Silamoth

That’s such a good one! I wish more people understood this. It seems management and business types always want some upfront plan. And I get it, to an extent. But we’ve learned this isn’t a very effective way to build software. You can’t think of all possible problems ahead of time, especially the first time around. Refactoring to solve problems with a flexible architecture it better than designing yourself into a rigid architecture that can’t adapt as you learn the problem space.

Statistics is absolutely fundamental to data science. But I’m not sure this relates to the above idea of “laws” being internally contradictory?

That’s how I understand it as well. It’s not about an abstract ideal of duplication but about making your life easier and your software less buggy. If you have to manually change something in 5 different places, there’s a good chance you’ll forget one of those places at some point and introduce a bug.

> Andrej Karpathy…is the most famous person on Git Hub

Is he really? I’ve only heard of him because HN is obsessed with his “AI” takes. Is he really that popular outside of this bubble?

Genuine question: Who uses stars on GitHub? Even if I use a library or tool, it’s never once occurred to me to give it a star on GitHub. Is this a real thing people do? And if so, why?

Very interesting article! I love reading about hacking in the classical sense: Using a system in way it’s not intended to accomplish an unexpected/unintended outcome. It’s a shame the media has perverted the term to exclusively refer to cyber attackers (“crackers”).

In some ways, I feel the hacker mindset embodies how I like to approach systems and problems. I like to dig in, get my hands dirty, and see how things work. In programming, this often means a lot of stack traces, breakpoints, and print statements. I’ve jumped into a lot of new areas and figured things out on my own.

But on the other hand, I often feel my hacker mindset isn’t nearly as well-developed as some people I meet. That’s probably a lot of imposter syndrome talking. But the things some people do are amazing, things I’d never even think to fathom, let alone attempt, let alone succeed at.

Care to share what those “other things” you’re concerned about are? If you mean what I suspect you do, then this is a complete false equivalence. But it’s hard to evaluate when you don’t actually state your claim or concerns.

Calling Richard Feynman a “professional bongo player” is hardly an honest description. He was a Nobel prize winning physics professor renowned for his problem-solving abilities. He was certainly qualified to analyze the Challenger explosion.

That’s funny because I don’t see civil engineers being “glazed” much online. Usually they’re the butt of jokes and ridicule from other engineering majors who perceive civil engineering as less ‘rigorous’ than other disciplines. I’m curious where you see this civil engineering “glazing”?

Ehh, that seems pretty reductive. I could just as easily claim women love games with character customization or games with deep stories. All of these things may have some truth to them. But (1) it’s unclear how universal this is and (2) it’s unclear if this differentiates women from men or is just something people in general like. “Good character design” is incredibly vague and appreciated by a lot of people.

Great article! Needless gendering absolutely hinders innnovation, in game design and elsewhere. (Not to mention the unfairness, oppression, and general absurdity).

Slightly unrelated, but the point about tutorials starting with the “basics”, i.e., “making a character move and attack” is interesting. On the one hand, if you have a strong enough grasp on programming fundamentals, it should be pretty easy to take what you learn there and make a “dress-up game”. Heck, a basic dress-up game shouldn’t be any harder than a platformer.

But if you lack that fundamental knowledge and are only interested in games, you need to develop it somehow, and you don’t want to build ‘boring’ console apps; games should be a platform for learning programming. So I agree wholeheartedly with the author: we need more diversity in introductory game programming tutorials!

Of course, that brings us to another can of worms with programming education: Tutorial Hell. But beginners need to start somewhere, and that somewhere should motivate them to continue learning and exploring on their own.

Am I the only one disappointed this was about some LLM slop and not digital signal processing? DSP is a well-established technical acronym, so I expected to hear about a new Python DSP library. Oh well.

It’s had an impact on software for sure. Now I have to fix my coworker’s AI slop code all the time. I guess it could be a positive for my job security. But acting like “AI” has had a wildly positive impact on software seems, at best, a simplification and, at worst, the opposite of reality.

I feel this article's argument is weak, largely for one key reason: They don't clearly define anything. Their references might clarify some things, but not all. They argue against "general problem-solving strategies" with a reference to Polya, but they don't provide a clear definition of what these strategies entail. How broad is the set of strategies they're arguing against? What are some examples of such strategies? I'd like something beyond two sentences on Polya.

Furthermore, what audience and level of mathematics education are we discussing? The goals (and hence appropriate metrics of success) are certainly different for high schoolers targeting non-STEM careers vs. engineering undergrads vs. math grad students. The authors reference "aspiring mathematicians" and "domain specific mathematical problem-solving skills", indicating they're arguing about education for math majors, or at least students in STEM fields. In that case, the argument is somewhat meaningless - who's arguing math majors shouldn't learn math-specific skills? But, as I understand it, the argument for general problem-solving skills is that students outside of math don't actually need many specific math skills. Instead, math is a vessel for teaching logic, reasoning, and problem-solving skills. Then again, this might not be the type of problem-solving the authors are referencing - as I said above, it's not very clear.

On a similar note, they cite evidence that studying worked examples is more effective than "general problem-solving strategies", citing an "improvement in subsequent problem-solving performance" without explaining how this performance is measured. If students are tested on specific problem types, of course they'll perform better when taught strategies for those specific problem types. But it's not clear that this is meaningful. For STEM majors, sure, solving specific problems is a skill worth cultivating. But for most students, solving specific problems isn't as important as learning logic, reasoning, and general problem-solving skills. In my anecdotal experience tutoring math, students tend to just memorize strategies for specific problem types instead of learning transferable logic and reasoning skills because that's what's tested. I'd be curious to see which method of learning facilitates better performance on a more general problem-solving test of some sort.

Now, I'm not an education researcher or an educator of any sort. But I am passionate about good STEM education, especially in math. I genuinely feel that math education fails most students, at least here in America. If I'm being generous, this article is a well-intentioned but poorly-executed argument for effective math education strategies. If I'm not being so generous, this article advocates for the status quo in math education that forces students to slog through years of math classes for little discernible benefit. Either way, it's a disappointing article with a poorly-explained thesis.

Godot is easily my favorite game engine. The node system is simple yet powerful, truly a joy to use. For the most part, I feel like I have a good level of control over my projects without having to get too low-level. It doesn't feel like I have to organize my code around the engine's architecture, like with Unity or Unreal. Godot is also free and open-source and very lightweight. Support for C# and 3D has been improving, especially with Godot 4.0. And it has pretty good cross-platform support.

I recently read "Enabling tabular deep learning when d ≫ n with an auxiliary knowledge graph" (https://arxiv.org/pdf/2306.04766.pdf) for one of my graduate classes. Essentially, when there are significantly more data points than features (n >> d), machine learning usually works fine (assuming data quality, an underlying relationship, etc.). But, for sparse datasets where there are fewer data points than features (d >> n), most machine learning methods fail. There's just not enough data to learn all the relationships. This paper builds a knowledge graph based on relationships and other pre-existing knowledge of data features to improve model performance in this case. It's really interesting - I hadn't realized there were ways to get better performance in this case.

My thoughts exactly. The author seems to think a proprietary period is the best option. But honestly, I'd rather have data be immediately openly available and ensure we credit those who played an important role in identifying what observations should be made and obtaining the data. That really seems like the best of both worlds. Promoting open access to data and collaboration among scientists is better than promoting closed access to data and "scooping" of results/limited collaboration.

Writing blog posts has been in my back-of-the-mind TO-DO list for a while now. I just never seem to have time for it. Or rather, there are a million things I end up prioritizing over blogging. One of these days, when I'm less busy, I'll start writing some blog posts. I definitely have some ideas for posts that I think could be interesting.

But reading papers is fun and cool. You get to learn new things. If you read recent papers, you learn things that aren't just new to you but new to the entire world; you can literally learn cutting-edge things that were discovered recently. And if you read older papers, you can better understand why the world is the way it is right now.

Putting TC aside (don't most programmers make plenty anyways?), isn't it just cool to learn new and cutting-edge things? Personally, I think so. Curiosity is central to what it means to be a hacker.

Very interesting paper! It's cool to see that Fortran is still evolving. I've been considering learning it (when I have the time) because of its use for numerical computations and parallel processing. I feel like there's been a big push towards general-purpose languages like C++. However, I'd love to get a better feel for what it's like to program in a language specifically designed for your use case. The closest I've experienced is MatLab, but I feel like MatLab is designed more for scientists and engineers who need to perform some calculations but don't want to actually learn how to program, not for professional programmers working on scientific computation.

Honestly, I don't know why Microsoft thought this was a good idea in the first place. Casual users don't know the difference between browsers and will just use the default anyways. But power users are going to use our preferred browser one way or the other. If it's difficult to set the default browser on Windows 11, then that's just one more reason we'll move to a better operating system.

That was a great article! Pipes can definitely be very powerful. I will say, though, that I often find myself reading pages of documentation in order to actually get anything with Unix and its many commands.

Thanks for sharing! I'll have to give that a read sometime. It looks quite interesting.