标签：#hardware

While Apple's first foldable iPhone Ultra will headline the September 9 launch, we're also expecting news about AirPods and HomePods.

Every guide I write ends in the same handful of questions. How much hardware do I actually need? What happens when one box dies? Are my backups real or just a feeling? A guide can walk you through a setup, but it can't do arithmetic about your lab — so I built eleven small tools that can, at peira.dev/tools . They're free, none ask who you are, and seven of the eleven keep working once the page has loaded — network unplugged, laptop in a cupboard, whatever. They share one lab profile This is the part that makes them a set rather than eleven unrelated pages. Describe your lab once — tick your services in the sizing calculator, press Save to profile — and the others pick it up. The failure simulator opens with your nodes already modelled; the backup planner knows what data you have; the power-loss playbook knows what's plugged in. Lab doc hands the whole thing back as a Markdown file. Nothing about that profile leaves your browser. No account, no sync, no server that could leak it — which is also why it doesn't follow you between devices. The Markdown export is how you carry it elsewhere. Plan the build Sizing calculator — asks what you want to run and recommends nodes, RAM, and storage. It cares most about RAM, because that's the constraint that actually bites; vCPUs overcommit happily, memory doesn't. Tick "survive one node failure" and it insists on three nodes (a two-node cluster loses quorum the moment one dies). Node failure simulator — kill a node and see which workloads fit on the survivors. It places the critical ones first and names the stranded ones. 3-2-1 backup planner — three copies, two devices, one offsite (the rule CISA recommends ). It's blunt: a snapshot on the same disk as the original is versioning, not a backup. Fix what's broken The overlay network diagnostic is a decision tree born from a miserable afternoon: a container couldn't reach a machine across a Tailscale subnet router, and three layers had to be right — the route in the guest, the ACL

Originally published on hexisteme notes . "Will it fit on our hardware?" is the wrong first question. It's the one everyone asks, because it's free to answer — the thing either loads or it doesn't. Throughput costs you a measurement. So the capacity gate passes, and it feels like the decision is made. The measurement Mac Mini M4, 24GB unified memory, ~120GB/s memory bandwidth. A 27B model, IQ4_XS quantized, 15GB on disk. Capacity gate: pass. Metal's recommendedMaxWorkingSet is 17.76GB, the model is 15GB, ollama ps reports 100% GPU resident. No swap, no spillover. By every "does it fit" criterion this is a clean win. Generation: 5.6 tokens/second. That's not a usable interactive worker. It's barely a usable batch worker. And nothing about the capacity check hinted at it. The arithmetic that would have told me in advance Autoregressive generation reads the entire model's weights once per token. So: ceiling ≈ memory bandwidth ÷ bytes touched per operation = 120 GB/s ÷ 15 GB = 8 tokens/second Measured 5.6 against a ceiling of 8. Ratio 0.70. That ratio is the whole verdict. When measured throughput is a large fraction of the arithmetic ceiling, you are bandwidth-bound , and you now know something concrete: the bottleneck is not your configuration, not memory pressure, not thermal throttling. It's how fast bytes move. Rule of thumb I now use: ratio ≥ 0.5 → bandwidth-bound, and size-reduction fixes are dead. Why "just quantize harder" doesn't work The natural move when capacity is tight is to shrink. Lower quantization, smaller batch, heavier compression. It's the reflex, and in a bandwidth-bound regime it's close to useless. I was considering Q3_K_M at 13.8GB. Run the same division: 120 ÷ 13.8 = 8.7 tokens/second (up from 8) Under 9% more throughput. For a real drop in output quality, because quantization error doesn't scale linearly with size the way bandwidth does — you give up more than you get, every time, in this regime. I killed that plan without downloading anythin

Mapping SpaceMouse Controls to SO-101 Movements In the previous article, I connected the SpaceMouse to the PC and confirmed that all six types of input could be detected correctly. https://dev.to/takeofuture/trying-vla-part-5-setting-up-and-testing-a-spacemouse-eio Forward / Backward Left / Right Up / Down Pitch Roll Yaw During the SpaceMouse test, I confirmed the following input values. Forward horizontal = +Y Backward horizontal = -Y Left horizontal = -X Right horizontal = +X Up = +Z Down = -Z Forward tilt = +Pitch Backward tilt = -Pitch Left tilt = -Roll Right tilt = +Roll Left twist = -Yaw Right twist = +Yaw An important point here is that these values from the SpaceMouse are not sent directly to individual SO-101 motors . Conceptually, the flow from the SpaceMouse to the SO-101 looks like this: SpaceMouse ↓ x / y / z / roll / pitch / yaw ↓ SpaceMouse Teleoperator ↓ target_x / target_y / target_z target_wx / target_wy / target_wz ↓ Inverse Kinematics (IK) ↓ SO-101 Joint Positions ↓ SO-101 The SpaceMouse plugin treats the 6DoF input from the SpaceMouse as movement of the End Effector in Cartesian coordinates. The target movement is then converted into the required SO-101 joint angles using IK, or Inverse Kinematics . In other words, instead of directly specifying something like: "Move this motor by 5 degrees" we provide commands such as: "Move the End Effector slightly forward" "Move the End Effector slightly upward" "Rotate the End Effector slightly" The SpaceMouse provides these commands, and IK calculates how the individual joints need to move. Mapping Between SpaceMouse and LeRobot Coordinates There is one thing we need to be careful about here. The x and y values displayed by the SpaceMouse test do not directly become LeRobot's target_x and target_y . With the default SpaceMouse plugin configuration, the axes are mapped as follows: SpaceMouse y -> target_x SpaceMouse x -> target_y SpaceMouse z -> target_z SpaceMouse roll -> target_wx SpaceMouse pitch -> targ

While Oura has largely dominated the smart ring market for years, a growing number of rivals are now racing to dethrone it by trying all sorts of approaches to get an edge over it.

U.S. CD revenue jumped 58.6% in the first half of 2026, reversing last year’s decline as interest in retro tech and physical media continues to grow.

Ultrahuman is targeting a $200 million annual revenue run rate by January 2027 as it builds a new Qualcomm-powered smart ring.

The Circular Ring 3 series announcement comes amid a year of heightened competition in the smart ring market, following the launches of the Oura Ring 5 and RingConn Gen 3.

Jio is betting it can turn an aging computer into an AI-ready PC for as little as about $11 for two months.

Upgrading Juniper MX Networks from 100GbE to 400GbE: What Engineers Need to Know Moving a production network from 100 Gigabit Ethernet to 400 Gigabit Ethernet sounds simple on paper: Replace a 100G interface with a 400G interface and get four times the bandwidth. In a real carrier or data-center network, however, the interface is only one part of the equation. The router's forwarding silicon, switch fabric, midplane, power system, cooling, optics, software release, slot selection, redundancy configuration, and licensing can all determine whether the expected capacity is actually available. Juniper's MX240, MX480, and MX960 platforms provide an interesting example because these systems can be upgraded with newer generations of Modular Port Concentrators rather than requiring an immediate chassis replacement. One particularly useful case study is the Juniper MPC10E-15C , a Trio 5-based line card capable of supporting both 100GbE and 400GbE interfaces. This article isn't about whether you should buy a particular line card. Instead, we'll use the MPC10E-15C to examine the engineering questions that should be answered before attempting a 100G-to-400G upgrade on an existing Juniper MX network. Video Overview The video provides a short overview of the hardware. Below, we'll go deeper into the architecture and the deployment considerations that matter when integrating this class of line card into an existing MX environment. Why Moving from 100G to 400G Isn't Just a Port Upgrade Suppose an edge router has four heavily utilized 100GbE connections. At first glance, replacing those links with 400GbE interfaces appears straightforward. But consider what happens behind the physical port. Traffic entering that 400G interface must travel through several parts of the system: Interface → Packet Forwarding Engine → Fabric → Other line cards/interfaces Every component in that path needs sufficient capacity. A 400GbE optic connected to a router that cannot move 400 Gbps through its inte

While some of the features see Google playing catch-up to Apple, which already offers similar features for iPhone users, others specifically leverage Gemini to provide various improvements.

Apple is hosting its iPhone release event next week, which is rumored to feature the first-ever foldable iPhone.

Starting on September 1, Ternus will lead one of the world's most valuable companies, but if you're not a dedicated Apple enthusiast, you've probably never heard of this man, who has largely remained out of the spotlight until now.

I attend conferences regularly, and for years I’ve wanted a badge that makes it easy for people to find me online. In 2019, I attended defcon and built defpi , a goofy raspberry pi powered badge. While that was fun I wanted a bit more turnkey and I found just what I wanted with a Badgeware Badger (nerdy domain hack badgewa.re). Since my use case is to just have my socials via QR I chose the e-ink version as it made the most sense. So what does the Badger come equipped with? RP2350 WiFi 1000mAh battery USB-C MicroPython I might not be the biggest Python fan but in the age of AI does it matter? I just ask Codex to help me get the desired outcome I'm looking for! So Codex and I cooked a few hundred lines of Python and boom my profiles are only a QR code scan away. Let's take a look at my default badge screen in all its glory. Social screen. All in all it's pretty simple, get you to either my LinkedIn or my personal site . The crazy thing is the simulator lets me test the code before I deploy it to the badge. make sim and I'm able to test it locally. Then a simple make deploy while the badge is connected and it deploys it all. You can check out the code repo if you wanna steal slopfork my setup. Hacksore / badger Badger 2350 badge Personal Badgeware app for the Badger 2350. Requirements: macOS, uv , Git, and a data-capable USB-C cable. Run make to see the three available commands. Simulator make sim The first run clones and builds Badgeware Desktop beside this repo. Later runs reuse that build, stage the current app in an isolated filesystem, and launch it at the Badger's 264×176 resolution. Simulator controls: Space : Button B; flip between the badge and social/QR screens ↑ / ↓ : change the background pattern ← / → : Buttons A and C Esc : hot reload Deploy the app Connect the Badger over USB-C. Double-tap RESET . Wait for the BADGER drive to appear. Run: make deploy The command validates the app, copies its Python and image files into /apps/badge , safely ejects the dr

Tim Cook’s farewell memo to Apple employees offers a glimpse at how he wants John Ternus to be seen: as a product builder with deep experience across the iPhone, Mac, AirPods, and other major hardware lines.

Hello, I'm Maneshwar, and I'm building LiveReview — a blast-radius aware AI code review built for...

I just wrote about the billion-dollar rounds flooding into humanoid robotics. Here is the story from the other end of the scale, and I find it more encouraging. Hugging Face's open-source robot, a 25-centimeter bipedal machine with fifteen actuators and a sensor kit that includes a camera, speaker, LiDAR, NFC, Bluetooth, and WiFi, just passed a million dollars in sales. Fully open hardware, openly documented, quietly making real money. One of these robotics stories is funded like an industrial giant. The other is a small, open, shippable thing that people are actually buying. They are both true, and the small one is the one most builders can learn from. Open hardware turned out to be a business The reflexive assumption about open-source hardware is that you cannot make money on it, because anyone can copy the design. Hugging Face's robot is a live counterexample. The plans are open, the software stack is open through their LeRobot ecosystem, and it crossed a million in sales anyway. That is worth sitting with, because it means openness and revenue are not the opposites people assume. The reason it works is the same reason open-source software companies work. Most buyers do not want to source fifteen actuators, fabricate a chassis, and debug a sensor stack to save money on a robot that already exists and is affordable. They want the finished thing, they want it to work out of the box, and they are happy to pay the people who designed it. Openness is not the giveaway that kills the business. It is the trust and the ecosystem that make the business, because you can see exactly what you are buying, modify it, and build on a platform other people are also building on. Why this is the better story for builders The mega-funded humanoid companies are placing a bet only a handful of players can place: billions of dollars, years of runway, factories. That is a real path, and it is not your path or mine. The Hugging Face robot is the other path, and it is copyable. Small, open

As my 20s are set to come to an end later this year, I’ve officially reached the age where sleeping in the wrong position or stretching just a little too far can cause aches and pains. I’ve always been somewhat skeptical of massage guns, mostly because I’ve tried a few off-brand ones and just assumed […]

Are XREAL's smart glasses the way of the future for home entertainment?

Meta has detailed MTIA 300, its first in-house accelerator optimized for training ranking and recommendation models. By Matt Foster