jackling

I'm confused by your question. The video in question is an entity walking in a simulator and not real life? There's a world of difference between what can be achievable in a simulator, and how to transfer that simulator learning to the real world.

Real world is messy. Lowering the sim2real gap is challenging. The sim above doesn't model actuator dynamics, overheating, probably doesn't model inertia correctly, joint friction, delays, etc.

For the reasons I stated above. Many people like the community aspects, often more than solving the problem itself. It's nice to work with people, the problem is just an excuse to congregate.

> You talking like what you doing is happen to appease people

What's wrong with that? Appeasing people is part of what being a community is. That's the human element. I could write a grand novel for the fun of it, but I still want people to read it and talk about it. To give me feedback, to discuss its themes. If that's taken away due to technological advancement, it's a sad thing.

It is not a binary between doing what you like because you enjoy the craft or being happy that people engage with your output. Both of those things feed into each other. An old neighbour of mine used to craft bracelets and give them away around the neighborhood. Why would she spend the additional time to walk to my apartment and hand me a bracelet? Did she only make the bracelet to get the neighbourhood's approval and didn't actually enjoy making it? Of course not. She made it cause she likes doing crafts, AND she likes people enjoying what she made.

The unfortunate part is that LLMs are addictive and they remove a lot of the complex thinking one has to do (in many cases), which lowers that community engagement that occurs in software circles.

Not a very charitable viewing of people. It could be that people just liked all aspects of it. They liked the challenging work AND helping others. They liked the community of solving problems together and being useful for each other. They liked the difficult collaborations when trying to find solutions. They liked discussing with people who had this niche interest who also spent time obsessing over it to gain the skills necessary to make an impact.

LLMs have disrupted this. It's much easier for people to silo themselves off and fix any problem that appears, without the need of a community. It's easier for people to discuss topics via copy/pasting from an LLM versus actually having a deep understanding of the material.

> The original author had their own motivations for researching and producing and I can almost guarantee they weren’t reviewing the output of someone’s prompt.

You see this at work too. If I find a problem I want to solve for some personal motivation, I can no longer share with my coworkers what that problem is. Since when I do, inevitably, senior+ engineers and managers vibe code a solution. Often that solution is hacky and they expect others to fix the fundamental problems that exist.

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.

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.

But the article showed that that the port/translation of an established app resulted in a mess anyways. Looks like even with a good foundation it's prone to fail.

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.

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.

It genuinely breaks my brain how productive Sébastien is. Does he research and build these solutions by himself? Bevy has an extremely impressive voxel collision system, and now from what I can gather, maybe the most impressive real time soft body simulation. It really distinguishes itself as simulator, not just being a N+1 sim, but genuinely ground breaking in a lot of aspects. Amazing work! I wish I was as talented.

I hope they take a look at using a penalty based solver in the future, especially for their GPU sim, could really contend with MuJoCo/Genesis as the robotics sim engine.

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.

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.