标签：#m

Meta was previously reported to be exploring facial recognition for its smart glasses.

DIYer and TikTok user Annike Tan, who goes by @ubeboobey, can carry her cyberdeck around without anyone noticing because it doesn't look like a computer at all. Tan, who has been featured in The Cut and Wired, went viral earlier this year with a mermaid-themed cyberdeck she made inside an old purse. She has since […]

By targeting large numbers of users, attackers increased their chances of success.

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

submitted by /u/henk53 [link] [留言]

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

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

Netflix and EA are both going to capitalize on the World Cup craze.

Rust Ownership System Explained for JavaScript Developers Quick context (why you're writing this) I was trying to rewrite a small utility I’d written in JavaScript—a function that takes a string, splits it into words, and returns the longest one. In JS it’s trivial: you pass the string around, mutate arrays, and nothing blows up. When I attempted the same thing in Rust, the compiler kept yelling at me about “use of moved value” and “cannot borrow as mutable because it is also borrowed as immutable”. I spent a good chunk of an afternoon staring at those errors, thinking I’d missed some syntax detail, only to realize the real issue was a completely different way of thinking about data. If you’ve ever felt that Rust’s compiler is being overly pedantic, you’re not alone—but once you grasp what it’s protecting you from, the frustration turns into appreciation. The Insight Rust doesn’t treat variables like JavaScript’s loosely‑typed references. Instead, it enforces ownership at compile time. Three ideas tend to surprise developers coming from a garbage‑collected world: Move semantics – assigning a value to another variable moves it; the original is no longer usable unless you explicitly clone it. Borrowing rules – you can have either many immutable references or exactly one mutable reference to a piece of data, but never both at the same time. Lifetimes – the compiler tracks how long references are valid, preventing dangling pointers without a garbage collector. The first two are the ones that trip people up most often, and they directly address the class of bugs JavaScript developers know all too well: accidental shared‑state mutations and use‑after‑free‑like mistakes (though in JS they show up as weird undefined values rather than crashes). Let’s look at each with a concrete example, show the common mistake, and then see how to do it right. How (with code) Move semantics – the “you can’t use it after you give it away” surprise fn main () { let greeting = String :: from

İlk yazıda Event Driven Architecture’ın temel kavramlarını, Kafka üzerinde topic/channel tasarımını, event-command ayrımını, schema contract’ları ve producer-consumer ilişkisini ele aldık. Bu yazıda odağı bir adım ileri taşıyıp event’in platform içindeki yaşam döngüsüne bakacağız. Çünkü EDA tasarımında asıl zorluk yalnızca event üretmek değildir. Asıl mesele, üretilen event’in güvenilir, izlenebilir, tekrar işlenebilir, zenginleştirilebilir ve farklı tüketiciler tarafından kullanılabilir hale gelmesidir. Bu yazıda şu sorulara odaklanacağız: Ham event platforma geldiğinde ne olur? Event nasıl doğrulanır, zenginleştirilir ve tüketilebilir hale gelir? Raw, validated, enriched ve curated topic’ler nasıl konumlandırılmalıdır? Bu yapı modern lakehouse mimarilerindeki Medallion yaklaşımıyla nasıl ilişkilendirilebilir? DLQ ve alert topic’leri ne zaman devreye girer? Replay, idempotency, monitoring, security ve governance nasıl düşünülmelidir? Event Pipeline Nedir? EDA mimarilerinde özellikle data platform projelerinde event’ler genellikle bir yaşam döngüsünden geçer. Bu yaşam döngüsü şöyle modellenebilir: raw -> validated -> enriched -> curated | | v v dlq alert Bu yapı, veri akışının aşama aşama olgunlaşmasını sağlar. Raw topic kaynaktan gelen ham event’i taşır. Validated topic schema ve temel kalite kontrollerinden geçmiş event’leri içerir. Enriched topic event’in referans veriler veya başka veri kaynaklarıyla zenginleştirilmiş halidir. Curated topic ise tüketiciler için güvenilir, normalize edilmiş ve iş anlamı netleşmiş event’leri temsil eder. Event Pipeline ve Medallion Architecture İlişkisi Bu yapı, modern lakehouse mimarilerinde sık kullanılan Medallion yaklaşımıyla doğal bir benzerlik taşır. Lakehouse tarafında Bronze katmanı ham veriyi, Silver katmanı temizlenmiş ve zenginleştirilmiş veriyi, Gold katmanı ise iş tüketimine hazır veri ürünlerini temsil eder. Kafka üzerindeki raw, validated, enriched ve curated topic’leri de benzer bir olgunlaşma mantığını akan veri ü

Sliding Your Way Out of Panic: The Mental Trick That Speeds Up Coding Under Fire Quick context (why you're writing this) I still remember the sweat on my palms during a technical interview a couple of years back. The interviewer tossed out the classic “longest substring without repeating characters” problem, gave me five minutes, and watched me stare at the whiteboard like I’d never seen a string before. I started with a brute‑force double loop, felt the clock ticking, and ended up writing a mess that was O(n²) and full of off‑by‑one errors. I walked out feeling like I’d choked, even though I knew the solution deep down. Later, after I’d spent way too many hours replaying that moment in my head, I realized the problem wasn’t my knowledge—it was the way I was framing the question while under pressure. I’d been trying to solve the whole thing at once instead of focusing on the tiny piece that actually mattered. When I finally isolated that piece, the answer clicked in seconds. That’s the mental framework I now teach anyone who’s about to face a ticking clock: identify the invariant you must keep true, and let everything else revolve around it . The Insight When the pressure’s on, your brain wants to grab the biggest chunk it can see and start hacking. That’s a recipe for wasted time and bugs. Top coders do the opposite: they strip away everything that isn’t a constant rule the solution must obey, then build the smallest possible state machine that enforces that rule. For the substring problem the invariant is simple: the current window must contain only unique characters . If you can guarantee that, the answer is just the biggest size that window ever reaches. All the fiddly details—where to move the left pointer, how to know when a duplicate appears—fall out of tracking the last index you saw each character. So the mental steps are: State the invariant (what must always be true). Find the minimal data you need to enforce it (usually a map or a set). Update that data

Bittensor pays roughly eight dollars in TAO token emissions for every dollar of real AI revenue that flows through the network. The exact ratio fluctuates by quarter, but the shape is durable. Q1 2026: about $328 million in annual emissions against $43 million in real AI revenue. That is 7.6 to 1. It is what the crypto-skeptical press has called "extractive by default." It is also what the crypto-friendly analysts call "the subsidy treadmill." The Bittensor engineering team is sophisticated. The subnet validators run real ML evaluation. The miners serve real inference. The revenue is real. The emissions are also real. The cause is the token model itself. One asset is asked to do two jobs that do not belong together. I want to be specific about this part, because every other decentralized AI compute network I have looked at has the same problem, and the fix is well-known. What the token does A token in a decentralized AI compute network does two structurally distinct things. The first job is utility settlement . Contributors run inference, and someone has to pay them for the compute work they did. The payment medium has to scale with usage, has to be denominated in something the contributor can spend on the network or convert to fiat, and has to remain stable enough that contributors can plan around it. This is a billing system. The second job is value capture . Early supporters, investors, and contributors take risk to bootstrap a network that does not yet exist. They have to be paid back for that risk in a way that scales with the eventual success of the network. The payment medium has to be a speculative asset that appreciates as the network grows. This is an equity instrument. A billing system and an equity instrument want opposite things. A billing system that is also a speculative asset means that contributors who get paid in it cannot help but hold a speculative position. An equity instrument that is also a billing system means that token-price volatility show

How I combined spaced repetition, adaptive algorithms, and clean UX to create a multi-purpose learning tool. I’ve always believed that memorisation doesn’t have to feel like a chore. After years of using (and sometimes getting frustrated with) existing tools, I decided to build my own. That’s how MemOrLearn was born in early 2026. MemOrLearn is a web-based adaptive learning platform that brings together flashcards, typing practice, math drills, and Bible memory tools — all powered by intelligent spaced repetition and performance-based adaptation. The Core Idea: Most flashcard apps follow a rigid spaced repetition schedule. I wanted something smarter — a system that actually adapts to the user in real time. If a learner is struggling with a concept, the algorithm increases review frequency and offers slight variations. If they’re crushing it, reviews are intelligently spaced out. This dynamic approach is what makes the experience feel responsive and human. Key Features: Adaptive Flashcards: The heart of the platform. Users can create decks or browse public ones. The system tracks performance per card and automatically adjusts difficulty and frequency. Clean, fast, and minimal interface — exactly how I like my tools as a developer. Typing Tutor: Built to help users improve speed and accuracy through gamified, adaptive drills. It adjusts to your current level so you’re always progressing. Math Drills: Focused practice on math facts with real-time adaptation. The system identifies weak areas quickly and targets them without wasting time on mastered content. Bible Memory Mode: A specialized tool many users love. It applies the same adaptive principles to Scripture memorization, making it effective for individuals, families, and small groups. Teacher / Parent Dashboard: A clean admin view that lets educators assign work, monitor progress, and adjust settings per student. Built with simplicity in mind. Technical Approach (For Fellow Builders): I focused on keeping the back

Enlace a post en Español Click If you are developing games in Godot and using AI to help you code, you are probably tired of constantly switching tabs between your editor and the browser. Copying code, pasting it, explaining your scene context over and over again... it is a massive workflow killer. To solve this, I built Golem-AI (named after the Godot Engine logo because let's face it, it looks like a tiny, friendly mechanical golem). It is a "Cursor-style" AI assistant extension integrated directly into a dock right inside your Godot 4.2+ editor. Today, I am opening the repository to the community as a completely open-source project. It is currently in Beta and has some bugs, but it is fully functional, and I want to share it so we can improve it together. / ____/___ / /__ ____ ___ / | / _/ / / __/ __ \/ / _ \/ __ `__ \______/ /| | / / / /_/ / /_/ / / __/ / / / / /_____/ ___ |_/ / \____/\____/_/\___/_/ /_/ /_/ /_/ |_/___/ 🎮 How it Looks Inside the Editor Here is a glimpse of the integrated dock interface, its session history, and the context autocomplete system in action: 🔥 Key Features 🦙 Local & Cloud Providers: Connect it to Ollama or LM Studio for a 100% free, offline local workflow, or hook it up to OpenAI, Anthropic, Gemini, or Cursor proxies. 🧠 Cursor-Style UX & Context (@ Mentions): Type @ in the chat composer to automatically attach open scenes, specific project files, or custom skills directly into the prompt. 🛠️ Editor Tool Calling: It features an optional multi-step verification loop. The AI can actually interact with native Godot editor tools to help you iterate and fix things faster. 📚 Markdown Skills System: Feed the assistant specific workflows, style guides, or documentation using standard markdown files (/skill or @skill :id). 💬 Advanced Chat UI: Built-in "thinking blocks", agent step progress tracking, searchable history sessions, and a native bilingual UI (English / Spanish). 🛠️ The Current State: "It works, but..." (Looking for Beta Testers!) L

If you've ever worked on an e-commerce project in Chile, sooner or later you bump into Transbank. There's no avoiding it. I built a reference template that use NestJS on the backend and Next.js 16 on the frontend, and in this post I want to walk you through the whole thing: what Webpay Plus actually is, why the integration looks the way it does, and how each piece fits together. Github Repository Reference: Link A bit of context Webpay Plus is one of the most important online payment methods in Chile. The user experience is straightforward: your customer enters an amount on your site, gets redirected to Transbank's branded payment page, fills in their card details there, and comes back to your site with a result. From a UX perspective it's not as slick as Stripe Elements or a fully embedded checkout — the user always sees Transbank's domain in the address bar during the payment — but from a developer's perspective that's actually a feature. You never touch card numbers. PCI scope stays minimal. Transbank handles 3D Secure, fraud rules, and bank routing. The trade-off is that the integration model is what you might politely call classical . It's built around full-page redirects and form POSTs, not modern APIs with JSON responses and webhooks. That's important to understand up front, because it shapes every decision you'll make in the code. The integration flow, step by step Before looking at any code, it helps to have a clear mental model of what's happening . There are three distinct moments where your system talks to Transbank's system, and each one has a specific shape. First Step: When the customer clicks "Pay", the backend asks Transbank to create a transaction (amount, order ID, return URL) . Transbank returns a transaction token and a redirect URL where you must send the user. Second Step: Transbank requires the redirect to be an HTTP POST with the token in a token_ws form field, so you must generate an HTML form with a hidden token_ws input and submit it prog

We are excited to announce the first alpha release of Arc, a lightweight, C-based programming language and interpreter designed for simplicity, performance, and educational clarity. Version Overview Version: v0.0.1-alpha Status: Alpha (Experimental) License: GPL-3.0 This initial release establishes the foundational pipeline of the Arc language, from lexical analysis to AST-based interpretation, featuring a robust set of core language constructs and a custom memory management system. Key Features Language Core Variable System: Declaration and updates using the VAR keyword. Functions: Support for custom functions (FN) with parameters and RETURN values. Control Flow: Conditional branching with IF, THEN, ELIF, and ELSE. Iterative loops with WHILE, FOR, and THEN. Loop control with BREAK and CONTINUE. Exception Handling: Graceful error recovery using TRY...CATCH blocks. Data Types: Integrated support for Numbers (Integers/Floats), Strings, Booleans, and Lists. Import System: Modularize projects by importing other .arc files using IMPORT. Syntax Highlights Case Sensitivity: Keywords (e.g., VAR, WHILE, IF) are case-insensitive. Identifiers (variable and function names) are case-sensitive. Operators: Comprehensive set of arithmetic (+, -, *, /, ^), comparison (==, !=, <, >, <=, >=), and logical (AND, OR, NOT) operators. Comments: Single-line comments starting with #. Built-in Standard Library I/O Operations: print, get_input, open_file, read_file, write_file, close_file. Data Manipulation: len_of, typeof, to_int, split_string, append_list, range. Math Library: A comprehensive math.arc providing constants (PI, E) and functions (sin, cos, tan, sqrt, log, etc.). Tooling & CLI Arc comes with a powerful CLI and an interactive REPL: Interactive REPL: Run code line-by-line with syntax highlighting. CLI Options --debug (-d): View tokens and AST tree during execution. --code (-c): Execute a string of code directly. --float-precision (-p): Control decimal output. --mempool-size (-m):

The indie artist has played a string of surprise, small-venue shows with no phones allowed, prompting fans to piece together clues about a potential upcoming album.

Scientists in Finland found bees could solve an insect version of the classic "box-and-banana" problem.