标签：#AR

For enterprise AI/security teams, what would make AI SPM useful rather than just another category label? Is the important part AI asset inventory data access mapping, agent permissions, prompt/data monitoring, policy enforcement or something else? submitted by /u/Efficient_Western609 [link] [留言]

Welcome back to TechCrunch Mobility — your central hub for news and insights on the future of transportation.

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If you’ve been following my previous posts, you know I’m a big advocate for Trunk-Based Development and shrinking your pull requests until they almost feel too small. In a perfect world, developers merge code directly into the main branch multiple times a day, everything flows smoothly, and production remains rock solid. But let’s be honest. When you actually try to pitch this to a backend team working on a core system, you almost always hit the exact same wall of resistance. Someone in the back of the room will inevitably raise their hand and ask: “That sounds great in theory, but I’m currently refactoring our legacy checkout service. It’s going to take me four days of deep architectural changes. Are you seriously telling me I should merge half-baked, broken code into the main trunk and push it straight to production where real customers are buying our products?” It’s a completely valid objection. If your only tool for hiding uncompleted work is holding onto a massive, long-lived feature branch, then trunk-based development breaks down immediately. You end up with the exact nightmare we talked about earlier: huge code reviews, painful merge conflicts, and code that rots before it ever sees a live environment. To make continuous delivery actually work without causing catastrophic production outages every single afternoon, you need to decouple two concepts that most engineering teams mistakenly treat as the exact same thing: Deployment and Release . Last article in this category is focused on Trunk-Based Development: https://codecraftdiary.com/2026/05/18/trunk-based-development-roadmap/ The Core Concept: Shifting Left by Decoupling In traditional development setups, deploying code and releasing a feature happen simultaneously. You merge your giant feature branch, the CI/CD pipeline runs, the code hits the live servers, and boom—your users immediately see the new functionality. This model is incredibly high-stakes. If something goes wrong, your only options are rollin

it turns into some unfinished project lying in some GitHub private repo lol submitted by /u/Complete-Sea6655 [link] [留言]

If you ask it to create a recipe, you can click plus and minus buttons to change the amount of portions. You can connect it to other apps like canva. It hallucinates WAY less, and it explains ilvery clearly. submitted by /u/OkComputer_13 [link] [留言]

I have been experimenting with LLMs to see how well they navigate highly cross-referenced texts like the Bible. Standard models often hallucinate verses or lose historical context. To try and fix this, I built a free app called Biblians (no ads, no paywalls). I built it specifically for people who have questions they might hesitate to ask in person, or who simply want a 1-click way to explain a verse. While testing it, I discovered a fascinating denominational bias that is still lingering and changes depending entirely on the language you use: In English: It is Protestant-leaning. It praises Luther, saying things like, "Martin Luther sought to return the Church to the truth of God's Word." In Spanish, French, or Portuguese: It is Catholic-leaning. It condemns Luther's actions, stating: "...trajo confusión..." (...brought confusion...). Has anyone else noticed how drastically the training data changes the core bias based on the language prompted? I would love for this community to test the app, look for other linguistic biases, or just try to break the AI's logic. You can experiment with it here: https://play.google.com/store/apps/details?id=com.biblians.app Let me know what weird outputs you get! submitted by /u/Snorlax_lax [link] [留言]

May 25, 2026. This is not just another date on my calendar. This marks the beginning of one of the biggest professional transitions of my life. After nearly a decade working in the world of IT infrastructure, technical support, networking, field engineering, and systems operations, I’ve made a decision that has been building in my mind for some time: I am transitioning into Data Analytics. And this is where I document that journey—publicly, honestly, and in real time. Not when I become an expert. Not when I feel “ready.” Not when everything looks polished. I’m starting now. Because real growth is rarely clean, predictable, or perfectly planned. Sometimes it starts with one uncomfortable decision: To leave what you already know… and step into what your future requires. Where My Journey Started Before data, before dashboards, before writing my first SQL query or building my first analytics project—my career started in the trenches of IT. For the past 10 years, I’ve built my career solving real technical problems across businesses, organizations, schools, offices, and field operations. My world has been cables, routers, networks, system failures, installations, troubleshooting, and making technology work where others saw complexity. Over the years, I’ve worked deeply in: Computer troubleshooting and hardware diagnostics Printer setup, configuration, and enterprise support Wi-Fi deployment and hotspot installations LAN design and structured network deployment Fiber optic installations and network termination Data cabling and structured cabling systems CCTV surveillance installation and maintenance Alarm systems and electronic security integration Intelligent security systems Electric fence installations and perimeter protection systems Router, switch, and access point configuration End-user support and enterprise technical troubleshooting Systems maintenance and operational support I’ve spent years on ladders, in server rooms, inside offices, on construction sites, insi

OK.. so I've had this reasonable PC sitting under my desk for ages.. NOT working because of some reason or other. But it was my baby as is housed in a lovely Soprano DX silver brushed case. SO, I swapped out the old HDD for a couple of SSDs (a couple of mirrored OS disks and a large 2TB storage disk) I swapped out the Nvidia 780ti graphics card for a couple of OG Nvidia 1080ti's. I pulled the whole thing to bits.. repasted the northbridge chip, southbridge chip and central CPU. Upgraded the fans to push pull the CPU heatsink. Wrapped ALL cables in mesh and it's so lovely now. Installed Windows 10 Pro. Installed the Nvidia App. Installed CrystalDiskInfo and all is sweet 😄 EXCEPT... I'd like to use this old bangin box for an HG AI server... now I have read that ALL LLMs need this thing called AVX (Advanced Vector Extensions) I didn't even know that was a THING! So even though I have 22Gb worth of GPU sitting there that I was going to point everything to, because I have a lame ass QX6700 CPU sitting on a kickass D975XBX2 (BadAxe2) main board I CAN NOT fulfill my wish for this OG box to be a headless source of awesomeness sitting in it's home under my desk supplying me with a home grown AI. IS THERE ANYTHING I CAN DO?!?!?! Surely after all this time of parts getting munched by AI farms a plenty people have been using what's around to do what they will... Does anyone know of anything I can do apart from just look at it running at 25 degrees aircooled humming along so lovely... it NEEDS purpose!!! 😄 Cheers and thanks all NB submitted by /u/Independent-Sound196 [link] [留言]

10 year senior dev, vibe coded this in 4 weeks and counting. Something like this would have taken me a year+ before and ive always been a 10x dev. I built this along side my day job (gov contractor dev). Feel free to check it out! https://imgur.com/a/F6OINKR⁠ Game Title: Roguelite MMO Playable Link: https://roguelite-mmo.com/⁠ Platform: PC / Web Description: Roguelite MMO is a browser-based RPG/MMO project built around dungeon runs, exploration, gear progression, PvP, quests, loot, and character building. The game is still in beta and active development, with the latest update adding new side activities and progression options. Latest update: The new Casino is now live, giving players more ways to spend gold, take risks, and chase rewards between dungeon runs and exploration. Horse racing and horse taming have also been added. Players can race horses, bet on races, and work toward collecting better horses over time. Fishing is now available too, adding a more relaxed activity with its own rewards while exploring the world. The core loop is still being refined, but the current focus is making sure players understand what they earned, where important items come from, what to do next, and whether the early gameplay loop feels worth continuing after the first few minutes. Free to play submitted by /u/HeadHunterX223 [link] [留言]

Constantly being plugged into the news grind is mentally exhausting. Sometimes we just need to take a break, unwind, and do something fun. That’s why we’ve built up a collection of distracting time-wasters for when we need a break from being obsessively online. We figured you might enjoy these harmless rabbit holes, mildly addictive browser […]

I had a work version of GPT do a very simple spreadsheet summary task for me yesterday. It took it 5 minutes to do it. I could probably have done it myself in 30 or so minutes. The heavily subsidised token cost of that task? 10 dollars. That's with a 10x subsidy. The actual compute cost was about 100 dollars. There's something seriously wrong there. It's going to crash and crash HARD. if people think i'm lying or are just interested. The spreadsheet had 45 sheets. Each sheet had roughly 500 x 50 populated cells. Formatting was not exactly standard across all sheets. The prompt was something like "there is labelled column in each sheet, give me a simple list of all the items from all the sheets in that column and ignore duplicates." We can chose which model to use. The model I chose was one of the newer ones, I honestly can't remember which one, possibly GPT 5.5. It took 5 minutes or more to so and the stated cost for the task was 10 dollars, possibly even more. I can't recall the token amount. submitted by /u/Complete-Sea6655 [link] [留言]

Over the past few months I built an AI-assisted delivery framework — not to write code faster, but to eliminate ambiguity across the entire software development lifecycle. The result completely changed how I think about AI in engineering. The problem I kept hitting Every time I used AI to generate architecture docs, API contracts, or implementation plans across separate sessions, the outputs looked great in isolation. But viewed together? They were broken. A pivot in the system architecture was never reflected in the API contracts. Frontend assumptions silently diverged from backend data models. AI wasn't the problem. Treating it as a collection of disconnected prompt sessions was. What I built instead A governance-driven framework built on three layers: Prompt → Agent → Skill The Prompt captures intent only — lightweight, declarative The Agent orchestrates execution and decides which capabilities to invoke The Skill is a reusable, schema-validated execution block with hardcoded governance rules This connects every delivery artifact into a sequential dependency chain: Business Requirements ↓ System Architecture ↓ Data Architecture ↓ Event Architecture ↓ API Contracts ↓ Implementation Plans ↓ Backend / Frontend Implementation Each artifact consumes the one before it. Upstream changes automatically propagate downstream. Governance is enforced at the Skill layer — not buried in fragile prompts. The finding that surprised me most The highest-leverage use of AI wasn't code generation. It was context generation . When engineers — or downstream agentic workflows — were given a governed, unambiguous spec, implementation quality was consistently higher than any raw AI-generated code output. The context was the unlock, not the syntax. What failed I'm including this because most write-ups skip it: Over-orchestrating everything (not every workflow needs an agent loop) Prompt bloat as a substitute for real architecture Severely underestimating token costs at scale Believing full

so i kept using chatgpt to ask questions about my pdfs and notes, and half the time i couldn't tell if it actually read the doc or just made something up that sounded right. that bugged me enough to build my own thing over the last few weeks. you upload a pdf (or word, csv, image, or just paste a link), ask whatever you want, and it answers using only what's in your file - and it shows the exact page it pulled the answer from, so you can check. if the answer isn't in the doc, it just tells you instead of guessing. stuff i actually end up using: flip on web search when i want it to look something up online instead one click to turn a doc into a summary / key points / flashcards (this is clutch for studying) resume review + cover letter help you can talk to it and it reads the answer back it's completely free, i'm not selling anything. honestly just want people to break it and tell me what's missing. link: https://athena-wisdom.vercel.app (there's a short guide on the site too if you get stuck) solo project so be gentle lol - but real feedback is what i'm after, especially what you'd want it to do next. submitted by /u/Independent_Diver352 [link] [留言]

If you have ever submitted an iOS application to Apple's App Store and received a cryptic rejection notice about icon specifications, you are not alone. Apple's human interface guidelines for icons are extraordinarily precise — and for good reason. The iOS ecosystem spans devices from the tiny Apple Watch to the expansive iPad Pro, each requiring icons at exact pixel dimensions to render correctly across Retina, Super Retina XDR, and ProMotion displays. Understanding iOS icon sizes is not optional. It is a prerequisite for shipping. Every pixel dimension you provide must match Apple's specifications exactly, must use lossless PNG format, must not include transparency, and must be delivered with the exact filename that Xcode expects. One missed size, one wrong filename, and your project fails to build correctly. Precision is not a suggestion — it is a hard requirement enforced by Xcode's build system. iPhone Icon Sizes For iPhone applications, the required icon sizes span multiple uses within the operating system. The App Store listing requires a 1024×1024 pixel icon. The home screen displays icons at different sizes depending on device generation and display density. Notification icons, Spotlight search results, and Settings app icons all require their own specific dimensions. Usage Scale Size (px) Filename Convention App Store 1× 1024×1024 Icon-1024.png Home Screen 2× 120×120 Icon-60@2x.png Home Screen 3× 180×180 Icon-60@3x.png Spotlight 2× 80×80 Icon-40@2x.png Spotlight 3× 120×120 Icon-40@3x.png Settings 2× 58×58 Icon-29@2x.png Settings 3× 87×87 Icon-29@3x.png Notification 2× 40×40 Icon-20@2x.png Notification 3× 60×60 Icon-20@3x.png iPad Icon Sizes iPad adds its own set of required sizes, particularly because of the larger screen real estate and different display densities. Xcode's asset catalog system requires each icon to be placed in the correct slot, and any missing slot will prevent archiving for App Store submission. This makes completeness not just a best p

In the previous article, we explored concepts such as total loss and epochs. Now, we will continue...

🚀 SpaceX signed a massive $920 million monthly deal with Google for 110,000 Nvidia chips — this is a huge infrastructure play ahead of their monster $1.7 trillion IPO. 🏛️ The Trump administration is discussing taking equity stakes in top AI firms — this would make the public official partners in the upside of AI-driven economic growth. 🔓 Meta's automated AI support was hacked to take over high-profile accounts — it proves that offloading critical security tasks to AI can create dangerous, easily exploited vulnerabilities. 🧠 Tech workers are trading hours of manual labor for high-level strategy thanks to AI — while tasks now take minutes, humans are still needed for crucial, complex decision-making. submitted by /u/Ok_Muffin_7347 [link] [留言]

Sigma’s new entry is both a bold design experiment and a pretty decent camera.

These 5- to 6-inch blades are the secret workhorses of the kitchen, whether you’re a smaller person, have smaller hands, or just desire a smaller but still high-functioning knife.

Just shipped this project and wanted to share the full technical breakdown because hotel/hospitality AI doesn't get much attention compared to the usual chatbot and SaaS use cases. The client manages 500 hotel properties. Their support team was manually handling around 15,000 guest emails per day. Same questions over and over across hundreds of hotels but each one still needed a human to read it, understand it, find the answer, and reply. Here's how the system works end to end: Layer 1: Email ingestion and question extraction This was the hardest part. Guest emails are messy. A typical one looks like: "Hi there, we're coming for our anniversary on the 20th and I was wondering if you have any room upgrades available. Also is the spa open to guests or do we need to book separately? We're driving so need to know about parking too. Last time we stayed the wifi was a bit slow in our room, has that been fixed? Thanks!" That's four separate questions plus a complaint wrapped in one email. If you just embed the whole thing and search the FAQ database you get a blended result that partially answers one or two questions and misses the rest. So I built an extraction layer that reads the full email and breaks it into individual questions. It handles directly stated questions ("is the spa open?"), implied questions ("we're driving" implies they need parking info), complaints that need acknowledgment but aren't FAQ-searchable ("wifi was slow"), and informational context that shouldn't be treated as a question at all ("coming on the 20th"). Getting this extraction reliable was probably 40% of the total development time. Layer 2: FAQ knowledge base with vector search All hotel FAQs get embedded and stored in a vector database. Different properties have different amenities, policies, and details so the search is scoped per hotel. When a guest emails the Berlin property asking about breakfast, it searches the Berlin FAQ, not the Munich one. Each extracted question from Layer 1 gets s