标签：#AR

Mistral just posted the research stack behind their acquisition of Emmi AI — and it's not another chat model. They're building neural surrogates that replace or accelerate the kind of computational fluid dynamics (CFD) simulations that currently eat weeks of supercomputer time. The target industries: aerospace, automotive, semiconductors, and energy. The pitch: foundational Physics AI that lets engineers build faster and gain continuous performance gains at scale. "We are doubling down on building foundational Physics AI for the industries that shape the physical world." What actually changed The Emmi acquisition brings a serious body of published research into Mistral: AB-UPT (Feb 2025) — Anchored-Branched Universal Physics Transformer. Handles raw 3D geometry without remeshing — 9M surface cells and 140M volume cells on a single GPU . Previously that kind of simulation required a cluster. UPT (Feb 2024) — Universal Physics Transformer. A general framework for scaling neural operators across diverse spatio-temporal problems, supporting both grid and particle simulations. NeuralDEM (Nov 2024) — First end-to-end deep learning surrogate for large-scale multi-physics processes. Enables real-time simulation of industrial processes like fluidised bed reactors. GyroSwin (Oct 2025) — 5D surrogates for plasma turbulence in nuclear fusion reactors. Addresses one of the key blockers for viable fusion power. 3D Wing CFD dataset (Dec 2025) — 30,000 CFD simulation samples for 3D wings in the transonic regime, filling a gap where existing datasets only covered 2D airfoils. What this actually means Most AI labs are competing on language, code, and reasoning. Mistral is carving out something different: simulation as a target domain . The moat here isn't a bigger transformer — it's domain-specific architecture work (AB-UPT, GyroSwin) built on years of physics-informed ML research, plus proprietary datasets that are genuinely hard to replicate. A 30,000-sample CFD dataset for transon

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

Stop fumbling for cables in the dark. These WIRED-tested stands and pads will take the hassle out of refueling your phone, wireless earbuds, and watch.

South Korean chip startup Xcena is betting that AI's real bottleneck is not compute, but memory.

Mallika Rao discusses the hidden risk of evaluation debt in production AI systems, drawing on her experience at Twitter, Walmart, and Netflix. She explains why traditional metrics fail modern architectures, breaks down a five-layer evaluation stack spanning infrastructure and UX, and shares a diagnostic maturity model to help engineering leaders eliminate silent semantic failures. By Mallika Rao

The Cloud Native Computing Foundation has highlighted a new AI-assisted migration approach that enabled engineers to migrate 60 ingress-nginx resources to Higress in roughly 30 minutes, demonstrating how artificial intelligence is increasingly being applied to modernize Kubernetes networking and gateway infrastructure. By Craig Risi

How much of the content in Reddit is AI generated do you think? submitted by /u/Zealousideal_Can_411 [link] [留言]

Hey everyone, A while ago, we realized a major annoyance: whenever you actually need an AI to summarize a document, write some quick code, or just brainstorm, you're usually on a flight, on the subway, or dealing with terrible cell reception. And bam, ChatGPT won't connect. Plus, there's the growing privacy concern of feeding all your personal data to cloud servers. So, my team and I started tinkering with a question: "What if we just run the AI directly on the phone's hardware?" We've been spending our evenings and weekends for months trying to make this work smoothly, and the result is Cortex AI. The logic is super simple: You download a highly optimized, small-scale local model (from our library) straight to your device. Put your phone in airplane mode, go off the grid—the AI replies entirely locally. Zero data leaves your phone. 100% private. Some real-world use cases we built this for: Coding help or summarizing offline docs while on a long flight. Getting quick answers while traveling abroad without an expensive data roaming plan. Brainstorming private ideas you just don't want OpenAI or Google to scrape. Note: We do have an optional "Online Mode" if you want to connect to massive models like GPT-4 or Claude, but the local offline models are completely free, and that's what we really want to test right now. We're currently trying to gather real user experiences on the local execution side. I'm not here to just spam a link and grab cash; we genuinely want to improve the offline mobile AI space. If anyone frequently travels, camps, or just loves local LLMs, we'd be super grateful if you could test it out. Brutally honest feedback like "runs too slow on my device," "needs X feature," or "this part of the UI makes no sense" is exactly what we need right now :) submitted by /u/Virtual_Ad_6024 [link] [留言]

Hey everyone, A while ago, we realized a major annoyance: whenever you actually need an AI to summarize a document, write some quick code, or just brainstorm, you're usually on a flight, on the subway, or dealing with terrible cell reception. And bam, ChatGPT won't connect. Plus, there's the growing privacy concern of feeding all your personal data to cloud servers. So, my team and I started tinkering with a question: "What if we just run the AI directly on the phone's hardware?" We've been spending our evenings and weekends for months trying to make this work smoothly, and the result is Cortex AI. The logic is super simple: You download a highly optimized, small-scale local model (from our library) straight to your device. Put your phone in airplane mode, go off the grid—the AI replies entirely locally. Zero data leaves your phone. 100% private. Some real-world use cases we built this for: Coding help or summarizing offline docs while on a long flight. Getting quick answers while traveling abroad without an expensive data roaming plan. Brainstorming private ideas you just don't want OpenAI or Google to scrape. Note: We do have an optional "Online Mode" if you want to connect to massive models like GPT-4 or Claude, but the local offline models are completely free, and that's what we really want to test right now. We're currently trying to gather real user experiences on the local execution side. I'm not here to just spam a link and grab cash; we genuinely want to improve the offline mobile AI space. If anyone frequently travels, camps, or just loves local LLMs, we'd be super grateful if you could test it out. Brutally honest feedback like "runs too slow on my device," "needs X feature," or "this part of the UI makes no sense" is exactly what we need right now :) submitted by /u/Virtual_Ad_6024 [link] [留言]

The alert was raised on May 5. Four health-care workers in the Ituri Province of the Democratic Republic of the Congo had died from an unknown illness within four days. Rapid response teams were sent to investigate, and tests at a research center in Kinshasa revealed the culprit: the Bundibugyo virus, one of the viruses…

submitted by /u/Doug24 [link] [留言]

I'm one of those people who enjoy on planes and in public spaces in general. But the issue of privacy was always a hurdle.

uber blew through its entire 2026 AI budget by april, 4 months in. 95% of their engineers use AI, 70% of commits are AI driven, and their COO still said he cant draw a clear line between all that usage and actually shipping more useful features. microsoft and duolingo have pulled back too. at the same time theres a CEO survey going around (oliver wyman) where the share planning to cut junior roles jumped from 17% to 43% in a year, and only 27% said their AI ROI met expectations, down from 38%. what gets me is the combination. companies are trimming entry level headcount because AI can do junior tasks, but juniors are also how you grow seniors. if that pattern holds for a few years the mid and senior pipeline gets thin right when the current seniors age out. cutting the bottom rung while the ROI is still unproven seems like a weird bet. anyone seeing this play out where they work? sauce: https://finance.yahoo.com/sectors/technology/articles/ubers-coo-says-getting-harder-050841491.html submitted by /u/PROfil_Official [link] [留言]

Spoiler: not a single visible cable, not a single piece of furniture moved twice. When I started, I had an apartment and dimensions from the building blueprint. No designer. No clear idea where to go. But there was a desire to make something that would turn a standard apartment in a high-rise into a place of power — a place comfortable to live and work in. Instead of a designer, I took Claude. How it all began The first conversation wasn't about furniture or wallpaper. It was about direction . I didn't know what I wanted. I knew what I didn't want — kitsch, heavy classics, excessive decoration. We worked through options together. Scandinavian minimalism. Japanese wabi-sabi. Loft. Modern classic. The AI broke down each style by character, materials, color logic. Not "this would suit you," but "here's what this means, here's what this requires, here's what you'll get." In the end I arrived at Scandinavian for the bedroom . Warm, light, calm, with one deliberate accent behind the headboard. The living room–kitchen — loft with a red thread running through the whole space, because the furniture there was already concrete-grey with red niches and replacing it wasn't on the table. The hallway and corridor — neutral grey , as a transition between two characters. Three zones, three moods, one logic. The bedroom This was the most detailed conversation. A room with one window, one door, three free walls. Together we came up with: an accent wall behind the headboard with golden geometric lines, the other three walls in cream from the same collection. Tone on tone, different saturation, same texture. The seam between walls reads not as a boundary but as gradation. White matte furniture with black hardware. A wardrobe with a top cabinet almost to the ceiling. Mirrored doors reflect the accent wall — the golden lines are present even where they physically aren't. Then came the centimeters. The AI calculated . Adding up wardrobe depth, gaps, bed width, nightstands, dresser. Checkin

To get to $600, Apple and HP made different compromises. If you demand more power and performance, the HP Omnibook 3 delivers.

Consoles with disc drives are the easiest way to enjoy all kinds of physical media, but that could end with the next-gen PlayStation 6 and Microsoft's Project Helix.

Built like a tank, the endlessly capable GoRuck GR1 is as close to a do-it-all bag as you can get.

Pope Leo XIV’s new encyclical on artificial intelligence includes a statement that warrants serious attention from technologists and policymakers: “Technology is never neutral.” Magnifica Humanitas (“Magnificent Humanity”) is a clarion call to all people to act with courage and solidarity as we enter an age already being transformed by artificial intelligence, the greatest change in…

Martin Fowler's May 27 Fragments brings together four arguments with direct implications for teams working with AI agents. All four are worth covering. Ian Johnson: build quality gates before releasing the agent Ian Johnson published a series about restructuring a gnarly codebase: three months, 258 commits, moving from a Laravel monolith with no tests to an application with automated quality gates and an AI agent shipping production code with minimal supervision. The insight Fowler highlights is about the transition from in-the-loop to on-the-loop: "For the first two months of this project, I used Claude Code with auto-approve turned off. Every file edit, every terminal command, every change… I reviewed it before it executed. The results were good. The code was clean. But I was doing most of the thinking and half the typing. The agent was a fancy autocomplete with better suggestions." Ian Johnson Manual review of every change is not how you build trust in the agent. Trust comes from building the structure that ensures the agent will do the right thing, then stepping back. The sequence: characterization tests first, static analysis, architectural patterns that make things flow correctly. Fowler notes this is exactly the sequence he would use himself. Adam Tornhill: roughly 2 hours of cognitive endurance Adam Tornhill observes that agentic work has a decision density that is mentally more expensive than it appears. The estimate is roughly two hours as a sustainable limit, not a full day of work. The implication: adding more parallel agents does not solve the problem, because the bottleneck is the coordinating engineer's cognitive capacity, not available processing volume. The solutions are smaller tasks, automation, and verification mechanisms, not more parallelism. NHS: closing open source repositories NHS (UK National Health Service) closed open source repositories citing LLM threats to code security. The UK Government Data Services countered directly: making code p

Podlite 2.0 is tagged. Podlite is a block-based markup language built around typed blocks and explicit boundaries — the same document is meant to read cleanly whether a person or a tool parses it. This release adds eight blocks and attributes and changes two parsing rules. The specification is at podlite.org/specification ; the full changelog sits inside the spec under =head2 v2.0 . The Coming in Podlite 2.0 article from the review window covered what is new in depth. This post focuses on what to do now: how to migrate existing documents and where to find the rest. For most documents the answer is short — a well-formed v1.0 document renders unchanged under v2.0. Breaking changes Two changes parse differently than before. Neither touches a well-formed document — if anything needs updating, it is a parser, not your text. Legacy attribute syntax removed A few outdated string attribute formats are gone. The bracket form ( :key<value> ) and the parenthesized form ( :key('value') ) remain. If a document uses the current syntax, nothing changes. =include is now a directive =include always behaved like a directive, but the spec previously listed it under block types. Tokenization rules differ between directives and blocks. Parsers built against the v1.0 spec must move =include into the directive dispatch path alongside =config and =alias . For document authors: no change. =include still takes the same syntax and produces the same output. The reclassification matters only for tools that build ASTs. New features at a glance Eight additions ship in v2.0. Existing documents render unchanged. =boundary : a typed section divider. Renders as a horizontal rule, exposes structure to tools. =set : pre-configure attributes for the next block. Multiline values, inline markup, lexical scope. G<> + :masked : content masking. Inline mark or whole-block attribute; hidden by default, revealed by render condition. =data-table block: renders CSV or TSV as a table. Three source forms (inline b