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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

These WIRED-tested wearables reduce your reliance on a phone while keeping you connected.

GitLab has introduced a new approach to Green DevOps, demonstrating how software engineering teams can measure the carbon emissions generated by their CI/CD pipelines. By Craig Risi

These locks, lights, and other smart home upgrades let you add automation without messing up your home’s vibe.

The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and…

Last month, more than a hundred Stanford students left their own graduation to protest Google’s military contracts and deals with ICE. Two organizers, Amanda Campos and Eva Jones, tell us why.

Recently submitted my abstract and the submission number is 32xxx. With still a day to go, I just wonder where are we heading. Hope these conferences at least start making the reviews and names public for the withdrawn/rejected papers. So that people atleast take that accountability submitted by /u/Fantastic-Nerve-4056 [link] [留言]

Bhavuk Jain discusses translating foundational AI into scalable mobile products. He shares the engineering challenges behind AI Wallpapers and Circle to Search, detailing how to implement robust runtime guardrails, fine-tuning, and seamless OS integration. For engineering leaders, he explains balancing UX constraints with model latency and infrastructure cost to deliver safe, reliable AI. By Bhavuk Jain

Every day, the internet quietly convinces us that we're running out of time. A 19-year-old launches...

Dealerships installed alarms in millions of vehicles—and left them in even if the buyer didn’t want them. Now researchers warn they can be hacked to unlock, track, and disable cars.

Yelp has launched Training Orchestrator. This new internal framework replaces individual team Spark training scripts. Now, it uses a configuration-driven, DAG-based execution model. By Claudio Masolo

Members of the Army received an email informing them that they were rapidly depleting their AI tokens, and needed to limit use.

Once a quirky bastion of amateur vulva jewelry and pet portraits, Etsy is now deluged with mass-produced goods and AI knockoffs. Some customers don’t seem to mind.

Hi everyone, I'm working on extracting the hierarchical structure of long PDF documents (legal/regulatory text, lots of numbered sections) and would like to gather some feedback on my approach before committing to it. What I've done so far: I render each PDF page to an image and run it through Baidu's DeepSeek-OCR model . It returns each detected block with a bounding box [x0, y0, x1, y1] , a label ( title , text , list , table , header , footer , etc.), and the recognized text. The OCR quality itself is genuinely good as the text comes out clean. The problem: the labels can't always be trusted. At this stage I want to extract and detect all the titles in my document, but sometimes a title element gets classified as something else (like normal body text). Concrete example: Say my section has the following hierarchy: ANNEX I — GENERAL PRINCIPLES AND PROCEDURES └── TITLE I — FOREIGN CURRENCY INVESTMENT └── A. Currency distribution └── 1. Redistribution of reserves ├── (a) Introduction │ body text │ list │ ... ├── (b) Procedure for a normal redistribution of reserves │ body text │ list │ ... └── (c) Procedure for an ad hoc redistribution of reserves body text list ... Logically, every element aside from the body text and lists should be detected as title . But the model output is: label='title' x0=475 y0=157 x1=548 width=73 text='ANNEX I' label='text' x0=480 y0=229 x1=542 width=62 text='TITLE I' label='title' x0=334 y0=181 x1=690 width=356 text='GENERAL PRINCIPLES AND PROCEDURES' label='title' x0=407 y0=368 x1=616 width=209 text='A. Currency distribution' label='title' x0=408 y0=392 x1=634 width=226 text='1. Redistribution of reserves' label='title' x0=163 y0=416 x1=304 width=141 text='(a) Introduction' label='title' x0=163 y0=544 x1=578 width=415 text='(b) Procedure for a normal redistribution of reserves' label='title' x0=163 y0=219 x1=586 width=423 text='(c) Procedure for an ad hoc redistribution of reserves' The top-level section marker TITLE I was labeled text , w