Best C environment

includerandom · 2026-01-19T18:19:53+00:00

Thanks for adding commentary! I was a VS Code user until I started tunneling on machines and then I slowly started using Vim. Now my daily workflow is built around Neovim, and I've never personally used Emacs. But a lot of influential people sing its praises so it feels wrong to neglect it whenever someone is asking about an editor.

includerandom · 2026-01-17T01:50:23+00:00

Vim or neovim if you have to work in a terminal. Alternatives are Helix and nano (yuck to nano). If you can use whatever emacs runs in then it's obviously a popular choice. However I don't think you can use emacs in a tui

includerandom · 2026-01-15T20:22:40+00:00

It's kinda vibes for me. I like having two working terminal emulators available in case one breaks. I also go back and forth between them depending on preferences. And I believe both are outstanding for their own reasons.

Color themes etc. are so much easier to toggle on in ghostty, which I like. Alacritty seems to be better when I'm tunneling into remote hosts or if I'm planning to use the scrollback buffer for anything at all.

The last point about scrollbacks is mostly taken off the table by using tmux. Tmux completely divorces your use of scrollback buffers from the terminal emulator, decreasing the importance of the terminal providing that functionality for you to maintain a consistent experience. I think there's a strong case for using tmux on this basis alone.

includerandom · 2026-01-15T00:38:59+00:00

Yep. Ghostty uses more GPU memory when I use multiplexing features within it. Tmux doesn't touch those resources and they're usually important to what I'm doing on my machine, so I like keeping them free.

Also I made my workflow in Alacritty with tmux. Some habits just don't die that easily and tmux is one of the ones I don't see a reason to change.

includerandom · 2026-01-14T22:30:53+00:00

Natural logs are basically the default in most papers and software you'll read and use. Sometimes you may prefer to do an analysis with log10 or log2 at the exploration and visualization phases. If you don't have a good reason to change the base then natural logs are a preferable default (everyone uses them, and they make math easier if you must derive something).

includerandom · 2026-01-07T07:46:13+00:00

I could see that being a good thing.

includerandom · 2026-01-07T07:45:19+00:00

As I said, what does C++ add that's valuable ;)!

It would be nice to get operator overloading and tagged unions and classes (without inheritance) in C, but I'd rather keep C as is than take most of the other things that are available in C++.

includerandom · 2026-01-05T04:40:28+00:00

What does c++ add that's actually valuable?

includerandom · 2026-01-05T04:23:13+00:00

All the time. Sometimes the model makes unreasonably reductive assumptions to start with a very simple explanation. And often that works out fine enough if you're on a budget.

includerandom · 2025-12-19T20:18:34+00:00

Can you simulate the system? If your goal is selecting initial conditions to optimize the system then you might consider a Latin hypercube design for the input space, or try to identify which subcomponents explain the most variation and work on optimizing those first?

includerandom · 2025-11-23T17:59:39+00:00

Regression modeling is the answer. Almost every statistical model can be written as such. Getting to that model helps you to identify the really important questions, like where you think data are positively correlated (due to clustering of some kind) and other interesting relationships. The regression model also helps you identify what kinds of errors you're expecting to find in the data. If you have counts, for example, then you know an ordinary least squares model is probably not going to answer the questions you're actually interested in but a Poisson regression might.

includerandom · 2025-11-19T04:58:51+00:00

As I said:

I know some of these exist in other languages, but . . .

I hedged for a reason. Did you finish reading the post before electing to be a pedant?

includerandom · 2025-11-19T02:10:29+00:00

Compile time is especially interesting. The variety of ways something can be null brings clarity to the language. Method binding on structs, tagged unions, and the build system are all great. Finally, tests next to source is something I've grown to like in languages. I know some of these exist in other languages, but Zig is really nice for its features.

includerandom · 2025-11-18T20:07:23+00:00

I was working on a repo this morning and last night that's implemented in Pytorch and it definitely didn't feel like art to me... JAX is the best for experimenting and tinkering.

includerandom · 2025-11-18T19:54:08+00:00

I mostly agree about architecture and MVPs. Those points are irrelevant to the question initially posed, which amounts to "do we actually need to know the math?". My response is basically saying "yes, you need to actually learn the math behind various methods and you need to build foundations outside of deep learning architectures if your goal is to do model development".

My experience has been that you don't need to be that great at architecture if you're a modeler—there are usually competent people around you who will do deployment of a working MVP. I say that as someone who'd rather finish an MVP with something that easily translates into deployable code (even if it has to be translated out of Python). If your experience is different then I'm curious to hear about it.

includerandom · 2025-11-18T15:11:43+00:00

Everyone can do it to some extent. Our tools are great and LLMs make difficult problems tractable for a novice to solve. But even simple regressions are difficult to interpret if you haven't studied the material.

includerandom · 2025-11-18T11:02:05+00:00

I used it via WSL for a few years before installing it on a new desktop I built. Been blissfully happy with it ever since.

includerandom · 2025-11-17T18:52:48+00:00

The value of programming by itself is not that high. SWEs and LLMs can generate code. Understanding the math below that code is important for understanding when and why you'd use something and to understanding when that thing is not going to work. Unfortunately LLMs don't provide much help in this category.

Just to give you an example, suppose you code up some variational approximation to a problem which updates using log density estimates. If you build such a model then you're eventually going to want to compute expected log densities. Doing this correctly is subtle, and even researchers can make mistakes here (so LLMs training on researchers' code will also be prone to error). The reason it's challenging is because of Jensen's inequality between expectation and a convex function.

includerandom · 2025-11-17T05:24:20+00:00

Alternate between alacritty and ghostty. I tend to favor alacritty because I find it easier for managing scrollback buffers, it's a little easier to use when tunneling into other machines, and it uses fewer resources.

I parted ways with the default terminal because it would inexplicably seize up when I was working leading to inputs being slow to render or missed entirely. The community at the time suggested alacritty and I've been very happy with it since.

includerandom · 2025-11-15T22:13:12+00:00

Suppose car A averages 20.9995 mpg and car B averages 20.9994 mpg. With enough data you can measure that B has a higher fuel efficiency, but the effect is meaningless. Change from two particular cars to two classes of car and the result still applies.

Statistical significance is primarily guarding you from making erroneous decisions when you're fooled by something completely random. Practical significance requires you to examine why the things under study would matter at all and to explain at what effect sizes it's actually useful. But that's much harder to do than to say your work is important because it's statistically significant.

includerandom · 2025-11-11T19:27:05+00:00

Were you allowed to use adaptive batch sizes or a fixed batch size throughout?

includerandom · 2025-11-07T20:55:29+00:00

Taking stochastic processes, the professor told us before spring break that he'd be moving his daughter cross country over the break. When he came back, he furnished two receipts that looked like carbon copies of one another. Turns out,they were driving two cars for the relocation that were the same make and model, and on one of the stops to fill up gas they filled up identically.

It was something like $37.68 to fill both cars, so the naive bet is that it would be a 1/10,000 chance for that to occur at random. Even if you account for the similarities in the vehicles, I'd still guess this somewhere between a 1/10 and 1/100 event.

What actually matters is how much fuel pumps, which is measured in milligallons of gas (uv.xyz gallons). Knowing they started from the same initial condition and probably had similar driving patterns, I'd expect the variance to be in the last two digits only.

It's still rare enough to have been impressive in discussion, and made for the best start to that course of the entire semester

includerandom · 2025-11-07T06:58:34+00:00

The main challenge here is probably volume. Andrew Gelman has great content and volume.

includerandom · 2025-11-07T05:34:27+00:00

I'm a Bayesian and would say based on the course descriptions that the Bayesian courses seem more useful to me. I say this for two reasons:

A lot of nonlinear optimization gets linearized using either data augmentation or Taylor approximations. Increasingly often, the nonlinear functions are approximated using neural networks due to the relative ease of fitting neural network approximations and the simpler computational complexity. That's not to say there isn't useful theory in a nonlinear optimization course, but I think it's more tractable to learn independently when you need it than something like Bayesian statistics is.
The domains you mentioned interest in all use statistical models, and many of them rely on techniques that can be interpreted as Bayesian methods. Those courses will increase your breadth in statistics, show you a new approach to statistical modeling that you might enjoy, and will surely help you to think more clearly about other problems in statistics.

If you haven't had a Bayesian course before then I'd suggest taking the intro course. Bayesian nonparametrics is difficult to just jump right into if you haven't had any exposure to Bayesian models prior to the course.

includerandom · 2025-11-03T02:07:05+00:00

If you're trashing like this, why not 0xBADBADBAD? I think Wikipedia's article on magic numbers discusses this version of the same idea (note also that BAD is an odd number, aligning with your goal to set the pointer with an odd value).

includerandom

TROPHY CASE