningful problem beyond the bare minimum? Does it solve your procrastination? Does it remind you at the right time?

If it doesn't answer this in the first 2 seconds of your pitch you're out.