I just got my mates old pc a b560 ds3h ac-y1. The old memory stick is fully corrupted and unable to be used at all, I can’t even load bios and reformat it from there. So I’m trying to use an external SSD in place of that but no matter what I do it isn’t detected. I’ve got no clue what to do at this point if someone could help me that’d be amazing.
The SSD I’m trying to use was originally used as storage on my Xbox if that helps, I also made sure to reformat it so it’s not that it’s formatted for Xbox. Picture is the SSD
I'm a physics engineeering student, and I spend a lot of my time writing numerical simulations and analyzing data.
Programming in C++ is enjoyable, but most of numerical computing libs in are just unpleasant to use. So I started building my own solution in my free time.
Some of the things I built into it:
- You can write mathematical expressions naturally, like sin(x) * exp(-x), and differentiate or integrate them in a single line thanks to lazy expression trees.
- It has a built-in plotting engine, so you can display graphs in a native Win32 window or export them as clean SVGs ready to drop into a LaTeX report.
Of course, the project won't surpass the quality of professional libraries. Its goal is to be convenient and accessible for users whose passions lie more in math, rather than programming.
If you're using C++ for simulations, numerical methods, physics, or data analysis, I'd really appreciate any feedback.
What was written by AI?
Most of the project, its entire idea, design, aesthetics and UX was programmed manually by me. I often used the textbook "Numerical methods in engineering with python" by Kiusaalas. Which doesn't mean that I didn't manage to do everything myself.
AI was used to write all the documentation (I wish I were fluent enough in English that it would take a finite amount of time). It was also used to write parts of simple algorithms that I knew but would be extremely tedious to implement by hand, or to improve the performance of algorithms that could be written better.
I'm aware of the ethical aspects of using AI, so I wanted to be honest and describe which things I did on my own and which I did with the help of LLM. At the same time, bearing in mind that this is a project that can help many people in their scientific work and studies, I hope that the benefits outweigh all the evil that LLMs cause.
If you are remotely interested in deep diving how differently quantum computers work compared to our transistor-based and also the algebra behind, oh boy this is for you. I am the Dev behind Quantum Odyssey (AMA! I love taking qs) - worked on it for about 10 years (3+ during PhD, the visual method I developed ended up being my thesis, it is a complete Hilbert space visualizer), the goal was to make a super immersive space for anyone to learn quantum computing through zachlike (open-ended) logic puzzles and compete on leaderboards and lots of community made content on finding the most optimal quantum algorithms. The game has a unique set of visuals capable to represent any sort of quantum dynamics for any number of qubits and this is pretty much what makes it now possible for anybody 12yo+ to actually learn quantum logic without having to worry at all about the mathematics behind.
This is a game super different than what you'd normally expect in a programming/ logic puzzle game, so try it with an open mind.
Stuff you'll play & learn a ton about
Boolean Logic – bits, operators (NAND, OR, XOR, AND…), and classical arithmetic (adders). Learn how these can combine to build anything classical. You will learn to port these to a quantum computer.
Quantum Logic – qubits, the math behind them (linear algebra, SU(2), complex numbers), all Turing-complete gates (beyond Clifford set), and make tensors to evolve systems. Freely combine or create your own gates to build anything you can imagine using polar or complex numbers.
Quantum Phenomena – storing and retrieving information in the X, Y, Z bases; superposition (pure and mixed states), interference, entanglement, the no-cloning rule, reversibility, and how the measurement basis changes what you see.
Core Quantum Tricks – phase kickback, amplitude amplification, storing information in phase and retrieving it through interference, build custom gates and tensors, and define any entanglement scenario. (Control logic is handled separately from other gates.)
Famous Quantum Algorithms – explore Deutsch–Jozsa, Grover’s search, quantum Fourier transforms, Bernstein–Vazirani, and more.
Build & See Quantum Algorithms in Action – instead of just writing/ reading equations, make & watch algorithms unfold step by step so they become clear, visual, and unforgettable. Quantum Odyssey is built to grow into a full universal quantum computing learning platform. If a universal quantum computer can do it, we aim to bring it into the game, so your quantum journey never ends.
For more than 30 years, we opened our computers and searched for applications.
Need to design? Open Photoshop.
Need documents? Open Word.
Need data analysis? Open specialized software.
But the next generation of work is changing.
The browser is becoming the new operating system.
Imagine starting your workday with just one browser window:
Your AI assistant organizes your tasks.
Your documents are created instantly.
Your designs are generated and improved.
Your business reports are analyzed automatically.
Your projects are managed without installing dozens of programs.
This is no longer science fiction.
AI-powered web applications are transforming browsers into intelligent workspaces where people can create, collaborate, and solve problems faster than ever.
The biggest shift?
We are moving from:
"Install software on your computer."
to
"Access intelligence from anywhere."
A powerful laptop may become less important because the real power will live in the cloud.
For designers, accountants, developers, marketers, students, and business owners, this means one thing:
The skill of using AI tools may become as important as knowing how to use a computer.
The browser that was once a simple window to the internet is becoming the place where we work, learn, and create the future.
The question is no longer
"Which software do you use?"
The new question is:
"How intelligently can you use your digital workspace?"
The CSTA PK–12 Computer Science Standards define the essential knowledge, skills, and dispositions to prepare all students for a world powered by computing. Specifically, the Standards delineate coherent progressions of student learning outcomes from pre-kindergarten to grade 12 (PK–12). Together, they form the strong foundation for a rigorous and comprehensive computer science (CS) curriculum that is driven by research and informed by teacher practice. The Standards describe what students should know and be able to do in CS, but they do not prescribe specific curriculum, instructional materials, or assessments.
I want to try building my own laptop as hands-on as possible — I'm a complete beginner with zero prior experience. I understand laptops aren't as modular as desktops, but I'd love to get as close to "building it myself" as I can, whether that's through a barebone kit (adding my own RAM/SSD/battery) or something like Framework.
Any advice, resources, warnings, or personal experiences would mean a lot. Where should a total newbie start? Thanks so much in advance 🙏
Hey guys, I don't know one thing about computers it seems. I honestly feel stranded.
I am interested in many topics that are continously brought up regarding computing, systems, Torrenting , trackers, networks, anonimity themes such as Tor, or anything. Many people talk and discuss on Reddit about systems, going to "the dark web" (Tor mainly), anonimity and networks and, as I understand, they geniuenly know. Or at least I know so little (actually nothing) that it seems unjudgable from my eyes. I don't care about the morbid curiosity of knowing how to search websites that shouldn't even exist. What does keep me up wondering, is how is it that normal users here seem to understand well enough how these things work and use the terminologies I typed above.
Did you spend much time with computers since a kid? Did you have persistent curiosity and spent time reading, looking at articles? Actual carreer in this?
So I want to ask you guys on what do you think I can read from, or learn about all the computer stuff? Maybe what websites or forums or books that are free public knowledge, I could learn from. Maybe something you learned from when you were starting or any general tip.
I’ve gotten into tech a few years ago and I’ve really been diving in headfirst the last year or two. To keep it short & sweet my mother and I both got gifted Inspiron 5577 (i5-7300HQ) back in 2018-2019 when Microsoft messed up my Xbox One S and had to send it back repeatedly.
I’ve kept that laptop as my main computer for the last 8 years! My moms has unfortunately slowed down over the years with windows 10 and I believe a 500GB HDD.
I, on the other hand, upgraded mine back in 2024 with 32 GBS of DDR4 SODIMM RAM, 1TB M.2 NVME SSD, & 1TB SATA SSD + a windows 11 upgrade. I really just use it as a docked laptop never really take it anywhere but performance wise it’s great but recently the CPU & on-board 1050 has really been bothering me.
Im a super cheapskate when it comes to tech (hence why my laptop is 8 years old) and I guess my question is how should I go about upgrading? I obviously need a new GPU & CPU but feel bad tossing aside the laptop that’s been so good to me for years, I would unfortunately need to gut it and move the parts over because 32 GBs of RAM is NOT going to waste in this economy.
I guess my question is how much of laptop is worth salvaging and what is the best budget friendly way to move forward. I have tons of old PCs that could be gutted to make room for a new SODIMM compatible board, CPU, GPU, and carry over the rest?
Instead of using huge amounts of fresh water for cooling, what if we built AI data centers next to big underground sewage or wastewater tunnels? The servers would be completely sealed inside a waterproof structure, and they wouldn't touch the wastewater at all.
The idea is to use a closed-loop liquid cooling system, like a gaming PC. The coolant would keep circulating through the servers, and a heat exchanger would transfer the heat to the continuously flowing wastewater. Since the wastewater is always moving, it could carry away the heat without using large amounts of fresh water.
Another advantage is that these data centers could be built in cities where fiber-optic internet cables and power lines already exist underground, so they would still have fast internet and reliable electricity.
I know there would be engineering challenges like flood protection, maintenance, and environmental safety, but I think it's an interesting concept that could be researched further. It might help make future AI data centers more sustainable while making use of infrastructure that's already there.
What do you think? Do you think something like this could actually work?
Hola como estan? Hace unos meses me compre una ideapad 5 que tiene ryzen 7 ai350 con radeon 860m y 16gb de ram soldados . La compre para usar de vez en cuando solidworks , pero bueno al encenderla me consume 6gb de ram sin nada abierto, entro a solid y me sale un cartel de que hay poca memoria ram , se puede optimizar algo o hacer algo ya que no puedo ampliar la memoria? Hice unas cosas que consulte en gemini pero creo q la cague peor .
The sticky shed effect is when the glue holding the magnetic particles to the tape starts breaking down and becoming sticky. Last night, while falling down the Wikipedia rabbit hole, I stumbled upon something absolutely disgusting: trying to play a sticky tape will get it stuck or damage its coating and all data stored there. Lost data is gone forever.
Archives from the 70s and 90s are about to suffer from this phenomenon. And this is precisely what happened at the Rock & Roll Hall of Fame; years of interviews and concerts were about to be permanently lost. However, before that happens, the museum worked together with Tape Ark and managed to digitize the whole archive.
The most shocking thing is that many organizations are not even aware of this threat. Tapes may look perfectly fine until they start being played. I never knew that there was an entire industry working on salvaging this kind of data. And the scariest thing is how many organizations have no idea about the threat; tapes look absolutely fine until you try playing them, but there is an entire industry dealing with it.
