Musk can't be trusted to protect X user privacy, public commenters warn FTC.

As a high school student, I’m trying to figure out what major I’m interested in. About half a year ago, I thought EECS was a great major for some STEM students like me, because I see many of the world's most influential entrepreneurs, such as Elon Musk and Jensen Huang, have built companies around software, hardware, and artificial intelligence, helping advance technological and society. Recently, however, I have been exposed to a variety of AI-powered programming tools, such as Claude Code and

Meta may have found one way to slash its massive data center bill: tents.

For those doing heavy AI programming or running local models on mobile hardware: Is the current generation of iPhone Pro or Samsung Galaxy Ultra actually making a difference in your workflow, or is it mostly a gimmick right now? submitted by /u/Spirited_Good9789 [link] [留言]

Poke, the startup that lets people use AI agents through simple text messages, has become the first AI agent approved for Apple’s Messages for Business platform.

submitted by /u/henk53 [link] [留言]

Yesterday I was looking at the caniuse page for invokers and had something like 83% coverage...I thought: I wonder what other feature has the same coverage and maybe I'm already using it. So I built it: https://ifyouuse.com submitted by /u/pablopang [link] [留言]

Canada's "AI for All" plan prioritizes strengthening data protections and increasing AI adoption.

Fusion startup Helion is racing to complete a power plant for Microsoft by 2028. A fresh infusion of cash should help with that.

submitted by /u/syrusakbary [link] [留言]

Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product. What if you could change how an AI thinks without retraining it? Not by rewriting prompts. Not by fine-tuning billions of parameters. Not by collecting another mountain of training data. Instead, imagine finding a direction inside the model's internal representation space and nudging the model a little in that direction. A small push. A different behavior. This idea sits at the heart of one of the most fascinating areas of modern AI interpretability: steering vectors . Steering vectors suggest that many behaviors we care about—careful reasoning, honesty, coding style, security awareness, verbosity, and more—may already exist inside a model. The challenge is learning how to activate them. Let's explore what steering vectors are, how they're created, and why they might become one of the most practical tools for controlling AI systems. 1. What Exactly Is a Steering Vector? Large language models process information through layers of high-dimensional activations. At any point during generation, the model's internal state can be represented as a vector containing thousands of numbers. Researchers discovered something surprising: Different behaviors often correspond to different regions of this activation space. For example: Writing Python code Solving math problems Speaking French Explaining concepts carefully Producing insecure code Each tends to produce distinctive activation patterns. A steering vector is essentially the difference between two activation patterns. Suppose we gather examples where the model is: Careful Methodical Thorough and compare them to examples where it is: Rushed Superficial Incomplete The average difference between these internal states becomes a steering vector. At inference time, we can add that vector back into the model's activatio

Believe it or not, this is not the first robotaxi-assisted theft.

Voltage lies. Put a battery under load and its terminal voltage sags. Let it rest and the voltage springs back. A naive fuel gauge watching only voltage will happily tell you a worn-out cell is "fine" right up until it falls off a cliff. The number you actually care about — is this battery still good, or is it time to replace it? — isn't in the instantaneous voltage at all. It's in the capacity : how much charge the cell can still deliver between full and empty. That quantity fades as a cell ages. Tracking it is called State of Health (SoH) , and it's the difference between "the device says 80%" and "the device has 80% of the runtime it had when it was new." I wanted my open-source battery SDK ( ibattery-sdk , Apache-2.0) to learn SoH on the device itself — no cloud model, no floating-point, on MCUs with kilobytes of RAM. This post is the story of getting that working end to end: from a coulomb integral in firmware to a faded value showing up live on a Grafana dashboard. The idea: learn capacity from one full→empty trip You don't need a PhD-grade model to estimate usable capacity. You need two anchors and an ammeter. Full anchor — when the cell is at its full-voltage plateau, declare "this is full" and set the coulomb counter to the rated capacity. Discharge — integrate current over time (coulomb counting). Every milliamp-hour that leaves the cell ticks the counter down. Empty anchor — when the cell hits its empty-voltage threshold, look at how much charge actually flowed. A healthy cell delivers close to its rated capacity before going empty. An aged cell hits empty early — it simply has less to give. From the charge measured between those two anchors, you get the cell's real usable capacity, and SoH = measured / rated . The SDK runs it through an integer EMA (so one noisy excursion doesn't whip the estimate around) and a plausibility guard (reject anything outside 30–120% of rated — that's almost certainly a glitch, not a real measurement). The whole thing is inte