21–30 of 42 posts
Earlier quoted context omitted.
computer science is not social science if your code doesn't work it doesn't work you can't bullshit a computer for people that are doing social science it's an issue but they where way past the point of no return already so it doesn't really matter
Code can absolutely "kinda work" or "mostly work"
Have you even dogfooded your quiz once before publishing to the Web? > JS methods to select an element > YOUR ANSWER > getMethod: "getQuerySelectorAll" > EXAMPLE CORRECT ANSWER > const button = document.example('example'); TypeError: document.example is not a function ---- > Create an HTML form with an input field and submit button: > YOUR ANSWER > Submit > EXAMPLE CORRECT ANSWER > Submit Not incorrect answer assesse…
I don’t see in which context coding without aid is helpful: - at work, nobody cares. They only want shipped features. - at home, I only care about having fun (and that doesn’t necessarily mean to not use tools like ides, llms, autocomplete, etc)
it's helpful for them, because it means they can get a lot of VC money by putting some ugly 4chan meme pictures on a website in order to get attention
Always love a fun quiz, but some of the example answers are just incorrect. Eg: `document.example('example')` to select a HTML button element. Others are to rigid to allow the true breadth of correct answers. Ya'll might want to switch from V0 to claude code.
I am not worried about personal atrophy. I am worried about young people who never learn the fundamentals and blindly believe everything the llm say.
computer science is not social science if your code doesn't work it doesn't work you can't bullshit a computer for people that are doing social science it's an issue but they where way past the point of no return already so it doesn't really matter
you can't bullshit a computer
this is wrong. I would argue the difference between a junior dev/intern and a senior engineer is that while both can write code that works, the juniors find local maximas, like solutions that work, but can't scale, or wont be very easy to integrate/add features on top/maintain etc.
This happens in maths, biology, in all science fields. Experience is partly the ability to take decisions between options that both work.
This is why coding assistants are amazing at executing things you are clear on what you want to do, but can't help (yet) on big picture tweaks
I am not worried about personal atrophy. I am worried about young people who never learn the fundamentals and blindly believe everything the llm say.
computer science is not social science if your code doesn't work it doesn't work you can't bullshit a computer for people that are doing social science it's an issue but they where way past the point of no return already so it doesn't really matter
Why is the code like that? How are people likely to use an API? How does code change over time? How can we work effectively on a codebase that's too big for any single person to understand? How can we steer the direction of a codebase over a long timescale when it's constantly changing every day?
I am not worried about personal atrophy. I am worried about young people who never learn the fundamentals and blindly believe everything the llm say.
computer science is not social science if your code doesn't work it doesn't work you can't bullshit a computer for people that are doing social science it's an issue but they where way past the point of no return already so it doesn't really matter
Earlier quoted context omitted.
computer science is not social science if your code doesn't work it doesn't work you can't bullshit a computer for people that are doing social science it's an issue but they where way past the point of no return already so it doesn't really matter
>if your code doesn't work it doesn't work you can't bullshit a computer this is wrong. I would argue the difference between a junior dev/intern and a senior engineer is that while both can write code that works , the juniors find local maximas, like solutions that work, but can't scale, or wont be very easy to integrate/add features on top/maintain etc. This happens in maths, biology, in all science fields. Experien…
My point being that it's quite easy to demonstrate that it can't scale, by running an experiment.
Meaning that you could quite easily BS your way through that by just agreeing with whatever the status quo is.
Whereas in social science you can't do an empirical experiment, so you're epistemologically on much much more shakier ground
> This happens in maths, biology, in all science fields Right but I wrote social science and not maths or biology.
For instance if someone where to say that due to Hegelian Dialeticts and gender critical theory, in the future women are destined to rule the world, this is a good thing, and this will lead to the abolishment of racial inequality and exploitation through capitalism
how do you prove that?
in comparison if the problem is that your software isn't efficient when there are over 100 instance, you can prove that by spinning up 100 instances?
You can't clone earth and force all the inhabitants to enact ideologically pure race critical theory, and then ask the inhabitants in the control group to try out nazism, wait for a while and then use that to prove that one or the other is the best way can you?