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Waymo has a lot of experience building virtual systems to help its autonomous vehicles better understand the real world. It built realistic 3D worlds to better anticipate natural disasters and unpredictable edge cases. It created a virtual representation of a hyperattentive driver to test against its own autonomous vehicles in a series of simulated scenarios […]

The 168-megawatt facility will support Meta's global AI computing needs and can be expanded over time.

The exec, Emad Dlala, has left just a few months after being promoted to SVP of engineering and digital, TechCrunch has learned.

This is a story about a company that rolled out an AI interview system — and the lunch break I spent...

Would you treat a serious illness without seeing a doctor, relying only on whatever your favorite AI...

Google just made it significantly cheaper to enjoy its budget AI subscription tier.

Target: pnpm/pnpm Issue: pnpm/pnpm#12240 PR: pnpm/pnpm#12301 Public branch: https://github.com/scarab-systems/pnpm/tree/fix/deps-status-no-manifest Latest pushed commit: cb68ac1af0dcffbe4fb607a10b0df2046d2490ba This field test targeted a pnpm command-routing failure where pnpm self-upgrade could fail outside a project directory with: ERR_PNPM_NO_PKG_MANIFEST The issue looked simple at the surface: a global/self command should not require a project manifest just because the current working directory is not inside a package. But the repair boundary was more specific than “ignore missing manifest.” The problem was in the dependency-status verification path. When dependency status was unavailable because there was no project manifest, the command could fall through into the auto-install path. That made a self-upgrade/global-style command behave as if it needed a local project manifest. Failure shape The failing behavior was: pnpm self-upgrade run outside a project directory dependency status cannot be established from a project manifest the command path falls into install/manifest expectations result: ERR_PNPM_NO_PKG_MANIFEST That is the wrong ownership boundary. A self-upgrade command should not inherit project-manifest preconditions when there is no local project context. Boundary The boundary here is: global/self command execution versus project dependency-status verification Dependency-status verification can be useful when a command is operating inside a project. But when there is no project manifest and the command is not recursive/all-projects, “dependency status unavailable” should not automatically mean “try to auto-install project dependencies.” There are two different cases: Dependency status is unavailable because there is no project manifest. Dependency status is unexpectedly unavailable even though a root project manifest exists. Those cases should not behave the same. The repair preserves that distinction. What changed The patch updates: exec/commands/src

Have you ever stared at two different medicine boxes, squinting at the tiny font of the active ingredients, wondering: "Can I actually take these together?" Modern healthcare is complex, and drug-drug interactions (DDI) are a leading cause of avoidable ER visits. In this tutorial, we’re going to leverage GPT-4o Vision , React Native , and the FDA OpenData API to build a "Drug Conflict Scanner." We will utilize multimodal AI to transform messy pill-box photos into structured data and cross-reference them against official medical databases for safety. By the end of this guide, you'll master GPT-4o OCR structuring and automated knowledge graph verification for real-world health tech applications. 🚀 The Architecture 🏗️ The logic flow involves capturing images of multiple medicine labels, using GPT-4o's multimodal capabilities to extract chemical compounds, and then querying the FDA's database for potential interactions. graph TD A[React Native App] -->|Capture Multi-Photo| B[Node.js Backend] B -->|Image Buffer| C[GPT-4o Vision API] C -->|Structured JSON: Ingredients| B B -->|Search Interactions| D[FDA OpenData API] D -->|Drug Labels & Warnings| B B -->|Safety Report| A A -->|UI Alert| E{Safe or Warning?} Prerequisites 🛠️ To follow along, you'll need: GPT-4o API Key (via OpenAI) Node.js (for our backend relay) React Native (Expo is recommended for camera access) An account at open.fda.gov (though the public API works for limited requests) Step 1: Extracting Ingredients with GPT-4o Vision Traditional OCR struggles with curved medicine bottles and shiny packaging. GPT-4o excels here because it understands context. We don't just want text; we want the Generic Name of the drug. The Backend Logic (Node.js) // backend/scanner.js import OpenAI from " openai " ; const openai = new OpenAI ({ apiKey : process . env . OPENAI_API_KEY }); async function analyzeMedicineLabels ( imageUrls ) { const response = await openai . chat . completions . create ({ model : " gpt-4o " , messages :

RubyConf holds a special place in my heart. It was the very first tech conference I attended after receiving a scholarship fresh out of Flatiron School back in 2017 (you can read about my experience here ), and then in 2021, it was the stage for my first conference talk in Denver. Now, in another first, I joined the Program Committee for RubyConf 2026 to help put the program together, and what a program it is! We have an absolutely amazing lineup this year, and I'm so excited to see it come to life! Who else is planning on attending? Let's make plans to meet up and say hi!

Five peer-reviewed papers update the design and model its expected output.

Anthropic is releasing Claude Mythos 5 to trusted organizations and Claude Fable 5 to the public, a version it says can’t be used for cyberattacks.

Because a reality wasn't given to us and is not there; but we have to make it ourselves, if we want to be; and it will never be one for ever, but constant and infinitely changeable. Luigi Pirandello, One, No One, and One Hundred Thousand Pirandello wrote this about the human condition. He didn't know he was describing the future of the internet. The web we know is about to disappear Not slowly. Not gradually. The web page, as the default unit of human navigation, is about to disappear: it will strip itself of everything we call "interface" and what remains will be only what it always was underneath — data, structure, instruction. The enticing homepages. The banners. The product carousels engineered by UX teams to capture attention in the first second and a half. The brand colors. The call-to-action buttons optimized for conversion rate. All of this is designed for a human eye that navigates alone. That eye is about to delegate. The agent that browses for you Imagine you want to buy a pair of shoes. Today you open a browser, search, filter, compare, go back, reopen the tab you closed, forget what you were looking for, start again. In a few years — maybe less — you will tell the agent what you want. The agent will already know that you have wide feet, that you prefer leather to synthetic, that you're looking for something for a wedding in June but deep down you want something that works afterward too. It will know that today you're in a practical mood, not an aspirational one. That you've spent a lot this month. The agent won't open a homepage. It will query a data structure. It will receive prices, availability, variants, return policies. It will build for you — and only for you, and only in that moment — a presentation tailored to measure. Colors that belong to you. Texts that speak your language. Images generated for your aesthetic sensibility of that day. The same store. Five billion different versions. One for each person, one for each moment. One, No One, and On

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Rivian may be all in on robotaxis and autonomy, but it's still got human drivers - and EV buyers - to win over. The pricey R1S SUV and R1T pickup brought Rivian A-list media attention and cult-hit status, but the company faces a critical next step. The 2027 R2 is Rivian's bid for mainstream success, […]

Netflix engineer Casey Bleifer shares how to achieve rapid, automated code changes across a massive, diverse software fleet. She discusses building an event-driven orchestration platform using composable, Lego-like steps, and explains how Netflix utilizes automated canary validation, compliance checks, and a custom "confidence metric" to eliminate the long tail of manual engineering migrations. By Casey Bleifer