Hi everyone, I've always had a special interest in linguistics and have informally studied a couple of different languages from all around the world. The different writing systems such as radicals in Chinese, Kurdish scripts and reading from right to left, to Inuit glyphs are all fascinating in themselves.

The IPA has been something fascinating, but I've yet to find a good resource that I could make sense of and hold my attention long enough to internalize the concepts.

I'm looking for books and authors that have a unique background. For example, seeing Chomsky's name in an automata formal language theory book was weird to think... But all the NLP stuff had foundation in older linguistic theory and ways people thought about the human brain, right?

Language and Information by Zellig Harris is an interesting read. John Sowa is another author I'd recommend for the whole way of ontology and computer systems. The particular book by him that I'm thinking is Conceptual Structures, I believe...? Would love to hear your thoughts, especially with all the AI hardware being released.

Hey everyone,

I've noticed this community is a bit barren. I'd like to add some posts myself but from a more ignorant POV.

It has come to my attention that there is not nearly enough software torture in my life. Hearing how companies are starting to add barriers to so-called "Translation layers" and other software pieces that give functionality to the underlying hardware...

What types of projects does the open source community need to pursue with respect to RISC-V drivers, firmware, or other necessary pieces to somewhat guarantee that the processor is compatible with GPUs, RAM, I/O, and other low level functions that hardware components perform?

Where should I start in my RISC-V journey? Is there a beginner computer or developer kit that I can purchase that won't incinerate my wallet? I don't expect to game or train LLMs off of a RISC-V-based computer, but am curious as to what a fully free hardware setup would consist of.

Love the idea of RISC-V!

Lately, I was going through the blog of a math professor I took at a community college back when I was in high school. Having gone the path I did in life, I took a look at what his credentials were, and found that he completed a computer science degree back sometime in the 1970s. He had a curmudgeonly and standoffish personality, and his IT skills were nonexistent back when I took him.

It's fascinating to see the perspectives on computing and how many of the things I learned in my undergraduate were still being taught way back to the 1950s. It also seems like the computer science degree was more intertwined with its electrical engineering fraternal twin.

Although the title of this post is inherently provocative, I'm curious to hear from those of you who did computer science, electrical engineering, or similar technical degrees in decades past. Are there topics or subjects that have phased out over the years that you think leave younger programmers/engineers ill-equipped in the modern day? What common practices were you happy to see thrown in the dumpster and kicked away forever?

The community also seems like it was significantly smaller back then and more interconnected. Was nepotism as prevalent in the technology industry then as it is today?

This is just the start of a discussion, please feel free to share your thoughts!

Hi Everyone,

I've gotten a lot of older books on mathematics, physics, chemistry, and dabbled a lot in computational simulation with programs like LAMMPS and GROMACS. I am interested in learning how to make a GUI application that I can use to automate graphing functions, understanding how sensitive a model may be to perturbations in particular parameters, and different ways of visualizing data to help me get an "intuition" on subjects. Numpy, SciPy, Pandas, Matplotlib.pyplot, numba, glob, and os, are libraries I like to use. See Gibbs'/Maxwell's original envisionment of thermodynamic surfaces from the late 1800s.

However, I am a moron with respect to software development. My interests are in the FOSS-sphere of things, but I have never made a piece of software other than botched code to calculate averages, perform PCA, and typical statistics visualizations with distributions, Monte Carlo simulations, and see how this effects the properties of the underlying system of study. I've also glanced at design patterns, know the different paradigms of computing to a basic level, and am willing to suffer for long-term educational gains.

The language I'm most comfortable coding in is Python, but I found it discouraging to start writing a software project, as I assume writing something entirely in Python isn't the best way to ship quality software.

Julia and Matlab are other languages I've written programs in. I've tortured myself with the whole gamut of toolchains/editors like Neovim (and my inability to get my lua.init file to ever work properly), and prefer to use FOSS tools. I can navigate in a clunky sense around a terminal, but whenever I try to configure my .bashrc or modify the behavior of my editor, it results in me chasing down a particular filepath for an hour just to change the color of the text, or rearrange how the text is displayed to the terminal so that I actually can read what is saying. Without color-coating, it's hard to distinguish between directories, file extensions, and so forth, and even more frustrating when you can't get the changes to work.

Essentially, I am a confused orangutan given a mallet.

When I ditched Word for LaTeX several years ago, it inspired me to take my FOSS journey one step at a time, rather than what I did a year ago, where I chucked every proprietary tool into the trash.

I need to actually be able to do work, as one would prefer to drive their car, rather than get out every 5 feet to fix another busted part.

I would like to eventually develop software that ends up in the FOSS sphere, and write programs that do not take up 100GB of space, or have 100s of bandaid layers, countless dependencies, and the whole gamut of issues that plague certain software packages.

Libraries that I've looked at are...

1. Tkinter
2. PyQt5
3. Dear PyGui

I don't particularly care about modern esthetics for the interface. All I care is that the program functions, uses a relatively low amount of resources, and can educate me further about how to rip open a widget, modify the code associated with that particular button, and get a greater control of visualizing concepts taught in math/physics books.

Thank you!

Hey everyone,

Recently, I've found myself bogged down in sending off resumes that seem to never to be read by anyone other than myself.

I'll go through the whole gamut of picking keywords that match the job description, showcasing my previous experiences, projects, skills etc... But it just seems to never result in a call-back or even an email to tell me I wasn't selected.

Given that I'm tired of screaming into the hills and hearing it echo back, I want to write a program that streamlines this whole process. I have a couple of resume templates written in TeX script that I can populate with content. Alongside this, I have all of my relevant bullet points in assorted text files labeled appropriately.

The idea would be to feed the program the job description, relevant qualifications, and other miscellaneous text files. These would be processed to give an idea on how my resume should be modified to suit their requirements. Perhaps that could aid in creating a strong resume in a more streamlined fashion. I have no clue what metric should be used to quantify how "good" it is, so that's to be figured out as well.

I saw "nltk" and "spaCy" are two NLP libraries for Python, but I wanted to open up discussion for those of you who have worked on projects similar to this. I have read mixed comments about the two. Which one seems better suited for this task?

Obviously I'll review the resume before I submit it, but I want to see if I could get something like this working.

I'm a giant noob when it comes to NLP, but have used Python for the past couple of years for data-science applications. I'd be open to learning a different language if there is a library that has some of these functions already coded, but I'm not a developer.

Thanks for any help! I love the community over here on Lemmy. Many of you have been very helpful and encouraging and it makes me want to keep learning more :)

Hey everyone,

Last night a rabbit hole took me to an unexpected place. Gwern is undoubtedly the most comprehensive website adorned with vocabulary, statistics, programming, and a strong bias towards the Haskell language.

I found myself sucked into the site and got lost in what seemed like an endless stream of text. One page about nootropics would lead to another about properly designing scientific studies, the "Dual n back" method for increasing IQ and countless more.

From a previous post I mentioned interest in LLM inferences, but at the time I kind of only nebulously wanted an AI tool better than GPT-4. Some of so kindly brought the Georgi Gerganov Llama.cpp to my attention, for which I finally have adjusted to Linux well enough to feel comfortable downloading software in myriad ways.

Returning to the topic at hand, I have an itching feeling that some sort of ML model could be made to serve the purpose as a brain extension. I can see the uses being for picking up and maintaining technical vocabulary for an interview in pharmaceuticals, chip manufacturing, chemical processing, 3D manufacturing, and legion others.

I imagine it could be an absolute super tool for learning. I mean past the usual Ebbinghaus forgetting curve that Anki seeks to ameliorate, combined with active recall, memory palace techniques, and Anthony Metivier's lovely curated channel. He led me to Gwern in the first place. His story is very inspiring and I would recommend his book "The Victorious Mind: How to Master Memory, Meditation and Mental Well-Being".

I think this is a great place to begin discussion on this topic. Given that we are neurodivergent and many of you have also resonated with the monotropic brain theory, what are your thoughts about having a "Brain Inference" or "Brain Buddy"? Here are a couple of questions to chew on:

• What features should a program do that has the Brain Buddy incorporated in it? Could this be analogous to orgmode in Emacs? Some sort of an fzf-esque program to globally search for something you vaguely recall?
• How would we design it? What facets do we need to consider?
• What training sets could we use? How do we clean up the set to ensure the model doesn't digest falsehoods?
• How large do the models need to be w.r.t. parameters?
• How much computing power would we need?

Hey everyone, I've been parsing through the Huggingface website and am having a bit of trouble picking out an LLM inference to help me parse through legal documents. I am not a lawyer, but I would like to understand my rights and how to search for answers to legal questions with concrete answers using an inference.

I have heard a multitude of things around Llama being a privacy nightmare and something about Gerganov ML files? GGMU is also a nebulous term to me and I understand the basics about how a model is trained and validated, but not how to pick one for personal use that isn't GPT-4.

Any suggestions or things to add on to the discussion?

Hey everyone, I've been searching for a bit on getting local LLM inference to process legal paperwork (I am not a lawyer, I just have trouble through large documents to figure out my rights). This would help me have conversations with my landlord and various other people who will withhold crucial information such as your rights during a unit inspection or accuse you of things you did not etc.

Given that there are 1000s of pre-trained models, would it be better to train a small model myself on an RTX 4090 or a Daisy chain of other GPUs? Is there a legal archive somewhere that I'm just not seeing or where should I direct my energy? I think lots of us could benefit from a pocket law reference that can serve as an aid to see what to do next.

After chatting with some of you on this forum and seeing that we all are on Lemmy rather than Reddit, I think it would be a good idea for us to have some study groups to improve our technological literacy and competency.

During my time on Lemmy, I've been able to increase my digital literacy and overall knowledge surrounding my system. I've loved the nearly endless rabbit holes Wikipedia has pulled me into, as well as the resulting happiness that comes from finally fixing a broken Linux system or piece of technology.

But what exactly does technological literacy encompass, one might ask? I'd like to illustrate via anecdote. When I first got into Linux, I was told to "Get a terminal emulator to SSH into the HPC so that you can run computational jobs". To most of you this sentence is completely normal, but to my unconditioned mind, I felt like a big bright light was flashed before my eyes while my PI spoke martian to me. After the initial disorientation, I downloaded what I thought was my only option for a terminal emulator (MobaXTerm), and found myself sitting in front of a pitch black terminal screen with a blinking prompt. Not knowing what a host was, how to manage a network, any Linux commands (coreutil, never heard of her...), or really do anything past opening up WoW and Google Docs. The only things more advanced than the plug and play Google/Microsoft software solutions I'd use, was my botched LaTeX setup. I used it to typeset math equations for my students, homework, and lab reports from how much faster I could type in the TeX format than click on every Greek letter/symbol I needed. Overall, it really messed with my ability to do the research I was tasked to do. I was supposed to learn how to use Vim as my IDE when the only IDE I had ever worked in was Spyder from Anaconda! VSCodium, CodeBlocks, Emacs, etc, I did not know that any of these existed.

Needless to say, this was extremely discouraging to be thrown head first into a difficult scenario with very little assistance whilst trying to juggle coursework and outside responsibilities. Humble beginnings reinforced in me that if I experimented with my computer and messed up on the OS side, that I'd brick my hardware and have some variation of Homer Simpson holding up the "So you Broke the Family Computer" book.

I'm sure that we all come from varying origins of computer literacy, which IthinkI've proposed a couple of possible areas of study, that we could set up in small or large groups depending on interest. The frequency, literature references (textbooks, white papers, blogs, forums, etc.), and the project goal (could be concrete or abstract) should be drawn up and worked towards to keep the topic focused. I've come up with a couple of fields for us to start with, feel free to add to the list or modify what I've written.

1. Cryptography with a rigorous mathematical foundation applied to both classical and quantum computing paradigms (AES, RSA, Hash functions deeper than just the surface, information theory (We love our boy Claude Shannon), Cryptographic primitives, Shor's Algorithm, etc.)
2. A hardware agnostic study of firmware (What are some unifying principles about firmware that can empower the user to understand why certain aspects of the device are not functioning)
3. Hardware architectures (GPU, NPU, TPU, CPU, RAM, DIMM)
4. Form factors (How geometry can impose certain design decisions, and so forth
5. Fundamentals from First Principles, i.e condensed matter physics theories to understand the classical computing systems. The group can also choose to segwey into topological states of matter (Dirac fermions, Weyl semimetals, Mott insulators, and a myriad of other cool matter states that aren't really discussed outside of physics / graduate engineering classes) Qubits (Bloch sphere representations) and loads of other things that I'm sure exist but am unaware of.
6. LLM Inference technology and how it can be applied to case law, accounting, stocks, and various other fields where the solution to the problem lay somewhere in an encoded technical language.

I'd like to begin the discussion with this as our starting framework, does anyone have any interest in the topics listed above or suggestions for other subjects? How should we manage these groups? Should we add some chats to the Matrix instance?

Hey everyone, I've been using an Arch system for the past 2 months and I've had an absolute ball learning more granular details about my hardware. Never in my life did I think I'd be looking at kernel modules and contemplating swapping out init systems and trying different kernels.

I write this post somewhat open-endesd because I'm clueless as to where Gentoo can take me in terms of hardware acceleration and help me learn development of software/firmware deeper. To my understanding, everything must be compiled from source code, which I'd like to learn more about as well. Ive dipped my toe in a myriad of programming languages too and found a more terminals focused work flow was ideal.

My major use cases for my computer are to benchmark hardware and pick more features in software that I'd have to compile from source anyways. I do work in molecular dynamics from time to time and have used software distributions like GROMACS and LAMMPS. Any advice that could be wouldbbe greatly appreciated. The thoroughness in the Gentoo Wiki's documentation is amazing, but its a overwhelming lol

My foundation is a little like swiss cheese though. Don't know what I don't know! Looking forward to becoming a part of the Gentoo community. I'd love to develop software like DWSIM someday.

Hey everyone, I'm still pretty new to using my GrapheneOS phone and have been slowly transitioning to a more privacy oriented technology lineup than I previously did.

I searched for clients on Google and found "Total Adblock", "Adblock", and "Adblock Plus" but I'm not quite sure how to audit an adblocker for security flaws or malicious intent. I also would prefer to install apps through the F-Droid store and learn how to compile from source code on mobile (if that's possible on GrapheneOS or if that's even something desirable)

Thanks for any help! Been lurking a lot on Lemmy and have really enjoyed the energy in the community. Definitely has made learning Linux and the countless times I've had to fix my Arch system much more enjoyable. GrapheneOS has been quite stable too other than the phone having interfacing problems with my cellular provider's network...

This week I finished setting up Arch Linux (It felt so good to nuke Windows 11 off my laptop!) and GrapheneOS for my new Pixel phone.

I am interested in getting a NAS for multiple purposes such as accessing files, hosting a small website, and to upload security camera footage to name a few.

Is there a particular brand to buy? I'm basically illiterate when it comes to networks aside from what an IP is and what DNS is. Any suggestions for books and reading material is greatly appreciated. It feels liberating to know more than I did before with tech!

Hey everyone! So I've been doing some playing around with Mint Linux and have quite enjoyed it in the virtual machine. Thank all of you for the insight into the mindset I should take when approaching a new distribution.

Now that I'm not struggling as much with the terminal and other general computer organizational problems, I wanted to learn how to train my own chat-bot assistants. These assistants would be trained on monographs, textbooks, and other scholarly resources on topics I've been trying to learn more deeply.

I was wondering if anyone here has done this before, and if you have any advice to lend me!

Thanks for all the help!

I have tried to learn Linux for ages, and have experimented with installing Arch and Ubuntu. Usually something goes wrong when I try to set up a desktop environment after installing Arch in VirtualBox. KDE gave me a problem where I couldn't log in after getting to the point where my username was displayed in a similar format to how it is for Windows. My end use case is to help keep my workflow more organized than haphazardly throwing files somewhere on my desktop or in a folder nested somewhere that I'll just inevitably lose :(

Somehow after all this time, I feel like I actually understand less about my computer and what I need to understand regarding its facets. Is it an unrealistic goal to want to eventually run a computer with coreboot and a more cybersecurity heavy emphasis? I'm still a noob at this and any advice would be appreciated!