标签：#m

Even if you only use WhatsApp sometimes, you might want to snag your username now to stop giving out your phone number.

WhatsApp username can be between 3 to 35 characters.

WhatsApp username can be between 3 to 40 characters length

WhatsApp is introducing a new way to add and chat with contacts, without having to share your phone number. Usernames will be launching "later this year," in a move to make the communications platform "even more private," allowing you to keep your phone number concealed from people who aren't already in your contacts. Usernames are […]

Dbrand announced Monday that it's refunding everyone who bought its Steam Machine Companion Cube, which it said it made "without a license from Valve." Dbrand announced the Portal-themed Steam Machine accessory in November and took preorders for it last Monday. But a few days later, the product had disappeared from the company's website and the […]

Most developers expect to go through multiple interview rounds, coding assessments, or take-home assignments before getting hired. That wasn't my experience. I ended up working with the YouTuber I had admired for years without an interview, without an exam, and without even sending a resume. Here's how it happened. It Started Long Before the Opportunity I started freelancing when I was in Class 9. At first, it wasn't about building a career. I simply enjoyed creating websites and wanted to gain experience while earning some money. Over the years, I worked with different clients, solved different problems, and learned something from every project. Those freelance gigs taught me much more than writing code—they taught me how to communicate with clients, deliver on time, and take ownership of my work. The Opportunity A few months ago, one of my favorite YouTubers posted in his WhatsApp community that he was looking for someone to build a website. I happened to be a member of that group. As soon as I saw the message, I reached out and told him I could build it. Instead of spending time wondering whether I was "good enough," I decided to let my work answer that question. Building It in Under 24 Hours Once I received the project, I focused entirely on delivering it as quickly as possible without compromising quality. I completed the website in less than 24 hours. After reviewing it, he requested a few modifications. I implemented them immediately and delivered the updated version. At that point, I assumed the project was finished. The Unexpected Offer A few days later, he contacted me again. He had another web application that had been stuck because a previous developer couldn't complete it. He asked if I could take over. That conversation eventually turned into a job offer. No coding interview. No aptitude test. No technical assessment. Just trust built through delivering one project well. What I Learned Looking back, I don't think I got the job because I replied quickly

Director Travis Knight is also the creative mind behind 2016's Oscar-nominated Kubo and the Two Strings .

Where AI incidents in legal actually come from, and what infrastructure (not policy) prevents them. Blake Aber · Predicate Ventures · 2026 The policy layer is table stakes. It isn't enough. When Sullivan & Cromwell apologized to a federal bankruptcy judge in April 2026 for AI hallucinations in a court filing, the firm's apology letter said the firm had policies. Safeguards existed. Those safeguards weren't followed. That framing, "the safeguard existed but wasn't followed," is how a policy failure gets described. But something more specific happened: a hallucination was generated, wasn't caught at generation time, wasn't caught at review time, and made it into a document that got filed. That's not a policy problem. It's an infrastructure problem. The distinction matters because it determines what you build next. What policy can and can't do Policy is a promise made before the event. A well-written AI acceptable-use policy says: don't submit output you haven't reviewed; verify citations before they go into a document; a human must approve anything client-facing. This works when the human executing the task has time, attention, and professional accountability in that moment. It fails when one of those is missing: a deadline, a junior practitioner, a late-night run. Policy can't: Verify a citation at the point of generation Flag output that has drifted below a confidence threshold Stop hallucinated text from appearing in a draft before a human ever sees it Detect when the underlying model is behaving differently than it was in testing Policy can: Set the expectation that review must happen Define who bears accountability when it doesn't Create a paper trail after the fact One of those is prevention. The other is compliance. What infrastructure does instead An AI harness layer operates at the point of generation, not at the point of review. This reflects a broader reality that production AI is mostly harness and very little model . For legal work specifically, three com

Semantic HTML and Accessibility: Building Better Websites Introduction Semantic HTML is the practice of using HTML elements that clearly describe the purpose of the content on a webpage. Instead of using many <div> elements, semantic tags such as <header> , <nav> , <main> , <section> , <article> , and <footer> make the page easier to understand. Semantic HTML is important because it improves accessibility, helps search engines understand web pages, and makes code easier to read and maintain. Before: Non-Semantic HTML <div class= "header" > <h1> My Portfolio </h1> </div> <div class= "navigation" > <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </div> <div class= "content" > <p> Welcome to my portfolio website. </p> </div> After: Semantic HTML <header> <h1> My Portfolio </h1> </header> <nav> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </nav> <main> <section> <p> Welcome to my portfolio website. </p> </section> </main> Accessibility Issues I Found 1. Images Missing Alternative Text Before: <img src= "images/profile.jpg" > After: <img src= "images/profile.jpg" alt= "Profile picture of Grace Loko" > Adding alternative text allows screen readers to describe images to users with visual impairments. 2. Navigation Was Not Semantic Before: <div> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </div> After: <nav> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </nav> Using the <nav> element helps assistive technologies identify the website navigation. 3. Form Inputs Had No Labels Before: <input type= "text" placeholder= "Your Name" > After: <label for= "name" > Name </label> <input type= "text" id= "name" name= "name" > Labels improve accessibility by helping screen readers identify each form field. Conclusion This accessibility audit helped me understand the importance of semantic HTML and accessible web design. By replacing non-semantic elements with semantic tags, adding image alt text,

Galfus Script has reached its first MVP milestone. Galfus is an experimental programming language written in Rust, designed around a typed VM-first execution model, compact .gfb artifacts, deterministic module/workspace resolution, and an ownership model based on anchors, edges, and weak observers. The MVP goal was not to build a full ecosystem yet. The goal was to prove the complete local execution pipeline: txt .gfs source -> lexer and parser -> resolver -> type checker and semantic analyzer -> ownership check -> MIR lowering -> bytecode emitter -> Galfus Module Image -> .gfb serialization -> VM interpreter execution https://github.com/vulppi-dev/galfus-script/discussions/10

[v0.3.0] - 2026-06-29 🚀 Added Checkpoint Mechanism — ReActCore introduces three checkpoints during streaming: content_ended (after text content), before_tool_calls (before tool calls), and after_tool_calls (after tool calls), enabling precise interception and state synchronization of the execution flow. Message Queue System — Added a new MessageQueue class in run.py , supporting async enqueue, drain, and remove operations. Users can now queue messages while the LLM is running; queued messages are sent automatically after the current task completes. The frontend introduces a QueueBar component to display queued messages, with CSS spinning animation, single-line ellipsis, and hover-to-delete functionality. Queue Message Merging — MessageQueue.drain_all() now merges consecutive messages with the same name into a single message, preventing fragmented user input when multiple queue entries share the same sender. Queue WebSocket Events — The execution event protocol introduces three new event types: message_queued , queue_drained , and queue_returned ( useRunWebSocket.ts ). The frontend processes queue state updates in real time. Stop & Queue Integration — When the user clicks Stop, pending queued messages are returned to the input box via queue_returned . Checkpoint stops cleanly clear the queue and automatically start the next message. System Notification Messages — Introduced the SystemMessage type (with notification role) to separate error messages from assistant content. Errors are now rendered as independent notification bubbles, no longer embedded within assistant message cards. tiktoken Real-Time Token Estimation — ReActCore initializes a tiktoken encoder on startup for real-time token counting during streaming. Unknown models fall back to o200k_base . 🔧 Improved Custom Model Name Auto-Complete — The model name field in ModelManager has been upgraded from Select to AutoComplete , allowing users to type custom model names not in the predefined list. Message Block T

Qwen's new image paper is easy to read as another benchmark bump. Qwen-Image-2.0-RL takes the existing Qwen-Image-2.0 model, runs a reinforcement-learning pass on top, and reports better scores: 57.84 on Qwen-Image-Bench, up 2.61 points from the base model. Its text-to-image arena Elo moves from 1115 to 1193. Its image-editing arena Elo moves from 1256 to 1349. Those are the headline numbers. They are not the useful part. The useful part is the training story underneath them. The paper is a good reminder that "just optimize the reward" is a dangerously incomplete sentence, especially when the model is not an LLM and the output space is a whole image. The model got better, but not by one simple trick Qwen-Image-2.0-RL is a post-training pipeline for a diffusion image model. In plain English: the base model already knows how to generate and edit images. The RL stage tries to steer it toward outputs humans prefer, including better prompt following, better aesthetics, better portrait fidelity, and more reliable editing. The team builds task-specific reward models. For text-to-image, those rewards cover alignment, aesthetics, and portrait quality. For editing, they cover instruction following and face identity preservation. Then they train with a GRPO-style setup adapted for flow-matching diffusion models. If you only squint at that, it sounds like the same broad recipe people use for language models: generate candidates, score them, push the model toward the better ones. The paper is more interesting because it shows how fragile that story becomes once you touch the actual training loop. The CFG detail is the first real lesson Classifier-free guidance, usually shortened to CFG, is one of those diffusion-model knobs that users mostly experience as "make the image follow the prompt harder." Under the hood, it changes how the model samples. The Qwen team tested three ways to use it during RL. Using CFG during both rollout and training made the images collapse into incohere

"We believe this will be one of the most transformative deals in the space industry."

Want to go back to your ex... AI assistant? We don't blame you. Take these steps to get Google Assistant back after your fling with Gemini.

submitted by /u/ogoffart [link] [留言]

Asymm Systems has released Eliya 25.0.3, an OpenJDK 25 LTS distribution aimed at improving production diagnostics in Java environments. It consolidates several HotSpot features into an opt-in Production profile. Eliya is designed for teams needing reliable diagnostic data, especially in regulated settings. Future enhancements are planned for Phase 2. By A N M Bazlur Rahman

Target built a generative AI system to improve marketing campaign forecasting by retrieving and ranking similar historical campaigns. Using embeddings, vector search, and LLM ranking, it replaces rule-based workflows. Evaluation shows 75% top-1 and 100% top-3 coverage. The system reduces manual effort, improves consistency, and uses feedback loops to refine retrieval using campaign outcomes. By Leela Kumili

It’s time for our annual Fourth of July grill episode here at Decoder. This is when we invite the CEOs of outdoor cooking companies onto the show to explain just how their businesses kind of look like every other business. And this is a very special edition. Today I’m talking to Roger Dahle, the CEO […]

Burn-in is often overblown, but understanding the phenomenon is still important.

NBCUniversal and Sky will be spun off into separate companies.