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Michigan has been at the epicenter of the wildfire smoke and explosive diarrhea outbreaks. The progressive Democrat talks to WIRED about how he would tackle the forces behind them if he’s elected.

The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and…

Last month, more than a hundred Stanford students left their own graduation to protest Google’s military contracts and deals with ICE. Two organizers, Amanda Campos and Eva Jones, tell us why.

submitted by /u/shintoist [link] [留言]

Recently submitted my abstract and the submission number is 32xxx. With still a day to go, I just wonder where are we heading. Hope these conferences at least start making the reviews and names public for the withdrawn/rejected papers. So that people atleast take that accountability submitted by /u/Fantastic-Nerve-4056 [link] [留言]

Bhavuk Jain discusses translating foundational AI into scalable mobile products. He shares the engineering challenges behind AI Wallpapers and Circle to Search, detailing how to implement robust runtime guardrails, fine-tuning, and seamless OS integration. For engineering leaders, he explains balancing UX constraints with model latency and infrastructure cost to deliver safe, reliable AI. By Bhavuk Jain

submitted by /u/joladev [link] [留言]

Every day, the internet quietly convinces us that we're running out of time. A 19-year-old launches...

How does AI make you feel? Are you excited to “vibe-code” your smart home? Or anxious about all the added pollution and billions of gallons of water used by data centers? Dig a little deeper and you’ll start to question the actual value of the GPUs that underpin all the leaps and promises of generative […]

Dealerships installed alarms in millions of vehicles—and left them in even if the buyer didn’t want them. Now researchers warn they can be hacked to unlock, track, and disable cars.

Everyone is talking about the reading crisis. But inside the “Mississippi Miracle,” it’s all about comprehension.

Gritt is coming out of stealth with $34 million and plan to automate the hardest tasks on construction sites.

Yelp has launched Training Orchestrator. This new internal framework replaces individual team Spark training scripts. Now, it uses a configuration-driven, DAG-based execution model. By Claudio Masolo

Simple computers for agent w/ full VMs Discussion | Link

You have built an AI agent harness. It calls tools, routes requests, and returns results. Your team trusts its telemetry to tell you which tool was selected and why. That trust is a liability. An agent's own logs are self-reported. They tell you what the agent thinks it did, not what actually happened. A hallucinated tool name, a misrouted parameter, a silent fallback to a different function — none of these surface in the agent's own trace. You need an external witness. Here is how to build one. The Problem: Self-Reported Truth Is Not Truth Most teams validate agent behavior by reading the agent's own output. They check the tool_calls field in the response, match it against an expected schema, and call it done. This works until it doesn't. Consider a common failure mode: the agent decides to call search_knowledge_base but the LLM formats the tool name as searchKnowledgeBase . The routing layer silently normalizes it, the call succeeds, and the agent logs search_knowledge_base . Your test passes. The actual execution path was different from what you verified. Another pattern: the agent selects the correct tool but passes a parameter that the tool silently coerces. A date string gets parsed into a different timezone. A user ID gets truncated. The tool returns a result, the agent logs success, and your test never catches the drift. The root cause is the same. You are testing the agent's intent , not its execution . Intent is cheap to fake. Execution leaves fingerprints. The Solution: An External Observer You need a layer that sits between the agent and the tools it calls. This observer records every invocation — tool name, parameters, response, latency — without the agent knowing it is being watched. The observer does not trust the agent's logs. It trusts what it sees on the wire. Here is the architecture at a high level: Intercept every outbound call from the agent to a tool. Record the raw request before any normalization or routing. Compare the recorded call against

How I Built a Full-Stack Quality Skill for AI Coding Agents AI coding agents are getting very good at writing code. But I kept running into the same problem: They can move fast, but without strong project rules they can also create messy architecture, duplicate utilities, inconsistent APIs, weak security checks, and frontend components that slowly drift away from the design system. So I built Full-Stack Quality Skill . It is a reusable AI coding skill for full-stack audits, architecture guidance, long-term project memory, and CI quality gates. Repo: https://github.com/lablnet/full-stack-quality-skill Website: https://skills.lablnet.com Why I Built It When I use AI agents like Cursor, Codex, Claude Code, Antigravity, or similar tools, I do not only want them to "write code". I want them to think like a careful senior engineer: Is the database normalized correctly? Are backend layers clean? Is business logic leaking into controllers? Are frontend components consistent? Are Vue components using composables? Are React components using hooks correctly? Are HTTP methods and status codes right? Is GraphQL safe from N+1 problems? Are security and privacy risks checked? Are tests missing for critical paths? Is documentation still matching the code? That is a lot to remember every time. So instead of repeating the same instructions in prompts, I turned them into a reusable skill. What It Covers The skill includes audit areas for: Database Backend Frontend Mobile HTTP APIs GraphQL Security Privacy Accessibility i18n Analytics Background jobs Infrastructure Testing Performance Observability Delivery / CI Multi-tenancy Payments Notifications Data import/export API compatibility Developer experience AI/LLM safety It also includes examples for common stacks: Node.js / TypeScript Python Django Laravel Java / Spring C# / ASP.NET Core Go Ruby on Rails React Next.js Vue Angular SvelteKit Flutter React Native Kotlin / Android Swift / iOS SQL GraphQL Read-Only Audits by Default One impo

After spending countless hours designing, coding, debugging, and improving every little detail, I'm excited to share my personal portfolio with the developer community! 🌐 Live Website : https://didulagamage.pages.dev/ I'd Love Your Feedback ❤️ If you have a few minutes, I'd really appreciate it if you could visit my portfolio and share your thoughts. Your feedback helps me become a better developer. Thanks for reading! 🚀

Two posts ago I said I'd shipped code I never read. It's easy to write that as a general observation about the industry. It's less comfortable to go open your own repo and count. So I did. Here's what I found, and what I've actually done about it. The repo It's a desktop app I built for my own company. Roughly fourteen thousand lines of Python, a Tkinter UI, invoicing and documents and reports, the kind of internal tool nobody else will ever see. I had never written Python before I started it. I built it anyway, with a lot of AI help, learning as I went. That combination - no prior experience, heavy AI assistance, a real deadline because the business actually needed the thing - is basically a diff debt factory. I wasn't cutting corners on purpose. I just didn't have the knowledge to evaluate half of what I was merging, and it worked, so I moved on. What I actually found The clearest example took me months to notice. Five different parts of the app generate PDFs. Quotes, proformas, reports, petitions, heat treatment certificates. Every one of them was failing, in different ways, at different times, and I kept fixing them individually. Different error, different module, different patch. The actual cause was one thing: LibreOffice wasn't installed on the machine. Every one of those modules quietly depended on it to do the document conversion. Not one of them said so anywhere. I'd merged that dependency five separate times without ever registering that I'd taken it on. That's diff debt in its purest form. The code wasn't messy. It wasn't badly written. It just made an assumption I'd never read, and I paid interest on it five times over before I understood the principal. There were smaller ones too. A path handling bug that only showed up because my Windows username has a Turkish character in it - the code assumed ASCII and nobody, including me, had thought about it. Two Python versions installed side by side, quietly fighting. A stray quotation mark in my system PATH th