azeirah

Luckily I work with laravel and that's a spiritual successor to rails.

The development experience is almost always really smooth and there are more and more tools to further smoothen that experience every day.

There are definitely better tools out there but given how the web ecosystem functions, it could be much worse.

I've been watching weird explorer for years and I had never considered I didn't know his name. I had _no_ idea he's caled Jared Rydelek!

His videos are incredibly interesting and fascinating

Given the popularity and activity and pace of innovation seen on /r/LocalLLaMa, I do think models will keep improving. Likely not at the same pace as they are today, but those people love tinkering but it's mostly enthusiasts with a budget for a fancy setup in a garage, independent researchers and smaller businesses doing research there.

These people won't sit still and models will keep getting better as well as cheaper to run.

I don't understand what the difference between a simulation and a test is?

Your url doesn't work, I can't read the article

Exploring the site, the about page and the related links made me quite confident this isn't just vibe coded with claude.

It seems like a passion project and a niche interest by the author.

The default search is not great and the syntax of the dataview plugin is not amazingly well designed. Even the author of dataview admitted to that.

The author started working on a new dataview-like plugin called datacore, but that project is stalled afaik

For learning with self-study it has been amazing.

Sometimes awful is the best we have, we don't have anything that performs at a similar level to EUV machines by ASML but are much simpler or tenable than what we have right now, right?

Perhaps we will find something better in the future, but for now awful is the best we got for the cutting edge.

Also, when is cutting edge not the worst it's ever been?

I often have the issue that AI recommendation algorithms give me more of the same. I already know what I like.

I've found that when I read books in genres that I'd never thought of reading (for instance, biography of a writer in 1800 France) that were actually much more enriching and valuable additions to my bookcase.

I already know where to find the books that I know I will like for the most part.

At the last place I worked at, which was just a small startup with 5 developers, I calculated that a server workstation in the office would be both cheaper and more performant than renting a similar machine in the cloud.

Bare metal makes such a big difference for test and CI scenarios. It even has an integrated a GPU to speed up webdev tests. Good luck finding an affordable machine in the cloud that has a proper GPU for this kind of a use-case

Humans are known for their exceptional sensitivity in their hands and fingers. There are only few animals that come close to our ability to manipulate objects.

Only octopuses, elephants and apes are in a similar league with regards to dexterity and finesse.

I'm active in the /r/localllama community and on the llama.cpp GitHub. For this use-case you absolutely do not need a big LLM. Even an 8B model will suffice, smaller models perform extremely well when the task is very clear and you provide a few shot prompt.

I've experimented in the past with running an LLM like this on a CPU-only VPS, and that actually just works.

If you host it on a server with a single GPU, you'll likely be able to easily fulfil all generation tasks for all customers. What many people don't know about inference is that it's _heavily_ memory bottlenecked, meaning that there is a lot of spare compute left over. What this means in practice is that even on a single GPU, you can serve many parallel chats at once. Think 10 "threads" of inference at 20 Tok/s.

Not only that, but there are also LLMs trained only on commons data.

This was a wonderful read.

It's a bit sad to see that the book he's referring to at the end is a book released in 2003, to which this is the foreword.

Lisp is an incredible experience to write and experiment in, unlike any other language I've ever worked in.

Still, I believe there are many people out in the field that are mostly about "just delivering the spec" or about making money; pragmatists who care not too much about the art behind the software, and I suspect that will always remain the case.

Craftmanship marrying art and mastery is a niche, but that doesn't mean it's not thriving in spaces for enthusiasts.

Really? Tamagotchis seem to be one of those things that have charm beyond straight up nostalgia :o

Learning is strongly related to spaced repetition.

This is often associated with learning tools like anki and stuff, but the real world is all about encountering things at certain frequencies (day night cycles, seasons, places you visit, people you see.... everything, really)

I'm wondering if there maybe some sort of inverse to SR, maybe?

I dunno, as an engineer who likes to make side projects, I can say with high certainty that LLMs have helped me compensate for things I'm worse at when coding a product.

I'm good at the engineering side of things, I'm good at UI, I'm good at UX, I'm good at css, I'm just not good at design.

So I tell the LLM to do it for me. It works incredibly well.

I don't know if it's a net increase in productivity for me, but I am absolutely certain that it is a net increase in my ability to ship a more complete product.

This one frustrates me so, so much.

My personal recommendation is and remains "E-Myth".

adhd

That is not what this is about. It's about being treated as non-technical because you happen to be a woman in tech.

The reality of being a woman in tech comes with serious problems, and how we're treated is one of the biggest contributors to those problems.

True solution to the problem stated in the article? They're talking about fine-grained control over model outputs, how would multimodality help there?

Are there differences between what non-profits are allowed to do in Germany vs the US?

I'm assuming that German has stricter rules, but I wouldn't know.

Any experiment you perform on yourself has N=1. Self-science isn't as robust as gold standard double blind blablabla PhD trials, but come on.

How else are you going to find out whether a particular diet or medication works for you specifically? It's ALWAYS N=1.

Microsoft did this iirc.

Do interaction nets have any interesting properties that make them useful for distributed computing? Think IoT, edge computing, embedded, that kind of thing?

And synchronization primitives?

I wanted to also say I loved reading the documentation. Your idea of places, spaces and values feels like a waaay more intuitive naming scheme for than what's common in CS.

The time-travel example also felt oddly intuitive to me. I don't really care that it uses black magic underneath, it's quite elegant!

Haven't run into that yet, but can imagine that it gets worse if your devenv is large.

I'm sure it's something that can be engineered away and optimized in the Nix core though. Because yeah it isn't a hyperspecialized tool for specifically git hooks alone written in Rust. No.

It's not a saw nor a hammer. It's an entire workshop.

Flakes are less of a workshop, but more like a bus with tools or a heavy-duty toolbox.

I don't know if this is well-known or not, but nix flakes are _amazing_ for managing and configuring cross-platform hooks.

In less than 40 lines of nix, most of which is boilerplate, you have fully cross-platform automatically installed and declaratively managed hooks, as part of your repo.

Need to run it in CI too? Well, no problem! Nix runs equally well in CI as it does locally.

Check this flake for example:

https://github.com/Azeirah/remarks/blob/main/flake.nix

See line 62 in the shell script.

This flake:

1. Manages ALL my dev env dependencies. Including even the specific version of bash that the script is running on, also the specific python dependencies down to the bit.

2. It's (posix-compliant) cross-platform, so it runs on MacOS, WSL, Linux, NixOS, ARM, x86 etc. Also docker.

3. It uses no specialized tools other than Nix, which is now a 20 year old Linux project which is quickly gaining even more traction. Nix is a programming language for reproducible, reliable and declarative dependency management (think docker but with a pure and functional programming language, rather than a recipe with installation instructions)

4. It also creates binaries as well as docker images from the binary. The docker image is like 2 lines of extra code.

Highly highly recommended. It's a bit difficult to wrap your head around initially, but holy damn if all dependency management problems don't just magically disappear forever once you learn Nix.

And I mean ALL of them. Whether it's Linux packages, docker containers, development environments, CI, virtual machines, containers, compilers, C headers from a 1970 Bell Labs project. No matter the architecture or OS (except Windows! :D).

Oh, did I mention it has a lockfile? So you can rollback or upgrade piecewise? Whenever you want?

It's like a programmable version of the superset of npm, cargo, pip, apt, brew, composer, gem, make, cmake, docker, git, Jenkins and even Linux itself (if you dare go the way of NixOS)

It becomes a problem when precision errors accumulate in a system, right?

The issue isn't so much that a single calculation is slightly off, it's that many calculations together will be off by a lot at the end.

Is this stupid or..?

AI is genuinely just a rebranding of "algorithms". Like I get it, it's faster and it works by feeding data instead of feeding code... but code is data, data is code etcetc...

Jim Keller said it best, about every 10 orders of magnitude in available computation the paradigm of computing shifts to a higher level of mathematical building blocks. In the beginning we had pure logic. Then came addition and subtraction, then came vectors, then came matrices, and now we're at tensors, he believes the next building block is graphs.

This is citing from memory while I'm sleep-deprived, but I think the general idea holds. Approximately every 10x increase in computation, there's a paradigm shift in what is possible.

But it's just algorithms. Always has been, and still is.