He’s being prosecuted for giving border officials a code that wiped his phone : The case centers on a feature included in GrapheneOS, a custom Android operating system that runs in place of the software on most modern Google Pixel devices. Tunick’s attorneys confirmed GrapheneOS was running on his phone. The software feature allows the device owner to set a passcode that deliberately wipes the contents of that device if entered instead of the user’s unlock passcode. Tunick’s case also raises ongoing questions about what constitutional rights can be invoked at the border, which the U.S. government has long asserted is not U.S. soil until a person is authorized to enter...

The deal gives Okta identity threat detection capabilities as enterprises seek to secure AI agents and other non-human identities across cloud environments.

Google said a July update resolves the problem on recent devices, with Pixel 9a and 8a getting a fix in September.

The milestone means Tesla is halfway to accomplishing one of the four core product goals Elon Musk has to hit to unlock his full $1 trillion pay package.

Three approaches to the issue of quantum results that can't be verified classically.

Your Mac's vitals in the menu bar. Free and open source. Discussion | Link

Google's AI brain for the next generation of robots Discussion | Link

Databricks is expanding its Lakebridge toolkit by introducing an agentic code conversion feature designed to help organizations migrate from legacy data warehouses. This new capability uses Genie Code to rewrite complex SQL scripts, allowing customers to transition their workloads to the Databricks lakehouse environment with higher efficiency and less manual intervention. Advanced Automation for Complex Code Translation The core of this update is the agentic code converter, a system that utilizes AI subagents to manage the heavy lifting of migration projects. These agents perform a variety of tasks including deep analysis of source code and the parallel conversion of multiple files. They also validate translated SQL and can autonomously retry sections that fail during the initial pass. This iterative approach is a significant step forward from traditional methods that often require human developers to step in when software hits a wall. By allowing developers to set specific migration rules for unique enterprise SQL structures, the tool provides a level of customization that previous automated systems lacked. The Lakebridge suite already offers several transpilation engines, such as the pattern-based BladeBridge technology and the compiler-based Morpheus engine. However, the addition of agentic AI introduces a reasoning layer that these older technologies do not possess. This reasoning is vital for moving beyond simple syntax mapping and into the realm of complex logic. Traditional transpilers like Morpheus are excellent at handling standard syntax mapping. They easily manage date functions and basic join commands. Problems arise when these tools encounter control-flow reasoning, cursors, or dynamic SQL that is generated at runtime. These complex elements often differ significantly across platforms like Oracle or Teradata. Industry experts note that these difficult sections usually represent about 15 percent of a codebase but consume the vast majority of manual labor

Best Buy has raised the price of the Asus ROG Astral RTX 5080 OC to $2,099 - the first time a major US retailer is directly selling an RTX 5080 for more than the $1,999 MSRP of the RTX 5090, VideoCardz reports. The ROG Astral RTX 5080 OC now costs 40 percent more than its […]

Microsoft just told on itself. The "Frontier Company" push, announced in a blog post pitching AI engineering that "amplifies and protects your intelligence" , flips the sales script from "look what AI can do" to "here's how we protect what you already have." That reframe is a confession. The AI governance framework Microsoft is now selling exists because enterprise deals were stalling, and they were not stalling on model capability. They stalled on trust, IP exposure, and payback nobody could prove to a CFO. Microsoft knows it, and it is now bundling the guardrails as aggressively as it once sold the hype. If you own an Azure platform or an AI line item on a P&L, your real question is narrower than the keynote: is this new tooling, new contract terms, or marketing stretched over Purview and Defender with fresh language? The honest answer is all three, in uneven proportions. Treat "Frontier Company" as a signal about where the next twelve months of sales pressure will land, not as a product you can deploy. What "amplify and protect" actually maps to There is no Frontier Company SKU. Decompose the slogan and you get three mechanisms that the marketing deliberately conflates. Data leaking out maps to Microsoft Purview. Data Security Posture Management for AI and sensitivity label enforcement in Copilot responses are shipped, GA, and genuinely useful. The extension of Purview controls into custom applications built on Azure AI Foundry is newer, and parts of that surface still carry preview flags. Check the status page before you put it in an audit response. Bad model output maps to Azure AI Content Safety . Prompt Shields and protected material detection for text are GA. Groundedness correction and some of the red-teaming evaluation tooling skew preview. Threat detection for deployed AI workloads lives in Defender for Cloud's AI threat protection . Legal exposure maps to the Customer Copyright Commitment . This one matters and almost nobody reads it. It is an indemnity,

There are fresh signs that fusion power startup Commonwealth Fusion Systems will list in the next two to three years.

What Is Retrieval Augmented Generation (RAG), and Why Does It Make AI So Much Less Confidently Wrong? You know that game show contestant who buzzes in before the host finishes reading the question, shouts "MOUNT EVEREST!" with absolute certainty, and then looks genuinely confused when the correct answer turns out to be "the Treaty of Westphalia"? That's been AI for most of its existence. Supremely confident, occasionally correct, and deeply committed to whatever pops into its head first. Now imagine that same contestant gets a new rule: before answering, they can phone a friend who has the exact relevant textbook already open to the right page. The friend reads them the actual answer, word for word, and then the contestant puts it in their own words for the judges. Suddenly, our buzzer-happy friend is getting questions right. That phone call is Retrieval Augmented Generation, and it's the reason AI chatbots have gotten weirdly more useful in the past year. The Old Way: Confidently Wrong at 200 Miles Per Hour Traditional large language models (big AI systems trained on tons of text) get trained on enormous dumps of text scraped from the internet, books, and whatever else researchers can feed them. Then the training ends. The model gets sealed off, frozen in time with whatever it learned. When you ask a question, these models generate answers by predicting the most plausible-sounding next words based on patterns they memorized during training. It's essentially very sophisticated autocomplete. The AI has no fact-checking mechanism. It doesn't "know" anything in the way you know your own phone number. It just knows what words tend to follow other words. This leads to what researchers politely call hallucinations, which is a fancy term for "making stuff up with tremendous confidence." The AI generates text that sounds authoritative and well-structured because it's learned the pattern of how authoritative text sounds. But the actual facts? Those might be completely invent

The API Gateway Has Grown Up When API gateways first entered the enterprise architecture conversation, the value proposition was straightforward: put a reverse proxy in front of your APIs, enforce authentication, and add basic rate limiting. Problem solved. That framing was adequate for 2012. It's dangerously incomplete for 2026. Today's API gateway sits at the intersection of integration, security, observability, and increasingly AI — and the organizations that still treat it as a simple routing layer are leaving significant capability on the table while accepting operational risk they don't have to carry. The modern API gateway is an integration hub in its own right, and understanding its full capabilities is essential to building a resilient, scalable API strategy. What Traditional API Gateways Got Right (and Wrong) The first generation of API management platforms — Layer 7, Apigee, legacy enterprise API managers, early Kong — nailed the fundamentals. Authentication enforcement, basic transformations, developer portals with API keys, rudimentary analytics. For the REST API era, this was genuinely valuable. But these platforms had structural limitations that became more painful as API ecosystems scaled: Static configuration : Policy changes required deployment cycles, not dynamic updates Monolithic architecture : The gateway itself became a single point of failure and a scaling bottleneck Reactive observability : Dashboards showed what happened; they didn't predict or prevent problems Protocol silos : REST gateways couldn't route gRPC, GraphQL, or WebSocket traffic without additional infrastructure No integration context : The gateway was blind to the systems it was protecting — it enforced policies without understanding the business logic behind the APIs The Modern API Gateway: A Capability Map Authentication and Authorization — Now Much More Than Token Validation Modern gateways don't just validate that a token exists and hasn't expired. They implement the full

What Are Vector Embeddings? (And Why Your Spotify Wrapped Knows You Too Well) Imagine a postal worker who never learned to read. Not a single word. Can't tell an A from a Z, wouldn't recognize their own name on a birthday card. And yet, this worker has memorized the precise physical location of every house in an infinite city. They navigate by pure spatial memory, knowing exactly which homes sit in the same cul-de-sac, which ones are clear across town, and which are practically next-door neighbors. They've never read a street name or house number in their life, but ask them which residences are similar and they'll tell you instantly based on coordinates alone. This is how vector embeddings work. An embedding is a representation of data (a word, a song, an image, anything) as a list of numbers that captures its relationships to other data. Your Spotify playlist, that photo of your dog, the word "pizza," they all get converted into coordinates in a vast mathematical space. The system doesn't "understand" content the way you do. It just knows where everything sits and can measure distances between points. Close together means similar, far apart means different. How the Worker Learned the Territory The worker didn't start with this comprehensive mental map. They built it gradually by walking millions of routes and noticing what appeared together. Which houses had mail delivered on Tuesdays. Which residents waved to each other. Which blocks had similar holiday decorations. Over time, patterns emerged, and the worker positioned each house based on these observed relationships. The AI does the same. It processes massive amounts of examples and notices what appears in similar contexts. Words that show up near the same other words get placed close together in the coordinate system. "King" and "queen" both appear frequently alongside "royalty," "throne," "crown," and "castle" in text, so their coordinates land in the same neighborhood. "Dog" and "puppy" show up in similar sen

Meta says AI is making it dramatically easier to build and launch new consumer apps, with CEO Mark Zuckerberg telling investors the company has more new consumer products on the way following a recent wave of releases for Facebook Groups, Marketplace sellers, Instagram, and gaming.

Avi Schiffmann has a new version of his controversial AI companion. It’s more expensive, and you can’t change its personality.