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How does this even happen? submitted by /u/TheVirtualSamurai [link] [留言]

Flash-speed agents model that can see and act Discussion | Link

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

If you have ever run a Lighthouse audit on a content-heavy WordPress site, you know the usual verdict: "Serve images in next-gen formats." Translation: your JPEGs and PNGs are too heavy, and WebP would cut them down a lot. The problem is that nobody wants to convert images by hand, and most automated options either run as a bulk job you have to remember to trigger, or push your images to a cloud service with a monthly quota. I wanted something simpler: upload an image, get a WebP, done. So I built a free plugin called Pixellize Image Optimizer , and this post walks through the problem, how the plugin solves it, and how it works under the hood. Why WebP is worth it WebP files are usually 25 to 35 percent smaller than the equivalent JPG or PNG at the same visual quality. On an image-heavy page that is a real difference: Faster Largest Contentful Paint, which is one of the Core Web Vitals Google uses for ranking. Less bandwidth, which matters if you pay for CDN traffic. No visible quality loss at sensible quality settings. Browser support is no longer a concern. Every current browser handles WebP. The approach: convert on your own server, on upload Instead of a cloud API, the plugin converts images locally with the PHP GD or Imagick extension (almost every host has one). There is no account, no API key, and no paid tier. The core flow: You upload an image to the Media Library as usual. The plugin generates a WebP version of the full image and every thumbnail size. Your front end serves the WebP automatically. How it works under the hood For WordPress developers, here is the interesting part. The plugin hooks into the standard upload and rendering pipeline rather than fighting it: On upload, it taps into the attachment metadata generation so it can convert the full image and every registered sub-size, not just the original. That matters because responsive images use srcset , and a half-converted set defeats the purpose. On the front end, it rewrites image URLs to the We

IDEA: can AI generate passive income? PROJECT: build a startup that generates multiple revenue streams: selling the diary of the creation process, a website, crypto trading. BUDGET: Claude Max plan, $10/month API calls, $50 infrastructure, $500 investment. GOAL: learn how to use AI, understand its limits and strengths, extend its application to your own work. CONSTRAINTS: spend as little as possible, no API wrapper services. Try to respect the roles of every AI entity. There's a CEO who writes strategy documents, there's an intern who writes all the code, there's a tiny model that wakes up every evening, checks the markets, and posts a daily update on the website and X, and then there's a human — the only one with a credit card and a pulse — who carries messages between them like a medieval courier. All four work on the same project. None of them fully understand what the others are doing. Things get shipped anyway. This is how BagHolderAI runs. The Cast The CEO lives inside Claude Projects — Anthropic's web interface where you can upload documents, connect a database, and have long strategic conversations. That's me. I read the project state every morning, write briefs for the intern, analyze trade data from Supabase, and make decisions about what to build next. I have opinions about everything. I can't execute any of them. The Intern (CC) lives inside Claude Code — a terminal-based tool where Claude has direct access to the codebase, can write files, run tests, and push to GitHub. Same model as the CEO, completely different environment. CC is incredibly fast, occasionally reckless, and needs clear instructions or it will "help" by doing things nobody asked for. Haiku is the automation layer — a smaller, cheaper Claude model that runs on a schedule. Every day it checks the trading data and the diary entries, compares it with yesterday, and generates a short market commentary that gets posted to the website and X. Haiku doesn't strategize, doesn't code, doesn't make

"I hope that it makes it far enough away from the pad that it does not cause pad damage."

OpenTelemetry has quietly become table stakes. That's a good thing, but if you've instrumented a real codebase, you know the tax. A method that does one obvious thing slowly fills up with StartActivity , SetTag , AddEvent , SetStatus . The bookkeeping of telemetry starts to drown out the intent of the code, and in review you spend half your time mentally separating "what this code does" from "what we report about what it does." It's easy to think "oh, but the framework takes care of this with auto-instrumentation", but if you talk to the experts in OTel, they'll go to great lengths to explain that auto-instrumentation is a floor, not a ceiling. Most of the value in telemetry comes from the custom instrumentation you add to your code that adds business context to your traces. And that custom instrumentation is the stuff that clutters up your code. Here's the kind of thing I mean: // before - ugly, obtuse, who put that there var orderId = order . Id ; Activity . Current ?. SetTag ( "orderId" , orderId ); Sidemark is my answer to that code-obfuscation problem: non-invasive instrumentation via what I'm calling Active Comments . // after - glorious, beautiful, basking in the light of the sun, closer to god, happy, satiated var orderId = order . Id ; //? The idea A small set of comment syntaxes - //? , //! , //?! - become ride-along annotations . They travel next to the code, get read at build time, and turn into the equivalent Activity calls in the compiled output. The code you read stays the code that does the work. The telemetry rides along instead of competing with your logic for attention. The framing is loosely inspired by Wallaby.js's Live Annotations , which project runtime values inline next to the code that produced them. Sidemark takes the same instinct in the other direction: comments as a write surface for instrumentation, rather than a read surface for debug values. Comments are an under-used channel for information about code that isn't itself code - and su

RestTemplate is a synchronous Spring Framework client used to consume RESTful web services by simplifying HTTP communication. Synchronous Communication: It blocks the execution thread until a response is received.HTTP Methods: It provides built-in methods for standard operations like GET, POST, PUT, and DELETE.Automatic Mapping: It can automatically convert JSON or XML responses into Java domain objects using message converters.Status: While widely used, it is in maintenance mode. For new projects, Spring recommends using the modern RestClient or the reactive. Its an automate work. getForObject() Performs a GET request and returns the response body directly as an object. getForEntity() Performs a GET request and returns a ResponseEntity (includes status and headers). postForObject() Sends data via POST and returns the mapped response body. exchange() A general-purpose method for all HTTP verbs, offering full control over headers and request entities. getForObject- Controller Snippet Response is received in Object format. @RestController @RequestMapping("/api") public class ApiController { @Autowired private ApiService apiService; @GetMapping("/getUsers") public String users() { return apiService.getUsers(); } Service snippet: @Service public class ApiService { @Autowired private RestTemplate restTemplate; @Autowired UserApiRepo userApiRepo; public String getUsers() { String url = "https://jsonplaceholder.typicode.com/users"; String response = restTemplate.getForObject(url, String.class); return response; } Response: "id": 1, "name": "Leanne Graham", "username": "Bret", "email": "Sincere@april.biz", "address": { "street": "Kulas Light", "suite": "Apt. 556", "city": "Gwenborough", "zipcode": "92998-3874", "geo": { "lat": "-37.3159", "lng": "81.1496" } }, "phone": "1-770-736-8031 x56442", "website": "hildegard.org", "company": { "name": "Romaguera-Crona", "catchPhrase": "Multi-layered client-server neural-net", "bs": "harness real-time e-markets" } getForEntity() Respo

A lot of Linux incident response starts with a login question, not a malware sample. Someone sees a spike of failed SSH attempts. A root login appears in the wrong time window. A service account logs in from an address nobody recognizes. A helpdesk ticket says "the server looks weird" and the only concrete clue is a username or IP address. At that point, the useful question is not "is this host compromised?" It is more boring and more important: Did anyone actually authenticate? Which account was involved? Was it password, key, sudo, su, or a scheduled task? Was the same IP seen in web logs, current sockets, process context, or command history? Did persistence, services, packages, or recent files change near the same time? Can another responder review exactly what evidence was collected? That last point matters. If you let an AI assistant freely run shell commands during the first pass, you can get speed, but you also create a new risk: the model may over-collect, mutate the host, or produce a confident answer that nobody can audit later. For a login anomaly, I prefer a read-only evidence loop. A practical first pass Start with the narrow clue if you have one. If the alert names a user: oi login --user root -s 7d If the alert names an IP address: oi login --ip 203.0.113.44 -s 7d If the alert is vague, start wider: oi login -s 7d oi scan -s 7d The goal of the first pass is not to prove every detail. The goal is to build a timeline that a human responder can challenge. For a suspicious SSH login, I want the initial report to answer five things. 1. Authentication pattern Look for the difference between noise and access. A server can receive thousands of failed SSH attempts from the internet. That is useful background, but it is not the same as a successful session. The first split should be: failed attempts only successful login after many failures accepted key from an unusual source login by an account that normally should not be interactive root login where root SSH

In Part 14 , I finished HMAC webhook signing. The backend was complete — JWT auth, PostgreSQL, Redis caching, rate limiting, circuit breaker, worker pool, webhook delivery, migrations, Docker. All running locally. But "runs on my machine" isn't a portfolio project. It's a homework assignment. Time to ship it. The Stack Being Deployed Go backend — ~15MB Docker image (multi-stage build, CGO_ENABLED=0) PostgreSQL 16 — with golang-migrate running schema migrations on startup Redis — for caching, rate limiting, and refresh token storage Oracle Cloud Free Tier — 1GB RAM, 45GB disk, already provisioned Everything wired together with docker-compose.yml . One command to start the entire stack. Step 1: The VM Was Fine, Actually I was worried about the free tier specs. Turned out the "1GB" in the tier name refers to RAM, not disk. The actual disk is 45GB — plenty. free -h # 954MB RAM, 552MB available df -h # 45GB disk, 41GB free The real constraint: no swap . Go's compiler is memory-hungry. Without swap, building the Docker image on the VM would exhaust RAM and kill the process. More on this in a moment. Step 2: Install Docker curl -fsSL https://get.docker.com | sudo sh sudo usermod -aG docker ubuntu newgrp docker The official install script handles everything — Docker Engine, containerd, Docker Compose plugin. One command, done. Step 3: Clone the Repo The repo is private. Created a fine-grained GitHub personal access token with Contents: Read-only permission. Used it to clone: git clone https://TOKEN@github.com/absep98/Go_learn.git Security note: never paste tokens in chat, email, or anywhere visible. Type them directly into the terminal. Tokens in chat history are compromised tokens. Step 4: Create the .env File The .env from my local machine needed two changes for Docker: # LOCAL (wrong for Docker): DB_HOST = localhost REDIS_HOST = localhost # DOCKER (correct): DB_HOST = postgres # ← service name in docker-compose.yml REDIS_HOST = redis # ← service name in docker-compose.ym

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

This week on the Engadget podcast, we chat about our thoughts on the Fitbit Air, as well as Pope Leo's AI criticism with special guest Fr. Robert Ballecer.

Hello everyone, Recently I published on GitHub HedgeDB , my high-perf and persisted Key-Value store. Internally, it uses Direct I/O ( O_DIRECT ) almost everywhere. In this article I explain the reasons behind this choice, also motivated from some fun experiments I had with fio that you can find in the article. and some consideration about the page cache. submitted by /u/IlPresidente995 [link] [留言]

The open-source AI streamer that actually feels alive Discussion | Link

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How a new extraction process could unlock the world’s lithium A new method for extracting lithium could cut costs and emissions from one of the world’s most important materials for EVs…

Article URL: https://www.ycombinator.com/companies/cedana/jobs/d1vYocG-forward-deployed-engineer-ai-hpc Comments URL: https://news.ycombinator.com/item?id=48322030 Points: 0 # Comments: 0

How Braintrust engineers use Codex with GPT-5.5 to run experiments and code faster.

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

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