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“The government believes it has become aware of a method of bypassing, or ‘jailbreaking’ Fable 5,” the company said in a blog post.

“I’m not sure that this company supports a hackathon culture anymore,” one employee posted in a forum open to the entire staff.

Executives and employees alike are struggling with Meta's chaotic AI strategy, according to sources and internal discussions reviewed by WIRED.

Winning fight against AI data centers gives people a "taste of political power."

GOP lawmakers, tech investors, and even OpenAI have tied the anti-data center movement in the US to Chinese interference. Experts say it’s much more complicated than that.

Google has announced the Google Colab CLI, a command-line tool that allows developers and AI agents to interact with remote Colab runtimes directly from a local terminal. By Daniel Dominguez

The fraudsters allegedly targeted hundreds of thousands of people with Gemini-coded scams sites.

TL;DR: Your AI coding assistant is a generalist. It writes Flutter that looks right but quietly reaches for 2022 patterns. Agent Skills are a new, official way (from the Dart and Flutter teams) to hand your agent task-specific, battle-tested workflows it loads on demand. Two repos, flutter/skills and dart-lang/skills , ship ready-to-use skills for responsive layouts, routing, testing, localization, static analysis, and more. Install in one command: npx skills add flutter/skills --skill '*' --agent universal npx skills add dart-lang/skills --skill '*' --agent universal This post breaks down what they are, how they differ from rules files and MCP, the full catalog, what a real skill looks like under the hood, and whether they actually move the needle. (Spoiler: mostly yes, with one honest caveat.) Let me tell you about a fight I have almost every day. I ask my AI agent to make a screen adapt to tablets. It confidently hands me code that switches layout based on MediaQuery.orientationOf(context) . It looks clean. It compiles. It even runs . And it's wrong, because device orientation has nothing to do with how much window space your app actually has on a foldable, in split-screen, or in a resizable desktop window. The model isn't dumb. It's a generalist trained on a giant pile of Flutter code, much of it old. And here's the uncomfortable truth the Flutter team said out loud when they launched this feature: Flutter and Dart ship new features faster than LLMs can update their training data. That lag has a name, the knowledge gap , and it's why your agent keeps writing rookie Flutter with a straight face. Agent Skills are the Flutter team's answer to that gap. I've been running them on real projects, and they're one of the few "AI workflow" things in 2026 that earned the hype instead of borrowing it. Let's get into it. Table of Contents The real problem: your AI is a generalist What are Agent Skills, exactly? Skills vs Rules vs MCP: who does what The full catalog: every of

Whether you’re planning a backyard barbeque or a World Cup watch party, Govee’s Table Lamp Classic can help set the mood with color-changing lighting effects. Right now it’s down to just $59.99 ($20 off) at Amazon, which is its best price yet. The rechargeable lamp can last up to 30 hours on a single charge, […]

Why isn't my 3D view transition working?! Sunkanmi tackles this frustration and offers an elegant fix for it. Why Isn’t My 3D View Transition Working? originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Pinecone has announced a new integration between its Nexus knowledge engine and Microsoft OneLake, aiming to fundamentally change how enterprise AI agents access and reason over corporate data. By Craig Risi

Light up and secure your driveway, backyard, or porch with a floodlight security camera.

The repurposing of Pokémon Go data for AI training continues to draw scrutiny.

The company has set aside an unusually high number of shares for retail investors. Still, experts say, you’re just getting the crumbs.

David Stein shares how to rethink large-scale architectural migrations using AI. He discusses ServiceTitan's "assembly line" pattern, explaining how decomposing legacy codebase refactoring into standardized tasks can achieve massive parallelization. He highlights the critical role of programmatically rigid validation loops to eliminate LLM hallucinations and accelerate engineering agility. By David Stein

Originally published on the Prufa blog . In June 2026 we pointed Prufa's free audit at 50 products that had just launched on Show HN — every launch from the previous 30 days that earned at least 10 points. These are products at their moment of maximum attention: front page, real traffic, founders watching the comments. The headline numbers, from the 49 audits that completed (one site couldn't be reached by our runner): 100% of the 49 launches had at least one machine-verified finding. 78% — 38 of 49 — had at least one critical finding. 40 critical and 61 warning findings in total, every one verified by deterministic checks against captured browser evidence. No site is named in this post. The point isn't to embarrass anyone — it's that these failures are systematic, and if these teams have them on launch day, you probably do too. Methodology, briefly Each site got the same audit a free Prufa run does: a real browser loads the public pages, captures network traffic, console output, cookies, and response codes, and a fixed suite of deterministic checks grades the evidence. Same input, same verdict. Every number below is from a code-verified check — no LLM opinions are counted anywhere in this data. One honest caveat: our export keeps only the top findings per site, so the per-issue counts below are floors , not totals. The real numbers are equal or worse. What actually breaks at website launch: the numbers Sites affected (of 49) Finding Severity 38 No analytics events detected critical 24 No canonical link on entry page info 22 Cookies set without the Secure attribute warning 14 Broken links warning 12 No <h1> heading on entry page info 11 No robots.txt info 10 JavaScript console errors during page load warning 10 Missing meta description warning 8 Images missing alt text info 7 Missing Open Graph tags info 3 Tag container loads, but no analytics events fire warning 2 Canonical URL pointing to a different host critical The most common launch bug: analytics that record

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The Budget You Approved Isn't the Budget You'll Pay You approved $180K for a senior AI engineer. Eighteen months later, you've spent $282K and you're still not sure the hire is working out. This isn't unusual. It's the rule. Companies hiring AI engineers for the first time routinely underestimate total cost by 40–60%. Here's a breakdown of where that gap comes from — and why most founders don't see it until it's too late. The 56% Gap: Where It Comes From 1. Recruiting Costs Are Higher Than You Think (~12–18% of first-year salary) AI engineer recruiting isn't like standard software recruiting. Specialized headhunters charge 20–25% of first-year salary. Even if you find someone through your network, you'll spend founder or VP time on 15–30 hours of interviewing, plus take-home evals that the best candidates increasingly decline. If you use a staffing firm, add the markup. If you DIY it, add the opportunity cost. Typical recruiting overhead: $22,000–$40,000 per hire 2. Onboarding Takes Longer for AI Roles (~2–3 months of ramp) An AI engineer hired to build production agent systems isn't productive on day 1. They need to understand your domain, your data, your existing architecture, and your risk tolerance for AI-generated outputs. The ramp is real — most teams see 60–90 days before meaningful output. At $180K salary, two months of ramp is $30,000 in salary with limited ROI. Add engineering time for mentoring (typically 20% of a senior engineer's time during ramp), and you're adding another $15,000–$20,000. Ramp cost: $30,000–$50,000 3. Infrastructure Spend Scales With Experiments AI engineers experiment. That's the job. Every experiment has a GPU bill, an API bill, and a storage bill. Early-stage teams routinely see $3,000–$8,000/month in AI infrastructure spend once they've hired their first AI engineer — much of it from exploratory work that doesn't ship. Over a year: $36,000–$96,000 in infra costs that weren't in the original headcount budget 4. Tooling and Data Cos

Unlike humanoid robots designed around a fixed form — think Boston Dynamics — Theker's machines are built to be reconfigured.

Today on Uncanny Valley, we take an early look at the SpaceX IPO and why you might find yourself among the investors without even realizing it.