标签：#computing

Zulfi Alam thinks his team doesn’t need to “prove” they engineered a new state of matter in their bid to reinvent computing. Science would disagree.

Running AI agents on Kubernetes raises a key question: should each agent get its own Pod? The kagent project argues no—agents are bursty, short-lived, can spawn subagents, and may wait for human approval, making one Pod per agent wasteful. Agent-substrate adds a control plane to schedule logical “Actors” onto long-lived worker Pods. By Mark Silvester

At a time when storage space commands a premium price, SanDisk's Extreme Fit drive is worth a look.

Samsung's latest memory and storage tech is mostly designed for data centers, not consoles or PCs.

Most Windows 11 users don't need to bother with the Pro version of Microsoft's operating system.

exFAT is best for cross-platform usage.

Not only did MacBook Air prices recently go up, but you couldn't get one today even if you wanted to.

Amazon isn't slowing down on data center spending — but investors don't seem to mind.

If you're looking for a high-performance PC with that clean 'zero-cable' look, Maingear's new pre-built lineup that may suit you.

NVIDIA shipped a 30-billion-parameter model that can see, hear, and talk — and gave the weights away. The catch: the seeing and hearing parts didn’t run on a Mac. So I spent an afternoon writing the missing piece. Here is the whole thing in thirty-five seconds — the model reading a real cart off my own store, on the laptop, with nothing leaving it. Thirty-five seconds: the model reads a real cart off my own store, on the laptop, with nothing leaving it. The elapsed counter is the real measured latency. Every couple of weeks I go looking for whatever new open-weight model just dropped, pull it onto my laptop, and see what it can actually do. Most of the time it’s a coding model, I run it against the one I already use, and my current favorite wins again. That’s a fine result. It’s just not much of a story. This time I found something different. What Nemotron Omni actually is NVIDIA released Nemotron-3-Nano-Omni-30B-A3B — a “tri-modal” model, which is a fancy way of saying one brain with eyes and ears attached. You can hand it a picture, a sound file, or a video, and talk to it about what it saw or heard. It’s 30 billion parameters total, but only about 3 billion of them fire for any given word, which is why something this capable can run on a laptop at all. The weights are public. Someone had already done the hard, unglamorous work of shrinking it down to a 4-bit MLX version that fits on Apple Silicon — that’s yayr over at mlx-community, and this project doesn’t exist without that upload. About 19 GB on disk. Ready to go. Except for one line, buried in the model card: The text backbone loads with standard MLX nemotron_h tooling. The vision and audio towers require a multimodal runtime that implements the C-RADIO ViT-H and Parakeet Conformer forward passes (e.g. the Evorix on-device engine). Translated: the brain works on a Mac. The eyes and ears don’t. The weights for them are right there in the file — all the knowledge, sitting on your disk — but nothing I could find

Modern electrical grids increasingly rely on distributed sensors installed across conductors, towers, poles, substations, and remote line sections. These devices can measure: Conductor temperature Current and voltage Mechanical tension Line sag Vibration Weather conditions Fault passage Switch and recloser states Collecting these measurements is relatively straightforward. Building a reliable data pipeline around them is much harder. Power infrastructure often operates in locations with unstable connectivity, limited bandwidth, and strict requirements for alarm delivery. A useful architecture must therefore do more than move telemetry from sensors to a cloud database. It must determine which data is urgent, validate measurements, preserve event order, survive network outages, and integrate the results with operational utility systems. This article explores how to design that pipeline. The Basic Architecture A practical grid-monitoring data flow may look like this: Field Sensors | v Protocol Adapters | v Edge Data Model | +----> Local Rules and Fault Detection | +----> Local Time-Series Buffer | +----> Event Queue | v Central IoT or Utility Platform | +----> SCADA +----> GIS +----> OMS +----> Analytics +----> Maintenance Systems The edge gateway sits between field equipment and central applications. Its job is not limited to protocol conversion. It also acts as a local data-processing and reliability layer. Why Cloud-Only Processing Is Risky Imagine a utility operating 5,000 field sensors. Each device reports one measurement every second. That produces: 5,000 measurements per second 300,000 measurements per minute 18,000,000 measurements per hour Most of those measurements will describe normal operating conditions. Sending every individual value to a central platform creates unnecessary: Bandwidth consumption Storage growth Processing overhead Communication costs Dependence on network availability More importantly, cloud-only logic can stop working when the connectio

The ports look the same, but one is much faster than the other.

Cloud migration used to be a simple pitch: move off your servers, save money, scale on demand. For Indian enterprises today, the decision is more layered. Compliance rules have tightened. Cloud bills have grown unpredictable. And the assumption that a global hyperscaler is automatically the right fit is being questioned more often, especially by mid-size companies with real workloads and real budgets on the line. If your organisation is planning a migration, here's what actually matters before you sign a contract. Start with why you're migrating Most migrations get justified with one of three reasons: cost, scale, or compliance. Rarely all three at once, and the reason should shape the plan. If cost is the driver, look closely at your current spend. Bandwidth charges, storage tiers, and auto-scaling fees add up in ways that rarely match the sticker price teams budgeted for. If scale is the driver, the question is whether your workload actually needs the breadth a hyperscaler offers, or whether you're paying for hundreds of services you'll never touch. If compliance is the driver, data residency and audit requirements should be the first filter, not an afterthought. Data residency and compliance For Indian businesses, DPDP Act requirements, along with RBI and SEBI guidelines for regulated sectors, increasingly dictate where data can legally sit. This isn't a checkbox. It determines your shortlist of providers before pricing even enters the conversation. Confirm three things with any provider: where the datacentres physically are, whether the billing entity is India-registered, and whether the provider can produce compliance documentation on request, not just a marketing claim. A provider that can name the datacentre city and the entity name without hesitation has usually done the legwork. One that answers in generalities probably hasn't. The real cost of a migration Sticker price is the easiest number to compare and the least useful one. The real cost includes egress

Probabilistic Graph Neural Inference for bio-inspired soft robotics maintenance with ethical auditability baked in I remember the moment it clicked. I was hunched over a workbench in my home lab, staring at a tangled mess of silicone tentacles—a soft robotic octopus arm I’d 3D-printed and embedded with pneumatic channels. The arm was supposed to mimic the graceful, adaptive movements of a real cephalopod, but after a few cycles, it had developed a slow leak at one of the joint interfaces. The pressure sensors were giving erratic readings, and my traditional rule-based diagnostic script was useless. I’d spent weeks training a simple neural network to detect anomalies, but it kept flagging benign sensor noise as critical failures. That’s when I stumbled upon a paper on probabilistic graph neural networks (PGNNs) for molecular dynamics, and I realized: soft robotics maintenance isn’t about deterministic predictions—it’s about reasoning under uncertainty over a complex, interconnected system. This article is the story of how I built a PGNN-based inference system for bio-inspired soft robots, with ethical auditability baked in from the ground up. Technical Background: Why Soft Robotics Needs Probabilistic Graph Inference Soft robotics is fundamentally different from rigid robotics. A rigid arm has well-defined joints, links, and sensors; failures are often binary (motor burnout, gear slip). But a soft robotic tentacle is a continuum of deformable material with distributed sensing and actuation. The system’s state is a high-dimensional, partially observable probability distribution over material strains, pressures, and temperatures. Traditional diagnostic models—like support vector machines or feedforward neural networks—treat each sensor as an independent feature, ignoring the spatial and temporal dependencies that define soft robot behavior. In my research of graph neural networks, I realized that a soft robot is naturally a graph: each sensor node (pressure, strain, te

Don't use WD-40 or Q-tips, for starters.

Amazon EKS has recently introduced support for Kubernetes version rollbacks, letting practitioners revert a cluster's control plane to its previous Kubernetes version within 7 days of an upgrade if issues arise. The feature reduces the risk of in-place cluster upgrades by giving teams a safety net to recover quickly from problematic updates. By Renato Losio

A few weeks ago, I set out to build a small portfolio project: a Streamlit app that could take any tabular dataset, understand something about its structure, and give honest guidance on how to model it. I called it ContextLens . I expected it to be a practical exercise in Python, machine learning, and deployment. What I didn't expect was how closely it would connect with the same questions I work with every day in my PhD research on context-aware intelligent systems. The problem I started with Most introductory machine-learning tutorials follow a familiar sequence: Load a CSV. Choose a model. Train it. Check the accuracy. What often gets skipped is the layer of judgment that should come before any of that: Is this actually a classification problem or a regression problem? Is the target so imbalanced that accuracy becomes misleading? Is that "ID" column secretly leaking the answer into your model? Are there duplicate rows, missing values, high-cardinality categories, or too many features for the number of available observations? Experienced practitioners make these judgments almost automatically. But that reasoning usually remains invisible—it sits in someone's head rather than inside the system, where another person can inspect it. ContextLens is my attempt to make that layer visible. Upload a dataset, and it profiles the data, flags structural risks—missingness, duplicate rows, likely identifier columns, class imbalance, and high-dimensional settings—and adapts its evaluation guidance to what it finds before training a single model. The point is not simply to train a model. The point is to ask whether the modelling process makes sense in the first place. Why I call it "context-aware" rather than "AI-powered" I was deliberate about this distinction, just as I have been throughout my PhD work, and it turned out to be the most important design decision in the whole project. ContextLens does not claim to be intelligent in the way a human expert is. It does not hide its

Apple is already preparing updates to the MacBook Neo, iMac, MacBook Air and more.

A practical roadmap for students who want to build real AWS skills, create an impressive portfolio, and prepare for cloud engineering careers. Introduction Every year, thousands of students begin learning AWS. They watch training videos, collect certificates, complete online courses, and share digital badges on social media. Yet when internship interviews or entry-level cloud engineering opportunities arrive, many struggle to answer a simple question: "What have you actually built on AWS?" The cloud industry rewards practical experience, not passive learning. AWS itself focuses beginner learning on hands-on experience with foundational services such as Amazon S3, Amazon EC2, Amazon VPC, Amazon RDS, and cloud security because these services power most real-world cloud environments. If you're a student aiming for a career in Cloud Engineering, DevOps, Site Reliability Engineering (SRE), Solutions Architecture, or Platform Engineering, this article provides a practical roadmap that can help you become job-ready. Why Students Should Learn AWS Cloud computing has become the backbone of modern technology. Companies of every size use cloud platforms to: Host applications Store data Deploy AI workloads Build scalable systems Reduce infrastructure costs Improve reliability As a result, companies continue to hire professionals with cloud skills across software engineering, cybersecurity, DevOps, networking, and data engineering domains. AWS offers dedicated learning paths, hands-on labs, certification tracks, and career-focused programs specifically designed to help learners develop these skills. The key question is not: "Which AWS service should I memorize?" The better question is: "Can I design, deploy, secure, and troubleshoot cloud solutions?" The 5 AWS Projects Every Student Should Build Instead of completing another course, build these five projects. Project 1: Host a Static Website Using Amazon S3 What You'll Learn Cloud Storage Static Website Hosting Bucket Policies O

✍️ This post was written with two hands. The story — the first part — is Murilo's, lived and told by the person who was there. The technical manual , at the end, was written with AI. The split is intentional and marked in the text. Nothing hidden about the seam: part is human, part is machine, and the reader sees both. If you've ever managed or logged into a web server and never set up SSH keys, it's because you don't yet know the real risks of a break-in — and that's okay. Until you find out what can happen. Logging into a server over SSH with a username and password is like locking the front door of a house that faces the street, with nobody keeping watch. Anyone can try as many combinations as they want, freely. And setting this up takes almost as much time as typing a username and password — and it makes getting into the server much faster and easier afterwards. Ignorant of best practices, I managed my servers for a long time by typing: ssh user@server-ip password That nearly cost me dearly, the day I found out my server had been broken into. After that incident, I realized just how vulnerable a username and password are on SSH. Today I can't say I sleep soundly — no system is completely break-in proof — but I sleep a lot better (and honestly, I always slept well, until I started managing servers). Waking up on a fine Sunday morning to do some maintenance on the server, and finding out it was broken into through the front door because you left a combination padlock facing the street — that is not the kind of surprise I'd wish on anyone. I have a degree in Law. I worked for 15 years in the legal field at a public institution, until I decided to venture into the world of programming. And where did I end up? Managing systems at the institution I work for, after spending some time building automations in Python. Managing systems wasn't exactly what I had in mind when I wanted to learn to code and understand the world of programming. But that opportunity ended up tea