I am a last-year data science major at university who initially joined because of AI's exciting potential across numerous industries. However, after learning about multiple companies backtracking on their AI use on their platforms and cutting back on their data center expansions, I can't help but think that something is very wrong behind closed doors. I came to understand that the demand for AI is slowly decreasing in some areas and increasing exponentially in others. To me, it seems every major industry "needs" AI to make life easier, yet is backtracking when it doesn't perform the way they want it to. My concerns revolve around how unpredictable AI's usage is. If I get involved in an industry that actively destroys land, water, and other resources, I would hope that the environmental costs will be outweighed by the benefits everyone sees from AI. However, with the economic trend of AI's value decreasing for companies that initially went all in on it, I can't help but feel like I'm actively destroying the planet. Does anyone have any suggestions or moral redemption for me? I want to jump ship before the big explosion, but I'll stay if there's great potential for growth with AI. submitted by /u/Emergency_Ad6929 [link] [留言]

80%. That is the share of code currently being merged into Anthropic's production systems that was written by Claude. Not code-reviewed. Not pair-programmed. Written. In February 2025, when Claude Code launched, that number was in the low single digits. Sixteen months later, the company decided that data point — and the trajectory behind it — was worth a public warning. On June 4, 2026, Anthropic published "When AI Builds Itself," a research paper co-authored by Marina Favaro, head of the Anthropic Institute, and Jack Clark, one of the company's co-founders. It was the first major publication from the Anthropic Institute since its founding in March 2026. The paper did two things simultaneously: disclosed internal productivity data that most AI companies keep private, and called for a global mechanism to slow or pause frontier AI development before the process becomes self-sustaining without meaningful human direction. The data came first. The policy recommendation followed from it. Here is what the numbers actually show and why every developer building on AI infrastructure today should read this carefully. The Productivity Curve Nobody Predicted Anthropic published a chart of engineering output per engineer, indexed to a baseline from 2021–2024. The curve is flat for four years. Then Claude Code shipped in February 2025. The multiplier progression from that point: 1.2x, 1.5x, 1.9x, 2.5x. By Q1 2026: 5.8x. By Q2 2026: 8x. The typical Anthropic engineer is now merging eight times as much code per day as they were in 2024. Not 8% more. Eight times more. That is not a productivity improvement — it is a different category of output from the same headcount. To understand what drives the number, you need to understand what Claude Code actually does inside Anthropic's engineering workflows. The tool was built for and by engineers working on frontier AI systems — which means the tasks it handles are not boilerplate CRUD endpoints. Claude is writing test harnesses for novel m

From a stand-alone app to a Google Gemini partnership, here’s everything you need to know from WWDC 2026 about Apple’s upcoming overhaul of Siri.

If you are just getting started with Azure CLI and Bash scripting, this post is for you. I will walk you through how I automated the creation of Azure resource groups for multiple environments using a single Bash script — something that was taking a cloud admin several manual steps every week. This is Project 2 in my TechRush Cloud Engineering bootcamp series. If you want to see where this journey started, you can read my previous post where I tackled deploying a web app across two Azure regions for the first time . That project involved real blockers — quota limits, CLI version mismatches, and a deep dive into Azure Resource Providers. This one went smoother, and I think that is because the previous project was the hard school. The Problem Imagine a cloud administrator who has to create five resource groups every single week, one for each active project: Project-A-RG Project-B-RG Project-C-RG Project-D-RG Project-E-RG Every week. By hand. Management's response was simple: automate it. But here is where the task gets more interesting. Instead of creating one flat resource group per project, the better approach is to create four resource groups per project — one for each environment: Dev Test UAT Production This matters because each environment needs its own access controls, cost tracking, and lifecycle rules. You do not want your Development environment sharing a resource group with Production. Keeping them separate is a real-world cloud best practice, not just a bootcamp exercise. What You Will Need Before running this script, make sure you have the following set up: Azure CLI installed on your local machine. You can follow the official installation guide . An active Azure account . A free account works fine for this. A terminal that runs Bash — Linux, macOS, or WSL on Windows. Understanding the Design The core idea behind this script is parameterization . Instead of hardcoding project names, the script accepts a project name as input and uses it as a prefix for ev

Yes, I'm calling it out. It IS racism. As an active member of r/MachineLearning and a researcher who is ethnic Chinese, I am DISGUSTED by unfounded accusations against the group of researchers who constitute over half of the field. Such posts pop up every other week, grounded in conspiracy theories, and creating a sinophobia echo chamber. I understand the salty feeling when one's paper is rejected, no matter whether the paper actually deserves acceptance or not. Given the noise in conference organization and reviewing process, and a relatively junior body of participants, it is very likely that one finds a paper "worse than mine" slip into the conference, and there's a high chance that the paper has a Chinese author. That's simply because of the composition of the authors, and does not warrant accusations, aka witch hunts, towards certain ethnic groups. This sub is about an important scientific subject in the modern world. If anyone agrees with the logic "80% of the authors are Chinese, so my rejection is their fault.", they should seriously rethink their career plan since such thinking does not belong to serious scientists. We should be open to discussing the problems we have in the current conference organization and reviewing process, but racism should not have a foothold in our field. submitted by /u/AffectionateLife5693 [link] [留言]

Home and Shortcuts are not immune to Apple's AI upgrades.

Apple is putting control back into the hands of parents with more granular screen time features.

Apple's OSes come with Liquid Glass tweaks and performance optimizations.

Apple Intelligence will soon be able to watch browser tabs and update passwords.

Siri will soon be able to provide something like to the standard chatbot experience.

Cycle tracker will now notify women when their cycle patterns are suggestive of perimenopause.

Apple's new dictation system could compete with Wispr Flow and others.

The idea behind the new "Siri AI" is to turn the assistant from a voice controlled assistant into an AI companion that can do a lot more.

At WWDC 2026, Apple announced an overhaul to its Screen Time parental control features that aim to improve its safeguarding features to protect children who use iPhone, iPad, and Mac devices. Some of the new features coming with Apple's iOS 27, iPadOS 27, and macOS 27 updates include giving parents and guardians more control over […]