By IO heavy I meant db operations or other external requests. When the request handler starts, it waits for the IO to be completed. While it waits, it can accept other requests and so on, so the bottleneck is the IO in my case, not the request parsing.
I imagine it like this (imaginary numbers):
Which case, it wouldn’t matter which http framework to use. However, there are probably other use-cases.
How much overhead does a simple request handler have with Brhama and with Express in ms?
It matters because most of my endpoints are IO heavy. I assume the framework cost is negligible compared to that, and if it is negligible for typical use-case, then what use-cases do you see where it matters most?
With SQL you scale it when it is required by sharding, read replicas, cache layers, and denormalization.
With NoSQL afaik, we have to deal with the scaling from the beginning by keeping the consistency of denormalized data, that has additional code overhead. Is mongoDB different in this regard?
That’s not the point of JSONB. Use normalized tables whenever you can. JSONB allows you to store a document with unknown structure, and it allows you to access that data within SQL.
The concept of understanding implies some form of meta-knowledge about the subject.
That can be solved if you teach it the meta-knowledge with intermediary steps, for example:
prompt: 34*3=
step1: 4*3 + 30*3 =
step2: 12 + 10*3*3 =
step3: 12 + 10*9=
step4: 12 + 90 =
step5: 100 + 2 =
step6: 102
result: 102
It’s hard to find such learning data though, but e.g. claude already uses intermediary steps. It preprocesses your input multiple times. It writes code, runs code to process your input, and that’s still not the final response. Unfortunately, it’s already smarter than some junior developers, and its consequence is worrying.
But LLMs are not simply probabilistic machines. They are neural nets. For sure, they haven’t seen the world. They didn’t learn the way we learn. What they mean by a caterpillar is just a vector. For humans, that’s a 3D, colorful, soft object with some traits.
You can’t expect that a being that sees chars and produces chars knows what we mean by a caterpillar. Their job is to figure out the next char. But you could expect them to understand some grammar rules. Although, we can’t expect them to explain the grammar.
For another example, I wrote a simple neural net, and with 6 neurons it could learn XOR. I think we can say that it understands XOR. Can’t we? Or would you say then that an XOR gate understands XOR better? I would not use the word understand for something that cannot learn. But why wouldn’t we use it for a NN?
Any explanation? If they can write text, I assume they understand grammar. They are definetly skilled in a way. If you do snowboarding, do you understand snowboarding? The word “understand” can be misleading. That’s why I’m asking what’s understanding?
What’s understanding? Isn’t understanding just a consequence of neurons communicating with each other? This case LLMs with deep learning can understand things.
You can try dualbooting a linux distro with an android. I expect it works, but you cannot be sure with phones.
I’m running Arch for a very long time. I agree this is not a distro for general audience. I disagree, however, that it is not stable. When I’m doing work I don’t update my system. I enjoy my stable configuration and when I have time, I do update, I curiously watch which amazing foss software had an update. And I try them. I check my new firefox. I check gimp’s new features. etc… or if I have to do something I easily fix it, like in no time because I know my OS. Then I enjoy my stable system again.
Do you want to know what’s unstable? When I had my new AMD GPU that I built my own kernel for, because the driver wasn’t in mainline. And it randomly crashed the system. That’s unstable.
Or when I installed my 3rd DE in ubuntu and apt couldn’t deal with it, it somehow removed X.org. And I couldn’t fix it. That’s also something I don’t want. Arch updates are much better than this.
They will probably get to there. It’s just not that important for the developers rn. They are working on a pacman rust rewrite and hopefully we can see more contributions to the project. I already considered contributing but C deterred me.
You can see the milestones here: https://gitlab.archlinux.org/archlinux/alpm/alpm/-/milestones
Was it even a thing? I remember I had to choose MBR for legacy BOOT, GPT for UEFI.
Seems like a lot of supportive commenters didn’t try CSS-IN-JS like @emotion/styled, stitches, styled-components. Where are you guys? Why learning alternative names for CSS rules considered to be better, than just using those good ol’ "let you do everything what you want"s.
Buy good hardware next time
you can remap keys with any keyboards
I use Ctrl, Alt for applications, Super for the os/windowing. I hated MacOS which mixed these things. Luckily X.org let’s you do whatever you like, sometimes it’s just harder to configure. But I like it as it is.
urxvt, bspwm, sxhkd, and many small utilities that I built my desktop with. It’s hard to reproduce the same setup.
You listed malwares. Nvidia works tho.
Most of the time a popular distro just works, your special case did not. You should find the root cause, and report it. I’m sure windows is not bug free.
So, for example, online editors that store state in huge jsons and has frequent backup can benefit from it. That’s actually great, good luck with it!