标签：#AR

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

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

I Have a Job — I Just Need to Do It A few months ago, I left my last job as a remote Unity developer. Before leaving, I had already started working on a freelance project to build a multi-tenant security and workforce management system . It became a fairly large system involving web applications, mobile apps, real-time tracking, scheduling, reporting, GPS, notifications, and more. The project is now mostly completed, but the client wants to continue adding maintenance, business logic changes, UI modifications, and new development under the same maintenance fee. That doesn't work for me. Maintenance and development are two different things, and when the amount of new development keeps growing while the price stays the same, eventually it stops being sustainable. So I started thinking about what I would do next. The Fear of Not Having a Job For the last few days, I was genuinely worried. I have more than 250,000 BDT in savings , so I'm not in an immediate financial crisis. But money slowly disappears when there is no income. And freelancing isn't exactly comforting right now either. I've been using Upwork, but the experience has become increasingly frustrating. You apply for jobs and often hear nothing. Some clients post a job and never hire anyone. Some jobs get dozens of proposals and disappear quickly. Some invites arrive, but someone else gets hired almost immediately. And every application costs money. After a while, it starts feeling like you're continuously putting money into a machine that promises a job somewhere in the future. You keep applying. You keep waiting. You keep hoping. And eventually, I realized something. What I Was Actually Missing I wasn't missing money. I was missing a job . And there is an important difference. I already have the skills. I already know how to build software. I already have ideas. I already have projects I want to work on. I was simply thinking that a "job" had to come from someone else. Then I thought: I can create my own job

Welcome to part one! This is the start of a series where I’ll be posting about my webdev and HTML nightmares. I hope you enjoy the read as much as I hate User Interfaces! Consider this a shared space for learning—I’m sharing what I’ve learned so far, and I’d love to hear your thoughts or better solutions in the comments. To kick things off, let’s talk about how this whole mess started. As a solo developer, you want to spend 99% of your time actually building the things you love. So when it’s time to share builds with early playtesters, I naturally take the path of least resistance... a pinned link in a Discord channel and a shared Google Drive folder. And for a while, it works. Until it suddenly doesn't. The Problem: The "Easy way" Trap Privately, with a small group of alpha testers, Discord is great. You can pin messages, create specific channels, and guide people directly. But as soon as you want to go public, Discord becomes a nightmare for onboarding new users: The "Tutorial" Requirement: If a new user needs a 5-minute guide just to navigate your Discord server to find the launcher or the latest release, you’ve already lost them. Zero Discoverability: Discord is great for community and chat, but terrible as a public storefront or documentation hub. Searching for news, filtering updates, or finding launcher links creates massive friction. Lack of Professionalism: To offer real support, showcase features, and look trustworthy to a public audience, you need a single source of truth—not a maze of text channels lost to the void. I didn't have time to manage an overly complex custom web setup or pay high monthly SaaS fees, but I needed a clean, low-maintenance way to go public. Yes, I spent no more than thirty seconds drawing this on my Bamboo tablet: Why Google? (And the Launcher Evolution) Before even thinking about the website, I had to solve the distribution problem for my launcher. I experimented with several download pipeline prototypes: Git Repos / Diversion (f

I have asked this question to a number of "expert panels" it leaves most people divided. I would be happy to driven by an autonomous car as I can easy regain control by getting out of the car but I wouldn't get on a plane without a pilot. 50/50 on a train. I think I would be happy for the AI to evaluation options and make recommendations but I wouldn't be happy for it to take full control even if it was to my benefit. Crazy right ? submitted by /u/Babayaga1664 [link] [留言]

Apple's sound settings often meant you'd sleep through your iPhone alarm if you lowered the volume before you went to bed. With its new update, that is no more.

When is it no longer worth repairing your phone and buying a new one instead.

From cordless vacuums to the best wireless earbuds we’ve tested, some of our favorite tech is on sale for Labor Day this year.

This question gets surprisingly complicated. Using AI to fix grammar? Most people seem fine with that. Using it to brainstorm ideas? Probably fine Using it to write the first draft? Depends who you ask. Using it to do the entire assignment while you barely understand the topic? That's where most people would probably draw the line But the line keeps moving Calculators, spellcheck, Google and autocomplete were all seen as shortcuts at some point. Now they're just normal tools. Do you think AI will follow the same pattern? Or is there something fundamentally different about outsourcing actual thinking? submitted by /u/cactussignal [link] [留言]

While Apple's first foldable iPhone Ultra will headline the September 9 launch, we're also expecting news about AirPods and HomePods.

Even though it'll be really pricey, Huawei's Mate XT2 is taking one of the Galaxy S26's best features and putting it in a foldable phone for the first time.

submitted by /u/KeanuRave100 [link] [留言]

A log? A trace? The model output? A record showing that the tool call happened? All of those can tell you what happened. They don't necessarily tell you whether the agent was actually allowed to do it. That distinction gets more important as agents move from generating text to acting on real systems: sending payments, changing infrastructure, updating customer records, approving workflows, calling internal APIs. A lot of agent stacks still reduce this to identity and access. The agent has an API key. The API accepts the request. The action runs. But having credentials isn't the same as having permission for a specific action. The harder question is: Was this agent authorized to perform this action, against this target, under this policy, at that point in time? And the answer shouldn't depend on asking the agent after the fact. The authorization needs to exist before execution. It needs to be tied to what is actually being executed. And later, you should be able to verify what authorized the action. That means being able to answer fairly basic questions: who issued the authorization? Which policy was applied? What action was it tied to? Who could use it? When was it valid? Had it already been used? Was authority delegated? This is where the execution boundary becomes interesting. It's one thing for a system somewhere upstream to decide that an action is allowed. It's another to make sure that decision still applies when the action actually reaches the system that will execute it. As agents get access to production systems, "the model decided to do it" isn't going to be much of an audit answer. The question is simpler: Can you prove the agent had the authority to do it? submitted by /u/docybo [link] [留言]

How to get rejected by IEEE T-PAMI with 'Excellent' scores?[D] submitted by /u/cussealin [link] [留言]

Every guide I write ends in the same handful of questions. How much hardware do I actually need? What happens when one box dies? Are my backups real or just a feeling? A guide can walk you through a setup, but it can't do arithmetic about your lab — so I built eleven small tools that can, at peira.dev/tools . They're free, none ask who you are, and seven of the eleven keep working once the page has loaded — network unplugged, laptop in a cupboard, whatever. They share one lab profile This is the part that makes them a set rather than eleven unrelated pages. Describe your lab once — tick your services in the sizing calculator, press Save to profile — and the others pick it up. The failure simulator opens with your nodes already modelled; the backup planner knows what data you have; the power-loss playbook knows what's plugged in. Lab doc hands the whole thing back as a Markdown file. Nothing about that profile leaves your browser. No account, no sync, no server that could leak it — which is also why it doesn't follow you between devices. The Markdown export is how you carry it elsewhere. Plan the build Sizing calculator — asks what you want to run and recommends nodes, RAM, and storage. It cares most about RAM, because that's the constraint that actually bites; vCPUs overcommit happily, memory doesn't. Tick "survive one node failure" and it insists on three nodes (a two-node cluster loses quorum the moment one dies). Node failure simulator — kill a node and see which workloads fit on the survivors. It places the critical ones first and names the stranded ones. 3-2-1 backup planner — three copies, two devices, one offsite (the rule CISA recommends ). It's blunt: a snapshot on the same disk as the original is versioning, not a backup. Fix what's broken The overlay network diagnostic is a decision tree born from a miserable afternoon: a container couldn't reach a machine across a Tailscale subnet router, and three layers had to be right — the route in the guest, the ACL

One skeleton, many screens argued that admin screens should be declared as typed data rather than coded, and it ended by claiming the idea was independent of the stack: draw the boundary as a one-way dependency — domains depend inward on a framework that knows nothing about them — and validate it with a zero-diff refactor of a screen you already trust. That was React and TypeScript. This is the same claim re-run in Rust, where a descriptor can be a compile-time constant and a template is a macro. Because the first result is already published, the second stack is a replication with a control rather than a fresh opinion — which is rare enough to be worth doing properly. Companion to Topcoat and the shrinking cost of full-stack Rust . That post was written from the announcement and promised a follow-up reporting where the rough edges actually show. This is it, from the pilot that followed: a small admin panel built on Topcoat 0.6.2 and Toasty 0.10.0, and the four questions that post committed to answering. The pilot is open source — a clean clone runs both screens and the test that decides the argument. That phrase, a compile-time constant , is where the title comes from, so it is worth saying now what it buys and why I wanted it. A TypeScript descriptor is an array of objects assembled when the module loads. A Rust one can be more than that: &'static , Copy , allocated never, fully checked before the program starts. Going in, that looked to me like the same idea in a stricter form — if declaring a screen as data is good, then declaring it as data the compiler can see through and verify must be better still. I treated that property as the thing worth protecting, and the pilot was partly a test of whether it could be. The stack is deliberately a young one. Topcoat is six weeks old: Tokio's team announced it on 22 July 2026, the pilot pins 0.6.2, and the project still expects breaking changes. It is not the only full-stack Rust framework — Leptos and Dioxus have been at

I thought embedding a web UI into a native desktop application would be the easy part. After all... macOS has WebKit. Linux has GTK WebKit. Windows has WebView2. One API per platform, smaller installers, native look & feel. Sounds perfect. Then reality arrived. macOS 🍎 Honestly, this was the easiest platform. System WebKit is there. It behaves consistently. No additional runtime. No installer surprises. Exactly what you'd expect from a platform component. 10/10 Linux 🐧 Things became... more interesting. GTK WebKit works, but suddenly packaging starts to matter. An AppImage built on one distribution may refuse to start on another because some required WebKitGTK library isn't available. Your application itself is perfectly fine. The user's system just doesn't happen to provide exactly the version your build expects. You quickly discover that "works on my machine" has many regional dialects. 7/10 Windows 🪟 This one surprised me the most. Unlike macOS, the web view isn't really just "there." Using WebView2 means depending on the Edge WebView runtime. If the runtime isn't installed, congratulations—you now need another installer. So your installer may install something whose purpose is to allow your application to display HTML. Not exactly the dependency story I was hoping for. 2/10 Meeting in the middle At some point I asked myself: Why am I spending time debugging operating-system packaging instead of building my application? So I tried CEF (Chromium Embedded Framework). Yes... The application becomes larger. Quite a bit larger. But in exchange: • Same rendering engine everywhere. • Same JavaScript engine everywhere. • Same debugging experience. • Same HTML/CSS behavior. • No Linux WebKit dependency lottery. • No separate WebView runtime installation on Windows. • One code path across all desktop platforms. Ironically, shipping your own browser turned out to be simpler than relying on the browser already "provided" by the operating system. It's one of those engineering

The idea of whether machines will dominate us is generally treated as a new one in current discussions on AI risk, since in 1863 Samuel Butler published a letter titled 'Darwin Among the Machines' which made an argument closely resembling the one in today's debate. He stated that the real danger was not that machines would gain control of power, but that people would find machines so useful that it would become practically impossible to get rid of them, so that by that stage the question of who was actually in charge would have already been answered — no matter who seemed to be in control. He said that a society which, in principle, could not destroy its machines had already "created a race of beings towards whom it is beyond our power to destroy" and had thus become "acquiescent in our bondage." This is a quite different claim from the one that says 'superintelligence takes over'; it's similar to the sorts of arguments currently being made regarding algorithmic dependence, attention economy lock-in, and infrastructure that is too deeply embedded to be undone — arguments that can be applied to any technology that is sufficiently indispensable, not just to intelligent machines. The full letter, together with the historical background (it is also the immediate source of the "Butlerian Jihad" in Dune, a fact that most people are unaware of) can be found here: https://romaricjannel.substack.com/p/the-rise-of-mechanical-life I'm wondering if people here consider "acquiescence through indispensability" to be a real and separate risk category or whether it's just a rewording of dependence anxiety that appears each time a major technology is introduced. submitted by /u/Smart_Fly_5783 [link] [留言]

You open a hotel page. It looks like one product: a search grid, a featured stay, local highlights, reviews, a sticky trip summary. Under the hood it is eight HTTP servers and three UI runtimes . That is the experiment behind HarborStay , a demo booking app I built to answer a stubborn question: Can independent teams ship independent UI, in independent frameworks, and still give the browser a single, paint-ready HTML page? The punchline: yes — if the shell never imports a component. It only fetches HTML. The one rule The browser never talks to a fragment. It talks to the composer on port 3100 . The composer owns routes, layout, and the booking flow. Everything else is a fragment server that returns a chunk of HTML. flowchart LR Browser["Browser"] --> Composer["Composer :3100"] subgraph fragments["Fragment servers"] Nav["Navigation Marko :3101"] Search["Hotel search Marko :3102"] Details["Hotel details Marko :3103"] Reviews["Reviews Marko :3104"] Recs["Recommendations Marko :3105"] Highlights["Local highlights React :3106"] Disco["Experiences discovery Riot :3107"] Itin["Experiences itinerary Riot :3108"] end Composer --> Nav Composer --> Search Composer --> Details Composer --> Reviews Composer --> Recs Composer --> Highlights Composer --> Disco Composer --> Itin Composer --> CDN["CDN :3200"] This is the opposite of the usual microfrontend story (Module Federation, shared React, a host that import() s widgets). HarborStay is HTML composition . The shell does not know whether a fragment was rendered by Marko, React, or a hand-rolled Riot string. It only knows a URL. That one constraint buys a lot: Fragment teams can pick a runtime without asking the shell. A fragment outage becomes a fallback box, not a blank page. You can deploy search without redeploying reviews. It also forces honesty. If two fragments need to share a Redux store, the architecture is already leaking. What the user actually sees HarborStay models a small premium catalog: Harbor View Lodge in Lisbon

In two weeks, I built Retro Dynamics Agent, an app that generates retrospective activities for teams, facilitates them on a real-time collaborative board, and turns the outcomes into Jira or Azure DevOps tickets. I built it working with an AI coding agent, Claude Code, throughout almost the entire process: design, implementation, production debugging, and documentation. I do not want to tell another “I used AI and it wrote the code for me” story. We have heard that one enough. What I found more interesting were the parts of the project where there was no obvious answer in a tutorial, and how the work was divided in those situations. I defined the constraints and made the underlying decisions. The agent proposed concrete technical solutions and implemented them. Then the responsibility for verifying that everything actually worked, not just that it compiled, came back to me. Here are three examples from the project. 1.- Connecting to Jira without server-side sessions or frontend memory I wanted any team to be able to connect its own Jira account through OAuth, instead of relying on a global token that only I could configure. The problem was that my application runs entirely on serverless functions. Nothing stays in memory between requests, and the frontend does not maintain its own state either. No localStorage. No router. An OAuth login means leaving the application, authenticating with Atlassian, and then coming back. But coming back to what, if nothing remembers which screen you were on? Before touching the code, I asked the agent to create a complete implementation plan, including the files that would need to change, the design decisions, and the scope. I reviewed that plan as if it were a pull request from another developer. I made decisions such as: For now, only Jira would use OAuth. Azure DevOps would keep its manual token flow because setting up OAuth there is considerably more involved. Tokens would be encrypted before being stored in the database, never sa