标签：#AR

The new Light Flip is a mashup of the Light Phone III and an old-school flip phone.

A flip phone with 5G, USB-C, a headphone jack, and a removable battery? Say it ain’t so.

Nvidia’s Vera Rubin platform combines CPUs and GPUs into a single system, reflecting the company’s growing ambition to power every layer of AI infrastructure.

Light co-founder Kaiwei Tang helped create the Motorola Razr, and he's as surprised as anyone that over 20 years later, the flip phone is relevant again.

Stellantis will use technology from Intel's Mobileye to power its Level 2 hands-free driving features for its vehicle brands, the companies announced Tuesday. Starting in 2027, Stellantis will begin using Mobileye's EyeQ system-on-a-chip and a data crowdsourcing program called the Road Experience Management, or REM, which uses real-time data from Mobileye-equipped vehicles to build out […]

Learn more about Paramount's planned acquisition of Warner Bros. Discovery — a historic Hollywood megadeal valued at $111 billion — as it continues to develop.

Xteink makes its pocket-sized e-reader even better with a touchscreen and front light.

Does anyone know the EXACT timing of when NeurIPS reviews are gonna be released? I'm just refreshing openreview all the time and it's stressing me out so much. Thank you for your help. submitted by /u/Anshuman3480 [link] [留言]

Automakers love connected vehicles, but support isn't open-ended.

Garmin announced a new smart band today designed to track "advanced fitness and wellness features" without a screen to eliminate distractions and push its battery life to up to 10 days between charges. Similar to competing products like the Whoop 5.0 and Google's recently announced Fitbit Air, the Cirqa uses a combination of heart rate, […]

Maritime nuclear energy startup Bluecore Energy has raised $10 million in a pre-seed funding round led by Slauson & Co.

More than one-eighth of apps analyzed contained foreign code.

It starts at just under $25,000, but the hybrid won't arrive until next year.

The XMC-1200 won't be in production for another year, so we might see smart glasses with it in 2028.

Last year, Halliday launched one of the worst-reviewed pairs of smart glasses. Now it's back with a second-generation model to right its predecessor's sins.

Halliday’s G2 glasses can listen to and summarize your workplace meetings—with no video involved.

A practical roadmap for students who want to build real AWS skills, create an impressive portfolio, and prepare for cloud engineering careers. Introduction Every year, thousands of students begin learning AWS. They watch training videos, collect certificates, complete online courses, and share digital badges on social media. Yet when internship interviews or entry-level cloud engineering opportunities arrive, many struggle to answer a simple question: "What have you actually built on AWS?" The cloud industry rewards practical experience, not passive learning. AWS itself focuses beginner learning on hands-on experience with foundational services such as Amazon S3, Amazon EC2, Amazon VPC, Amazon RDS, and cloud security because these services power most real-world cloud environments. If you're a student aiming for a career in Cloud Engineering, DevOps, Site Reliability Engineering (SRE), Solutions Architecture, or Platform Engineering, this article provides a practical roadmap that can help you become job-ready. Why Students Should Learn AWS Cloud computing has become the backbone of modern technology. Companies of every size use cloud platforms to: Host applications Store data Deploy AI workloads Build scalable systems Reduce infrastructure costs Improve reliability As a result, companies continue to hire professionals with cloud skills across software engineering, cybersecurity, DevOps, networking, and data engineering domains. AWS offers dedicated learning paths, hands-on labs, certification tracks, and career-focused programs specifically designed to help learners develop these skills. The key question is not: "Which AWS service should I memorize?" The better question is: "Can I design, deploy, secure, and troubleshoot cloud solutions?" The 5 AWS Projects Every Student Should Build Instead of completing another course, build these five projects. Project 1: Host a Static Website Using Amazon S3 What You'll Learn Cloud Storage Static Website Hosting Bucket Policies O

Building a Decompiler Pipeline in Rust: Why Fission Separates NIR and HIR Decompiler output often looks simple from the outside. A binary goes in. Pseudocode comes out. But between those two points, a decompiler must recover several different kinds of information: instruction semantics register and memory effects control flow stack variables calling conventions data types expressions loops and conditionals readable source-like structure Trying to represent all of this in one intermediate representation quickly becomes difficult. While building Fission , a reverse-engineering and binary decompilation workspace written primarily in Rust, I decided to separate the decompiler pipeline into two main intermediate representations: NIR , a lower-level representation intended to preserve machine semantics HIR , a higher-level representation intended to express recovered, human-readable program structure This article explains why that separation exists, what each representation owns, and why it makes decompiler development easier to reason about. Correctness and readability want different things A decompiler has at least two responsibilities. First, it must preserve the behavior of the original machine code. Second, it must produce output that a human can understand. Those goals overlap, but they are not identical. Consider a simplified fragment of machine-level behavior: tmp0 = RAX tmp1 = tmp0 + 1 RAX = tmp1 flags = update_flags(tmp0, 1, tmp1) A human reader may prefer to see: rax ++ ; The concise form is easier to read, but it omits details that may still matter elsewhere in the pipeline. The flags update could affect a later conditional branch. The operation width may matter. The source and destination could alias. The operation may have originated from an instruction with additional side effects. If the decompiler converts everything into source-like syntax too early, it becomes easy to discard evidence. If it keeps everything at machine level until the final rendering st

AI coding tools have gotten very good at one thing: generating code fast. What they haven't gotten good at is discipline. They don't know your architecture. They don't remember that you rejected a pattern last sprint. They don't know which parts of your context window are signal and which are noise. And they have no concept of a development workflow — no phases, no review gates, no verification steps. You describe what you want, they generate, and you hope the output fits. For small tasks this works fine. For anything that touches your real codebase at scale — refactors, new features with cross-cutting concerns, compliance-sensitive changes — the lack of workflow structure creates subtle, expensive problems that compound over time. We've been building Ortho to address two of these problems: workflow discipline (through ASES, a 6-phase AI development methodology) and context discipline (through a 9-component token optimization pipeline). This post explains both. The workflow problem with AI coding tools When a junior engineer joins your team, you don't just hand them a task and say "generate." There's a process: understand the codebase, plan the change, get the architecture reviewed, build it, test it, verify it, get it reviewed. The process exists because individual steps catch different categories of mistakes. AI coding tools collapse all of that into one step. You prompt. You get code. Done. The result isn't always bad code per file. The problem is architectural: the AI has no model of your layer boundaries, so it imports from layers it shouldn't touch. It has no memory of past decisions, so it re-proposes patterns you've already rejected. It has no verification step, so it confidently generates code that looks right but has subtle issues a reviewer would have caught in 30 seconds. The missing piece isn't a smarter model. It's a workflow. ASES: A 6-phase workflow for AI-assisted development ASES (v1.2) is the methodology built into Ortho's orchestration layer. It

Managing payload cms lexical tables in a content-heavy site means enabling EXPERIMENTAL_TableFeature — but the real trap is the markdown import that strips tables without warning. We lost a whole batch of production blog posts to this exact hole before we found the fix. Here’s why it happens and the step-by-step configuration that keeps your tables intact. The Silent Table Eater: Payload CMS Lexical Tables and Markdown Conversion The default markdown-to-Lexical conversion helper completely ignores your editor’s feature list. So even when you’ve added the table feature to your editor config, every GFM table in imported markdown is silently dropped. Here’s the code that ate our data: import { editorConfigFactory , defaultFeatures } from ' @payloadcms/richtext-lexical ' // ❌ This uses a plain config that doesn’t know about tables const mdConverter = editorConfigFactory . default ({ features : defaultFeatures , }) const lexicalData = mdConverter . parse ( ' # Hello \n\n | A | B | \n |---|---| \n | 1 | 2 | ' ) // result: { root: … } — no table node anywhere The problem: editorConfigFactory.default builds a conversion pipeline from a static feature set, not from your actual editor config. Any experimental or custom feature you’ve wired into the editor simply isn’t there during markdown parsing. Fix It: Wire EXPERIMENTAL_TableFeature Into the Conversion Config Switch to editorConfigFactory.fromFeatures , which actually reads the feature array you provide. Include the table feature alongside the defaults, and the markdown converter will start producing proper Lexical table nodes. import { editorConfigFactory , defaultFeatures , EXPERIMENTAL_TableFeature , } from ' @payloadcms/richtext-lexical ' const mdConverter = editorConfigFactory . fromFeatures ({ features : [... defaultFeatures , EXPERIMENTAL_TableFeature ()], }) Takeaway: You must add EXPERIMENTAL_TableFeature() to both your editor’s features array and to every markdown conversion config. Missing one side silently eat