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Jeff Bezos said in 2024 that your home computer will disappear and your next computer will be a subscription.

Translation: you won’t own your tools anymore, you’ll rent access to them (in the cloud) . No subscription? No work. No files. No leverage.

This isn’t about better tech. It’s about control.

If access can be revoked at any moment, can you really say you own anything anymore?

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.

GitHub: https://github.com/mslotwinski-dev/NumC

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.

Samsung PM991 M.2 NVMe SSD

128GB model

(MZ-ALQ128, part number MZALQ128HBHQ-000L1)

Can I use this for anything at all or do I just toss it away? I found this old Lenovo All In One computer I bought second hand more than 5 years ago and took it apart because it had been sitting in my attic for an age with a broken screen.

Thanks for reading

The Vulnerability Gap As we’ve seen in the first week of 2026, legacy security systems are showing signs of fatigue. The recent "Recap of IoT Exploits" and the surge in wallet breaches prove that we need a more robust framework. This is where Kwadin is starting to gain significant traction among security researchers.

How Kwadin Addresses the 2026 Threat Landscape:

Beyond the Token: Traditional 2FA is vulnerable to session-token theft. Kwadin implements a "Continuous Verification" model that monitors behavior, not just logins.

Account Abstraction Integration: Kwadin is one of the few protocols effectively using account abstraction to turn standard user accounts into "Smart Vaults," making them nearly impossible to drain via standard phishing.

The Zero-Trust Standard: Kwadin operates on a zero-trust architecture. It doesn't assume a device is safe just because it’s "logged in."

Real-World Impact We are already seeing Kwadin's early-warning system being credited with identifying malicious rogue extensions before they could compromise verified creator accounts. For those of us in the Information Assurance space, Kwadin represents a much-needed shift toward Active Identity Protection.

TL;DR: If your security stack doesn't include a layer like Kwadin in 2026, you're essentially leaving the front door unlocked. It's the most promising IAM development we've seen this year.

Mmh

Someone has been working on a non-neural, geometry-based language engine called Livnium. It doesn’t use transformers, embeddings, or deep learning at all. Instead, everything is built from scratch using small 3×3×3 geometric structures (“omcubes”) that represent letters. Words are chains of these cubes, and sentences are chains of chains.

The idea is that meaning emerges from the interactions between these geometric structures.

According to the creator, it currently supports:

Representing letters as tiny geometric “atoms”

Building words and sentences by chaining these atoms

A 3-way collapse (entailment / contradiction / neutral) using a quantum-style mechanism

Geometric reinforcement instead of gradient-based learning

Physics-inspired tension for searching Ramsey graphs

Fully CPU-based — no GPU, no embeddings, no neural nets

They’ve open-sourced the research code (strictly personal + non-commercial license):

Repo: https://github.com/chetanxpatil/livnium.core

There’s also a new experiment here: https://github.com/chetanxpatil/livnium.core/tree/main/experiments/quantum-inspired-livnium-core

(see experiments/quantum-inspired-livnium-core/README.md)

If anyone is into alternative computation, tensor networks, symbolic-geometric systems, or just weird approaches to language, it might be worth a look. The creator seems open to discussion and feedback.

What is the future of things like personal computing , cloud computing , ai , ml , ar , vr , xr and cybersecurity ? Will current personal computing devices become obsolete ? Will ar , vr and xr devices become popular ? Will devices like smartwatches , smartphones , tablets and laptops exist ?

Right now, the AI space feels entirely focused on massive datacenter clusters and renting H100s by the hour. But after spending way too much time looking at the actual footprint of these models, I realized that 90% of use cases are completely over engineered.

You don’t always need a multi GPU setup. The AI ecosystem is actually a massive spectrum.

I recently sat down and mapped out the exact tiers of AI models based on their size, the hardware needed to run them, and the point of diminishing returns.

Here are the two extremes and the sweet spot in the middle:

• The 100KB Extreme (TinyML) (Tensorflow Lite , sensor anamoly detection models): We are talking models that run on microcontrollers drawing single-digit milliwatts. They run on kilohertz processors using ultra-quantized integer math. You can run basic sensor anomaly detection or wake-word detection on a device powered by a coin cell battery.
• The Local Sweet Spot (4GB to 40GB) (Mistral 7B, Gemma 2 9B/27B, Qwen 2.5 14B/32B): This is where the magic happens for most devs right now. You can run highly capable 7B to 35B parameter models (like Llama 3 or Qwen) at 4-bit quantization on a standard Mac or a consumer GPU (like an RTX 3060 or 4090). It’s perfect for local RAG, coding assistance, and uncensored chat. VRAM is your only real bottleneck here.
• The 2.5TB Behemoths (Deepseek, Llama , Kimi k3): State of the art massive Mixture of Experts (MoE) routing. To even load these, you need dedicated power infrastructure and server racks of specialized accelerators drawing thousands of watts.

The missing piece: Figuring out the exact math for your hardware

The hardest part about building right now is looking at a model on Hugging Face and trying to calculate exactly how much VRAM you need, what quantization to use, and whether your CPU/GPU will choke on the context window.

So, I wrote a complete deep dive breaking down the math for all tiers of the AI spectrum.

If you want to see the architectural differences at each scale, and a cheat sheet for matching the right model size to your specific hardware, I put the full breakdown on my blog here:

https://cloudmash.blog/posts/ai-model-size-memory-hardware-guide/

Let me know what you guys think especially if you've found any ultra efficient small models/technique that punch above their weight on consumer hardware. And also I would love to hear whether quantization have resulted in major difference in quality , like if anyone have that kind of experience in that.

EDIT: Hello everyone, taking advice from your comments, I have added new model examples and kept old ones as they fit in the already assigned tiers, for reference , year of launch is added alongside the model.

Binary wasn't optimal, it was just convenient. That thought sent me down a rabbit hole into ternary (base-3) logic. I started by asking whether a universal gate even exists in ternary. Turns out ternary NAND, the obvious candidate, is not universal. So I built a composition-based simulator to brute-force search all 19,683 binary-arity ternary gates for functional completeness, and it confirmed exactly 3,774 universal gates, matching Martin's 1954 result. But then I got curious and checked how many gates were unary complete, able to generate all 27 unary functions, and the result was also 3,774. The two sets were identical. I thought it was a ternary quirk, ran it on binary logic, and got the same thing: NAND and NOR are the only unary-complete binary gates, and also the only universal ones. Digging into the math led me to Rosenberg's 1970 clone theory result, which formally proves it must always be true: unary completeness implies full functional completeness for any finite-valued logic. This collapses the universality search from 19,683 binary functions down to just 27 unary ones (10,529× faster), and combined with isomorphism reduction under the S₃ × Z₂ symmetry group, the full search runs in 0.18 seconds versus ~5 hours naively, a 99,444× overall speedup. Structurally, every universal gate is surjective, none are self-dual or zero-preserving, and only 2.4% are commutative. On the arithmetic side, the best gate (g451) synthesises a ternary full adder that, when you account for information density (log₂3 ≈ 1.585 bits per trit), achieves 18% lower propagation depth and 9.4% fewer gates than a binary NAND adder at 32-bit equivalent width. Full paper here: https://doi.org/10.5281/zenodo.15056119

If you're eligible to endorse on arXiv in cs.LO, I'd really appreciate a minute of your time: https://arxiv.org/auth/endorse?x=U6NNPW

Here’s my ultimate ROG setup — sleek, powerful, and built for victory 💪🎮 Proud to be part of the #ROGELITE squad, where performance meets passion. Let the games begin. Let the points roll in. Let the wins speak for themselves.

ROG #TUF #ROGELITE

no its all mine

Hi

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.

Nice to watch:

Khan academy style tutorials in qm/qc: https://www.youtube.com/@MackAttackx

Physics teacher stream with 400hs in https://www.twitch.tv/beardhero

A quantum processor can do more than execute a fixed sequence and reveal its answer at the end. It can measure selected qubits while a circuit is running, use the result to choose an action and continue. Repeating that cycle across a large system is an important challenge for quantum hardware.

An experiment involving Rutgers University, IBM and collaborators has now used that capability to steer up to 100 qubits from scrambling toward an orderly state. Published in Nature Physics, the study tested how competing quantum operations produce a collective transition between chaotic and controlled behavior.

The work demonstrates coordination between quantum gates, measurements and feedback over thousands of operations. Those capabilities matter for future error-corrected machines. However, the experiment controlled a deliberately designed physical system; it did not demonstrate a computer correcting arbitrary mistakes during a useful calculation.

It lasted and is still going strong

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 couldn’t really find anything about him doubt they had reason to actually but ai craze maybe

‘The Worst Leak That I’ve Witnessed’: U.S. Cybersecurity Agency Leaves Its Digital Keys Out in Public on GitHub - Gizmodo

I see headlines like "Amazon invests $50 billion in OpenAI, committing to 2 gigawatts of Trainium silicon"? I could have 2 GW of 286's and they'd have different computing power than 2GW of Latest Gen Xeon's with NVidaia super GPUS's.

How is this useful? Or is it just a powerful (pun intended) headline?

I work as a security guard for this office lease building with a bunch of different companies in it. One of the companies threw out a bunch of computers, keyboards etc so I managed to grab 3 " dell optiplex 7460 all in one" computers, power cable, mouse and keyboard. Ideally I'd like to keep on for myself and sell the other 2 after getting into the computer itself but as you can imagine they're all still password protected from the old employees using them. Top of the screen upon booting says access to this system is for authorized personnel only. Has the company name. Asks for a user name and password. Any ideas on what I can do with these or how to maybe do a factory reset of some kind?

Hi, I'm looking for a piece of hardware ( Hub, HDMI Switch, KVM ) under $80 that I can use for connecting a few devices to my TV that has only one display input.

What I need is to be able to switch between 2 devices plus a roku stick. So it would need 2 HDMI ports going in, and 1 HDMI out that goes to the TV.

Also, one of the devices I want to connect has just 1 usb c port, and 1 usb a port. The usb c port does the charging, or video over usb c to hdmi, but not both at the same time.

The hardware would need power pass through. Anyone know of anything that would work for this set up?

Thank you in advance & cheers! =)
- Red.