It's actually worse than that. In a repo where there is a heavy amount of AI code, it becomes impossible to manually write code. The AI code favors obtuse, unmaintainable mess (from the standpoint of a non-AI dev). Constantly overwriting your changes, large comments, hacky logic, overly-defensive programming, misunderstandings that the LLM made that cause you to waste hours debugging.

I've come to the realization that AI code bases are effectively AI-only code bases, unless one is using AI as just a code-autocomplete, then that seems mostly fine. But most devs are using it as an end2end implementation tool, having it 1-shot full features, and then iterating on them with the agent afterwards. The amount of churn and terrible code stops the manual coder from doing anything effectively.

In my own team's codebase, I'm confident that we would actually be further along without AI, and that AI coding is a hindrance (although AI debugging seems to be a pretty good tool). It is made worse that everyone does code reviews with AI now, and it constantly gets things wrong, and when its right, it's usually for the most basic, unimportant issues (again with the overly defensive coding style). This waste the manual coder time trying to figure out if the issue its requesting a fix for is an actual problem or not.

Curious to get other's thoughts, but I recently got into a disagreement with a coworker when I stated that you can't be programming as a hobby if an AI writes the code for you. You could be developing software but if you're not actually writing the code, then you're not hobbyist programmer.

There is a big difference IMO between someone who wants to code traditionally and one that uses AI when it comes to developing software. And this difference illustrates what's a software developer to a programmer.

Seems like a fun way to teach basic calculus. I think a notebook style, maybe a Julia equivalent of Jupyter, would work better.

From my quick skim, the book is quite light on proofs, which I think is a big mistake for intro calculus classes. Proofs are usually what you need to get a solid feel for the material, at least at the start. Developing intuition on how things work by playing around with Julia, and using that to inform exploration to come up with theorems, and using that data to help develop a proof would be a more interesting approach. Maybe they do this in later chapters. For example, when teaching eulers method for integrating the ODE, visually graphing the error compared to the actual as step size changes could give the reader some intuition about the order of the method.

We have Pluto (https://plutojl.org/)!

Jupyter handles Julia too, but Pluto is designed specifically for it, and is lovely. I'm using it to teach an intro to ecological modelling course right now.

Doesn't the _J_ in Jupyter stand for "Julia"?

Arguably, the effort of the reader manually transcribing the code into their own Jupyter notebook seems didactic.

Oh dang, didn't know that. Learn something new everyday.

I think you're right that manually transcribing to their own notebook would be better for learning, but I question how many students would actually do that. Having in a notebook at the start allows for better interactivity I think.

The Ju, yeah.

Isn't this easily catchable with static analysis, -Werror -Wall? I've never had a practical problem with this.

Also arrays aren't pointers in C, they decay into pointers. You can see this since sizeof will work differently in the function that instantiates the array versus one that takes in the pointer as a parameter.

My theory is that people run into the inevitable difficulty of math and rather than look inwards and come to the conclusion they aren't as smart, or special as they once thought, they blame "the system". It can't be that they haven't tried hard enough or aren't smart enough, it's the gated community who hides behind esoteric jargon that stops them from being successful in their mathematical studies.

They believe this despite the fact that there are a million different textbooks, all explaining the same concepts in different ways, or different branches that specify the same structures with unique methods. It's really strange when I read threads like this. I'm not the greatest at math either, but that's because it's a hard thing to be good at.

To learn math is an exercise in humility. You must struggle hard, do hundreds of problems, spend years refining your understanding, redoing proofs, finding counter examples, just to be at a good undergraduate level. Much harder than pretty much all CS classes, except maybe Theory of Computation, which is pretty much math.

Yes, that could be the case. I'm not a mathematician but I gave my fair share of struggle towards trying to understand the mathematics behind physics that I was studying. You don't need to know what a smooth manifold is to solve problems in classical mechanics, just as you don't need to know what a tensor product of vector spaces is to learn what tensors are in the context of neural networks. The former is always harder because the standard of rigor is higher, and that's because of different goals. So it could be that a person tries to look up what a tensor is, finds the mathematician's definition first, thinks "what the hell is this", then finds a simpler explanation in applied contexts and goes away with the idea that mathematicians unnecessarily complicate things, because the reason why the mathematical definition has to be so complicated escapes them.

I've never met someone who believed this who was actually able to solve simple math or physics questions given to them. Attitudes like this always feel like cope to me.

Read your original comment and disagreed, but you have changed my mind. Trump is tackling climate change response more than any other president, he's just doing it in the most psychotic way possible. Threatening sovereign nations, antagonizing Asia, etc.

I agree that Canada and the EU should've been more responsible for their own defense. Hope going forward they are and cut out the USA as much as possible.

> "work, study and live where they want on either side of the Atlantic"

Will the Canadian job market improve or suffer here? Does anyone know of any research that will determine if this would be beneficial for Canadians? Naively, I think it'll be harder to compete with Europeans for jobs.

Canada has needed more workers but is suffering from a lack of affordable($675000 average when the average family makes <$110000) housing like many places with other costs rising too. The number of housing starts has never recovered from the huge downturn around 2010.

We don't need more workers. This is a corporate lie to suppress wages. It's clear some of you haven't spoken to a new grad looking for a job in ages.

I dont mean minimum wage. I mean skilled trades and highly educated. Who is going to build the homes we are build 1/2 to 2/4 what we did prior to 2010. Who is going to work the R&D roles when the Canadians are all working

Wage suppression can be dealt with by making the wages for visa workers higher than some formula that takes local and national averages into account

There's no labor shortage there either. Go look at the Canada trades/jobs subreddits. Go look at the experiences of new grads who can't find an entry level job that pays a decent wage. Go look at how many kids want to enter the trades but can't get anyone to take them on as an apprentice. Software developers, engineers, etc. Even people with experience are out of work for months. Skilled laborers are sitting around with nothing to do because nobody is building houses.

Why do you think any Canadian grad who is able moves to the States? If we truly had such a dire labor shortage, surely they'd be able to work here and be paid well for it?

You might be right, but I honestly think that this is a situation where competition helps both sides to pull themselves up.

It will definitely depend by work market, eg it's well known in France video game worker move to Quebec already.

Plenty of people use Oxford commas though, can't really use it as a reliable signal, too much noise.