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Just for kicks, I took a look at Jersey Mike's IPO documents. Surely a sandwich shop would have no need to mention AI. But lo and behold.

The news comes about a week after OpenAI announced its own custom AI chip in a partnership with Broadcom.

Here is a question I could not answer from the headlines: which European countries are actually losing people the fastest, in absolute terms or per capita? Those are two different questions, and they give two different answers. So I pulled the open data and ran the numbers. The headline figure Across the 19 European countries in the 2024 dataset, 17 recorded a net loss of native-born residents . Only two were net positive. So the "brain drain" story is not a handful of outliers, it is the default state of the continent. But the interesting part is who tops the ranking, because it depends entirely on how you measure. Load the data yourself The dataset is public on GitHub (CC BY 4.0). Every number below is reproducible with a few lines of pandas. No download, no API key, it reads the raw CSV straight from the repo: import pandas as pd url = ( " https://raw.githubusercontent.com/DatapulseResearch/ " " brain-drain-eu/main/data/net_migration_native_born_2024.csv " ) df = pd . read_csv ( url ) print ( df . shape ) # (19, 3) print ( df . columns . tolist ()) # ['country', 'net_migration', 'per_1000_residents'] # How many countries lost native-born residents? losers = ( df [ " net_migration " ] < 0 ). sum () print ( f " { losers } of { len ( df ) } countries had a net loss " ) # 17 of 19 net_migration is the raw count for 2024 (negative means a net loss of native-born residents). per_1000_residents is the same flow normalized by population size. The absolute ranking: Germany runs away with it Sort by the raw count and one country dominates: worst_absolute = df . sort_values ( " net_migration " ). head ( 5 ) print ( worst_absolute [[ " country " , " net_migration " ]]) country net _ migration 0 Germany - 91067 ... Germany loses -91,067 native-born residents, far more than anyone else in absolute terms. If you stop reading here, the story writes itself: "Germany, Europe's biggest brain drain." Plenty of coverage did exactly that. The counterintuitive finding: the ranking inve

Introduction Most projects start with a single repository. Imagine you're building an e-commerce platform: a Next.js storefront for customers, a React Native mobile app, and a NestJS backend API. Splitting these into three repositories feels like the obvious, clean solution. ecommerce-web ecommerce-mobile ecommerce-api For the first few months, this works fine. Then the project grows, and the cracks start to show: You copy utility functions between repositories instead of importing them. You duplicate TypeScript interfaces across the frontend, mobile app, and API. Your frontend and backend drift apart because each repo defines its own version of the same models. Updating one shared component means editing it in three different places. Eventually, maintaining the project becomes harder than building new features. If that sounds familiar, you're not alone — it's exactly the problem monorepos were designed to solve. In this article, we'll cover: What a monorepo actually is, and how it differs from a multi-repo (polyrepo) setup Why engineering teams choose it How Turborepo makes monorepos fast instead of slow A practical, production-ready structure for a Next.js + React Native + NestJS monorepo Common mistakes and best practices Whether you work with React, Next.js, React Native, or NestJS, these concepts will help you build projects that scale without becoming a maintenance burden. The Problem With Multiple Repositories A typical multi-repo setup looks like this: web-app/ mobile-app/ backend-api/ shared-components/ Each repository has its own package.json , dependencies, CI/CD pipeline, Git history, and versioning strategy. It looks clean at first — but as the project grows, several problems appear. 1. Code duplication You write a helper function once: export function formatPrice ( price : number ) { return `$ ${ price . toFixed ( 2 )} ` ; } Both the web app and the mobile app need it, so instead of importing it, someone copies it. Now there are two versions. When one

The company has been gaining traction in Europe.

A former software manager claims Wisk rushed software testing ahead of a crucial 2025 flight test.

The EU went after Google for the practice of bundling its search engine and browser with Android.

GitHub had 20,000+ secret scanning alerts across 15,000 repositories. Here's how we separated signal from noise, built remediation workflows, and reached inbox zero in nine months. The post How GitHub used secret scanning to reach inbox zero appeared first on The GitHub Blog .

What happens when a machine no longer needs to be trained to see something new? That's the quiet question sitting underneath this week's news, buried next to a less invasive brain implant and a handful of robots getting tougher for the real world. Neuralink says it's completed its first "transdural" brain implant, a surgical approach built to reduce trauma during the procedure. As someone who spends a lot of time thinking about how you get sensors close to a human eye without hurting anyone, I find these less-invasive-implant strategies worth watching, because the surgical-risk problem is basically the same one we wrestle with in ophthalmic hardware. Vision is getting less invasive too, in its own way. Roboflow rolled out text-prompt object detection built on SAM3 (Meta's latest segmentation model): you type the class of object you want "forklift," "cracked tile," whatever, and it returns boxes and masks without you collecting a single training image first. That's a real shift. For most of computer vision's history, teaching a model to recognize something new meant labeling hundreds of examples before you could even start; this collapses that step into a sentence. The same week brought several applied builds using the same detect-then-orchestrate pattern: a drone system that patrols for intrusions, a pipeline that inspects transmission lines for damaged cables, and an airport tool that spots foreign debris on the tarmac. The Robot Report's roundup of June's biggest robotics stories leaned heavily on humanoid robots companies going public, new deployments, and production milestones stacking up faster than would have seemed plausible a few years ago. Apptronik unveiled its Apollo 2 humanoid alongside a dedicated data-collection facility built so the robot keeps learning after it's deployed, not just during initial training which quietly answers one of the harder questions in robotics: how do you keep a system improving once it's out of the lab? X Square Robot raised e

For a few days, it seemed like Universal decided that there would be no advanced screenings of Christopher Nolan's The Odyssey for influencers. But on Monday, influencers sat alongside traditional critics and journalists at special showings of The Odyssey specifically for the associated press junket. Despite what it may have looked like, Universal was not […]

OpenAI CEO Sam Altman has reportedly proposed giving 5% of the company’s equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.

The company announced a new slate of executive hires meant to help turn things around, as Gravity SUV sales are not taking off as expected.

Tesla just released its second-quarter delivery and production report, showing that the automaker is starting to recover after a particularly brutal sales year in 2025. The company said that it produced a total of 451,758 vehicles between April and June of this year, including 442,936 Model 3 and Model Y vehicles, as well as 8,822 […]

The company delivered more than 480,000 EVs globally, seemingly thanks to geographic expansion and cheaper versions of the Model 3, Model Y, and Cybertruck.

Even the most advanced enterprise systems are tethered to a costly paradox: manual bottlenecks that introduce critical errors, security risks, and slow innovation. These hidden operational anchors are the friction preventing your organization from realizing its full potential. The Challenge: Manual Bottlenecks in Modern Enterprise Operations In an era defined by cloud-native architectures, microservices, and declarative infrastructure, a persistent and costly paradox remains at the heart of enterprise operations. We have built systems capable of immense scale and resilience, yet they are often tethered to manual, human-driven processes that act as operational anchors. These bottlenecks aren't just minor inefficiencies; they are critical points of failure, introducing latency, human error, and security vulnerabilities into our most important workflows. They represent the friction that slows down innovation, drains resources, and prevents organizations from realizing the full potential of their digital investments. Before we can orchestrate an autonomous workspace, we must first dissect the anatomy of these manual constraints. Identifying the High Cost of Manual Invoice Reconciliation To ground this challenge in reality, consider a ubiquitous and deceptively complex business process: accounts payable invoice reconciliation. On the surface, it seems simple. In practice, it's a classic example of a high-friction, manual workflow that silently bleeds enterprise resources. The typical process is a gauntlet of context-switching and swivel-chair integration: An invoice arrives, often as a PDF attached to an email, with no standardized format. A finance professional must manually open the document and visually identify key data points: invoice number, date, vendor, line items, and total amount. They then pivot to an ERP system like SAP or NetSuite to find the corresponding Purchase Order (PO). Next, they might need to access a separate logistics or warehouse management syste

The company now expects to ship a few thousand more vehicles by the end of 2026 than it previously expected, after launching its R2 SUV last month.

Originally published on productize.life . Quick answer: pm-skills is a marketplace of around 68 Claude skills for product management across 9 plugins, from strategy and discovery to market research and AI shipping. It is built by Pawel Huryn, author of the Product Compass newsletter. Each skill is not a loose prompt but a named, sourced framework, and one of them audits the gap between documentation and code, a PM lens built for the era of AI-written code. Last week I was reading through a run of repos that pack product work into skills. Some pick one topic and go deep. This one does the opposite: it is the broadest of the bunch. It is called pm-skills, by Pawel Huryn, the author of the Product Compass newsletter. He packs almost the entire product management craft into around 68 skills across 9 plugins, from setting strategy, running discovery, and researching the market, to analyzing data, executing, and shipping software that AI wrote. Usually something this broad ends up shallow. But when I actually opened it, it was not, and one skill in particular made me stop and look for a while , because it covers an angle that only recently became necessary in the era where AI writes code for us. I will tell it in three parts, starting with what it is , then why it is not just a prompt box , and closing with lessons for anyone building products . Terms, gathered once, right here skill a ready-made set of instructions an AI agent (such as Claude Code) can invoke, like a shortcut that wraps one way of doing a task. framework a ready-made way of thinking from the PM world, such as SWOT, JTBD, or RICE, that you once had to read a book to use well. plugin (category) a group of skills that belong to the same topic, such as the discovery category or the go-to-market category. PRD a product spec document that says what will be built, for whom, and how success is measured. Part 1: What pm-skills is It is a marketplace of around 68 Claude skills for PM, organized into 9 plugins, eac

Google tries balancing AI data center emissions with clean energy efforts.

I ripped open $892 worth of Pokémon packs on my phone in under 15 minutes and walked away with 62 cents. My adrenaline rush felt like the future of gambling.

Your comments on a dangerous rule putting politicals in charge of science can matter.