标签：#ics

The Pentagon awarded SpaceX a $4.16 billion contract to build missile-tracking satellites linked with President Donald Trump's planned "Golden Dome" defense system, as reported earlier by Bloomberg. In an announcement on Friday, the US Space Force says the sensor-equipped satellites will allow it to detect and track targets from space. The Elon Musk-owned SpaceX - […]

The website, which compares human beings to extraterrestrials, touts arrest numbers from the Trump administration’s sweeping immigration crackdown. But some of its details are really out there.

Border security czar Tom Homan keeps threatening to "flood" New York City with ICE agents. But a new investigation shows that ICE has been quietly ramping up arrests in the New York area already - and disproportionately targeting Latino neighborhoods. The City, a local nonprofit news organization, found 430 street arrests in the metropolitan area […]

Where's the Trump phone? We're going to keep talking about it every week. We've reached out, as usual, to ask about the Trump phone's whereabouts. As the phone's supposed launch continues, we noticed its website may be violating FTC rules. When the Trump phone was first announced last June, it was proudly promised to be […]

Lithium battery packs are only as strong as their weakest cell. Whether you're designing a drone battery, an EV pack, or an energy storage system, cell balancing plays a critical role in battery safety, lifespan, and performance. But many developers and hardware engineers still confuse passive balancing and active balancing , or underestimate how important balancing becomes in multi-cell lithium systems. In this article, we'll break down: Why battery balancing matters What causes cell imbalance How passive balancing works How active balancing works Engineering trade-offs between both methods Where each balancing strategy is commonly used 1. Why Battery Cells Become Unbalanced In theory, every lithium cell inside a battery pack should behave identically. In reality, that never happens. Even cells from the same production batch will have slight differences in: Internal resistance Capacity Self-discharge rate Temperature response Aging characteristics Over time, those small differences accumulate. For example: One cell may charge slightly faster Another may discharge deeper One may heat up more under load Eventually, the pack voltage becomes uneven. This is called cell imbalance . 2. Why Cell Imbalance Is Dangerous Imagine a 4S lithium battery pack. If one cell reaches 4.25V while the others are still at 4.10V, the charger must stop to avoid overcharging that single cell. That means: The entire pack never reaches full usable capacity Weak cells age faster Heat generation increases Safety risks become higher The same problem happens during discharge. If one cell drops below the minimum safe voltage earlier than others, the BMS cuts power to protect the pack — even though the remaining cells still contain energy. In other words: A battery pack is limited by its weakest cell. 3. What Is Battery Balancing? Battery balancing is the process of equalizing cell voltages inside a battery pack. The goal is simple: Prevent overcharge Prevent over-discharge Improve pack lifespan I

Claims about low testosterone and false accusations of veganism might play well to the online far right, but will they win an election?

LinkerBot makes dexterous robotic hands for as little as $600. It wants to become the standard for humanoids and automated factories—and eventually replace human labor altogether.

Analytics is often treated as a reporting feature: a dashboard added after the product already exists. That is usually too late. For software operators, analytics is closer to infrastructure. It is the layer that makes the state of the product visible. Without it, a team cannot evaluate the situation, understand whether the product creates value, or know whether a workflow is improving. That is the reason WebmasterID is built around privacy-first analytics. The goal is not to collect more data than necessary. The goal is to preserve enough signal to make practical decisions without turning measurement into surveillance. Analytics answers operational questions Good analytics starts with plain questions. What happened? Which workflow changed? Which part of the product is used? Where do people leave? Which system events matter? What evidence supports the conclusion? Those questions sound simple, but they are the foundation of product judgment. If the data model cannot answer them, the team is forced to reason from anecdotes, support messages, and internal opinion. Those inputs still matter, but they are not enough on their own. Analytics gives operators a way to compare the current state with the previous state. It makes change visible. It also makes uncertainty visible when the evidence is incomplete. Product value needs evidence A product can look polished and still fail to create value. It can also look unfinished while solving a real operational problem. The difference is usually visible in behavior. Do users return? Do they complete the workflow? Do they avoid a manual step? Does the product reduce confusion? Does it make a business process easier to operate? Privacy-first analytics should help answer those questions without building a profile of every person. In many cases, first-party events, coarse context, workflow state, and careful retention rules are enough. The system does not need to know everything about a user to show whether a product path is working.

Temu has been fined €200 million (about $232 million) by the European Commission after it found that consumers are "very likely to encounter illegal items" on the popular Chinese e-commerce platform. According to the commission, Temu breached Digital Service Act (DSA) rules by failing to identify and assess the systemic risks of illegal products being […]

AI bias mirrors human bias; both stem from our language and lived experiences. Ethics and AI are inseparable, but AI changes affordances, making harmful actions easier to carry out. The EU regulations apply to AI, since digital products are products. The ultimate goal is accountability: companies must ensure transparency, and laws should favor using the simplest AI that gets the job done. By Ben Linders

Why your age-gating algorithm is probably doomed to fail in the wild For developers building in the computer vision and biometrics space, there is a massive gap between a model that passes a NIST benchmark and a system that survives the "child-with-a-VPN" test. Recent data indicates that roughly 32% of children are successfully bypassing age-gating tech. As engineers, our first instinct is often to blame the model—to tweak the weights, gather more training data, or tighten the threshold. But the technical reality is more sobering: the failure isn't in the algorithm; it's in the deployment architecture. The Problem with Probabilistic Logic in Binary Workflows Most age estimation models rely on analyzing biometric markers—skin texture, bone structure ratios, and periocular geometry. They produce a probabilistic age range. However, according to NIST's evaluation of age estimation software, to maintain a low false-positive rate, systems often need to set a "challenge age" between 29 and 33 years. If you are a dev tasked with keeping 17-year-olds off a platform, you are essentially forced to build a "buffer zone" of over a decade. If the system flags anyone who might be under 30, the UX becomes a nightmare. If you lower the threshold to 18, the false-negative rate skyrockets. This is the fundamental trade-off of probabilistic facial analysis: precision and recall are at constant war, and in a high-traffic production environment, the "noise" of real-world variables (poor lighting, low-res sensors, off-axis angles) makes consistency nearly impossible. The Breakdown of the Identity Handoff Beyond the model, there are three technical failure points that no amount of Euclidean distance analysis can fix if the pipeline is broken: The Signal-to-Noise Ratio at Source: Evaluation datasets are clean. Production images are taken on scratched lenses in low-light bedrooms. The delta between training distribution and inference-time reality is where the first 10% of accuracy vanishes.

So far this year, authorities have seized or demanded ballots from elections in four states. Experts fear the trend could throw the midterms into chaos unless courts draw a line.

What if a development board could be as friendly as an Arduino, yet powerful enough to drive industrial-grade robots? That is exactly the gap the new G2 Nano sets out to close. Most hobby boards handle simple robot builds with ease, but they hit a wall once a project demands tight, simultaneous control of several motors. Embedded systems engineer Ryan Strace noticed that the custom controllers built for these complex machines tend to look remarkably alike, with motor coordination as the recurring headache. Rather than reinventing that hardware on every project, he designed a single accessible platform to handle it, and the G2 Nano is the result. Precise motor control usually leans on closed-loop techniques like PID, but real-world gremlins such as integrator windup, sensor noise, mechanical saturation, and phase delay can all degrade performance. Robots also need smooth multi-axis motion with managed acceleration to avoid jerky, stressful movement, plus solid fault handling so an unexpected state does not wreck expensive parts. Strace is tackling all of this with a low-cost motion-control IC he is developing, and the G2 Nano is the high-performance platform built to prove out that future chip. What's under the hood Processor: NXP Arm Cortex-M7 clocked at a brisk 1 GHz Wireless: u-blox MAYA-W1 module with dual-band Wi-Fi and Bluetooth Motion sensing: six-axis IMU (3-axis accelerometer plus 3-axis gyroscope) and a dedicated magnetometer for compass heading Form factor: just 0.8 by 3 inches, breadboard-friendly, on a six-layer PCB stackup for clean high-speed signals On the software side, the board targets native micro-ROS and the Zephyr real-time operating system, with planned MicroPython support so you can prototype in Python without paying the usual speed penalty, thanks to that unusually high clock. Every design file and document is open-source and published on GitHub. Build it yourself If you want to follow along, the core ingredients are clear: an NXP Cortex-M7 a

The bill requires companies like OpenAI, Anthropic, and Google to have third parties confirm they’re following safety standards. Illinois governor JB Pritzker says he’ll sign it.

The black hole accounts for over two-thirds the mass of the object it inhabits.

Amid ongoing economic anxieties, BLK and other companies are giving away basic essentials to appeal to the public.

“Republicans are just going to have to suck it up and get it done,” says one Trump aide about the funding melee. The votes, though, may simply not be there.

The office was created a year ago and seemingly named for a far right European plan to expel minorities and immigrants from Western nations. It now works, a source says, with little to no oversight.

The email instructs workers to report behavior that predates Donald Trump’s second inauguration. One employee tells WIRED it felt like a “reminder to narc on your coworkers.”

Parsing a papal proclamation.