标签：#AR

Soccer officials already rely on cameras to see who’s offside and who sent the ball out of bounds. But during this World Cup, refs will use digital twins of each player to view plays from every angle.

It’s a tale of two nuclear industries. In China, large reactors are coming together at a stunning pace. The country has nearly doubled its nuclear fleet since 2016, reaching nearly 60 gigawatts of total power capacity. The new facilities are nearly all gigawatt-scale pressurized-water reactors. Meanwhile, the US has built just two reactors in that…

Most people studying for Security+ use practice questions the wrong way. They take a 90 question set, score a 74, feel bad, take another set the next day, score a 76, and call that progress. Two weeks later the number has barely moved and they have no idea why. The score is the least useful thing a practice exam gives you. What you actually want is a map of what you do not know yet. Here is the approach that worked for getting through SY0-701 without burning out on endless question sets. Start cold, on purpose Before you study a single domain, take a full practice exam and do not look anything up. It will feel bad. That is the point. A cold score tells you where you actually stand, not where your notes say you should be. SY0-701 is split into five domains, and they are not weighted evenly: 1.0 General Security Concepts (12%) 2.0 Threats, Vulnerabilities, and Mitigations (22%) 3.0 Security Architecture (18%) 4.0 Security Operations (28%) 5.0 Security Program Management and Oversight (20%) Domain 4 alone is more than a quarter of the exam. If you bomb Security Operations and ace General Concepts, splitting your time evenly between them is a mistake. A cold diagnostic shows you that split in about an hour. If you want one to start with, there is a free diagnostic exam at secplusmastery.com/diagnostic that breaks your result down by domain so the holes are easy to see. Review the wrong answers, and the right ones too This single habit moved my scores more than anything else: for every question I missed, I wrote down why each wrong option was wrong, not just why the correct one was correct. Security+ loves distractors that are real terms used in the wrong context. A question about a control that prevents an attack will offer you a control that detects one, and a control that corrects after the fact, all as plausible answers. If you only learn that the answer was C, you learn nothing you can reuse. If you learn that B was a detective control and the scenario asked for a p

Ask a large language model for a specific statistic, then ask where it found that number. More often than not, the citation it gives you doesn't exist. The model will hallucinate a plausible-looking reference, confidently present outdated conclusions, or simply make things up without any internal signal that something is wrong. This failure mode has a well-known name — hallucination — and the most widely adopted engineering solution for it is RAG. RAG in One Sentence RAG stands for Retrieval-Augmented Generation. The idea is straightforward: before the LLM generates an answer, retrieve relevant document chunks from an external knowledge base, then feed those chunks to the model as context so it can compose its response based on real source material rather than parametric memory alone. Think of it like writing a research paper. You don't cite statistics from memory; you look them up first, then write your argument around verified data. RAG gives language models the same "look it up, then write" workflow. Three Structural Limitations of LLMs To understand why RAG is necessary, we need to identify the specific gaps it fills. Knowledge cutoff. Every model has a training data deadline. GPT-4's cutoff is late 2023; Claude's is early 2025. Anything that happened after that deadline simply doesn't exist in the model's world. It will either admit ignorance or, more dangerously, fabricate an answer that sounds current. Bounded parametric capacity. Even a 100-billion-parameter model can only "memorize" so much. Long-tail facts, niche domain knowledge, your company's internal documentation, yesterday's meeting notes — none of these are in the weights. No built-in fact-checking. Token generation is probabilistic sampling. The model has no mechanism to distinguish whether it's recalling a training fact or pattern-matching its way into a plausible-sounding fiction. RAG addresses all three: it supplies up-to-date, verifiable, externally sourced evidence at inference time. How RAG W

I'm really interested in trying to learn about this new concept of having a one central AI-powered database acting as a digital brain for your business, pulling in all of the various data sources and having one single source of truth. People like Nate B Jones talk about it and I really want to try to build something - but concious how wrong they can go. Are there any credible ones already build I can base off? Has anyone done this? submitted by /u/zascar [link] [留言]

link - https://arxiv.org/abs/2606.06158 Abstract : Adaptive video tokenisation seeks to dynamically allocate token budgets based on the underlying visual complexity of a sequence. Current continuous-regime approaches achieve this via iterative binarised searches or trained neural regressors, while discrete methods often require a full-rate decoder pass to estimate information content. We demonstrate that such computational overheads are not strictly necessary. We show that the latent space of a frozen continuous video tokeniser inherently encodes temporal redundancy that can be exploited directly: spatial positions whose latent representations change minimally between consecutive frames carry near-zero additional information. We introduce a parameter-free adaptive token allocation mechanism that applies a fixed threshold to per-position temporal-L1 differences, identifying and dropping redundant latent positions. Consequently, the compression rate emerges naturally from the input content rather than being enforced top-down: static scenes get compressed aggressively, while highly dynamic sequences retain more tokens. To reconstruct the dropped positions, we propose the Latent Inpainting Transformer (LIT), a lightweight factorised spatial-temporal attention architecture. The resulting inference pipeline is highly efficient, requiring only a single encoder pass and one LIT forward pass, eliminating the need for auxiliary routing networks. Evaluations across TokenBench and DAVIS, which are the standard benchmarks used by recent tokenisers, indicate that our framework yields meaningful, content-driven token allocation while maintaining competitive reconstruction fidelity, and delivers a 31x inference-time speedup over the continuous adaptive baseline (ElasticTok-CV) and an 2x speedup over the discrete information-theoretic baseline (InfoTok) submitted by /u/chhaya_35 [link] [留言]

OpenAI's GPT-5.5, GPT-5.4, and Codex are now generally available on Amazon Bedrock, one month after OpenAI revised its exclusive Azure arrangement. Pricing matches OpenAI's direct rates with usage counting toward AWS commitments. Codex shifts to pay-per-token billing with no seat fees. GPT-5.4 is the first OpenAI model available in AWS GovCloud. By Steef-Jan Wiggers

Cindy Zhang discusses the evolution of XDS, a unified UI system powering 10,000+ internal tools. She shares actionable insights for architects and engineering leaders on managing large-scale community contributions, executing safe monorepo refactors using JS AST and AI codemods, mitigating breaking changes via feature flags, and expanding UI libraries into full-stack platform systems. By Cindy Zhang

Customers have complained that a telehealth network selling compounded GLP-1s has been ripping them off—even after it had to pay $5 million to clients as part of a settlement with the US government.

From Wired: “We’re changing Fable 5’s safeguards for frontier LLM development to make them visible.” Anthropic said in a statement to WIRED. “We made the wrong tradeoff and we apologize for not getting the balance right.” Anthropic now says it’s changing course, and that Claude Fable 5’s safeguards for AI development will be visible to users. If the company suspects a user is trying to use Claude to build a highly capable AI it will alert them that it’s either refusing the request, or rerouting the user to a less capable model. Full article: https://www.wired.com/story/anthropic-responds-to-backlash-on-claudes-secret-sabotage-on-ai-research/ submitted by /u/goldcakes [link] [留言]

ACL ARR May 2026 reviews are due on July 2. I do not see any reviewer assignement as of today. Will the review period be just 2 weeks in that case? Anyone got papers assigned for reviewing? submitted by /u/Impossible-Garden612 [link] [留言]

TL;DR — We'll install dskripchenko/laravel-api , write one controller, and end up with a versioned API ( /api/v1/... ) and interactive OpenAPI 3.0 docs at /api/doc — generated from the docblock you'd write anyway. Then we'll ship a v2 without copy-pasting a single controller. The problem Two things rot in every growing Laravel API: Versioning. v1 ships, then v2 needs to change three endpoints but keep the other twenty. You either copy-paste a V2 folder (and now bugfixes live in two places) or bolt if ($version === 2) branches into your controllers. Docs. The OpenAPI spec drifts from the code the moment you merge. Annotation libraries ( #[OA\Get(...)] , giant YAML files) ask you to describe your API twice — once in code, once in attributes. This package's bet: your controller already describes itself . The method name, the request fields, the response shape — write them once, as a normal PHPDoc, and let the package derive routes and docs from it. Versioning becomes plain PHP inheritance. Let's build it. What we'll build A tiny tasks API: POST /api/v1/task/list — list tasks POST /api/v1/task/create — create one interactive docs at GET /api/doc (raw spec per version at /api/doc/{version} ) then a v2 that adds an endpoint without touching v1 Total: ~4 small files. Step 0 — Install composer require dskripchenko/laravel-api Publish the config (optional, but handy to see the knobs): php artisan vendor:publish --tag = laravel-api-config // config/laravel-api.php return [ 'prefix' => 'api' , // → /api/... 'uri_pattern' => '{version}/{controller}/{action}' , 'available_methods' => [ 'get' , 'post' , 'put' , 'patch' , 'delete' ], 'openapi_path' => 'public/openapi' , 'doc_middleware' => [], // lock down /api/doc here ]; Step 1 — Write a controller Nothing exotic — it extends the package's ApiController , which gives you response helpers ( success() , error() , validationError() , created() , noContent() , notFound() ). The docblock is the documentation : <?php namespace App\Api

Most developers never have to design a network protocol from scratch. You use HTTP, gRPC, WebSockets, or something else that already exists and has been debugged by thousands of people over many years. That is the right call for most situations. I did not take that path when building Vaylix, a key-value database engine. I designed a custom binary protocol called VTP2, and the process taught me things about networking that I would not have picked up any other way. This is not an argument that you should also build a custom protocol. For most things, you should not. This is an honest account of what I ran into. Why not HTTP The first question anyone reasonably asks is: why not just use HTTP? HTTP is everywhere. The tooling is excellent. Every language has a client. Debugging with curl is trivial. If I had used HTTP, I would have had working client libraries in a dozen languages before writing a single line of server code. The problem is that HTTP is stateless by design. Every request is independent. Every request carries headers. Every response carries headers. The model assumes that each round trip is a fresh conversation with no memory of what came before. A database session is the opposite of that. A client connects, authenticates, and then issues many commands over the same connection. The authentication should happen once. The session should carry state. Pipelining requests without waiting for each response to return should be natural, not something you fight the protocol to achieve. HTTP/2 closes some of this gap. But using HTTP/2 correctly for a stateful session model involves working against the grain of what HTTP was designed for. I would have been spending a lot of time on infrastructure that exists to make HTTP behave less like HTTP. The other issue is overhead. HTTP headers are verbose. For small key-value operations, the headers can easily exceed the payload. That felt wrong for something designed to be a tight operational data store. So I went with TCP d

My daughter Emma is 11. She's been vibe coding lately, and honestly, she's pretty good at it. The...

anthropic just released fable 5 two days ago and i haven’t had a chance to properly dig in yet for context it’s basically a public version of mythos, the model they’d been keeping locked behind project glasswing for select partners only. now it’s out for everyone on pro/max/team plans until june 22 for free, after that it’ll need usage credits from what i’ve read it’s supposed to be insane at long agentic tasks… like multi-hour sessions where it spins up sub-models, gathers data, writes and tests its own code. someone gave it one prompt to build a travel-time map and it went off on its own for hours and just… built it the one catch is it has hard safety blocks in areas like cybersecurity, bio, chem. falls back to opus 4.8 when it hits those but i want to hear from people actually using it right now. what’s the best thing you’ve noticed? and what feels overhyped or still rough? drop your experiments in the comments, genuinely curious submitted by /u/NewMuffin3926 [link] [留言]

From personalized gifts to business essentials, WIRED can help you save with our selection of VistaPrint promo codes.

Hi, I want to use AI to check my grade with the mark scheme and see what grade it would give me. Now, after doing this, would the assignment be flagged by an AI detector? submitted by /u/No-Witness1045 [link] [留言]

submitted by /u/ThereWas [link] [留言]

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

The decision comes as India emerges as the world’s largest GCC market.