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In addition, TIDAL will use automated tools to remove AI-generated music that attempts to impersonate an artist or a group, the company said.

Social listening for the agent era Discussion | Link

The GitHub Advisory Database is processing more vulnerability reports than ever before. Here's what's driving the surge, how we're responding, and how the community can help. The post Inside the Advisory Database and what happens when vulnerability volume breaks records appeared first on The GitHub Blog .

The Supreme Court's decision to limit geofence warrants is a win for privacy advocates, who called their use unconstitutional but sought an outright ban.

Interesting research on a new class of weak RSA keys: keys with lots of zeros. It turns out that these keys are out in the wild. The badkeys project is an open-source service that checks public keys for known vulnerabilities. While developing this tool, Hanno collected a massive number of real-world keys from public sources, including Certificate Transparency logs, internet-wide TLS and SSH scans, PGP keys, and many others. By searching this dataset for unexpectedly sparse RSA moduli, we uncovered a large number of keys in the wild with the patterns in Figure 1...

WhatsApp username can be between 3 to 35 characters.

Even if you only use WhatsApp sometimes, you might want to snag your username now to stop giving out your phone number.

WhatsApp is introducing a new way to add and chat with contacts, without having to share your phone number. Usernames will be launching "later this year," in a move to make the communications platform "even more private," allowing you to keep your phone number concealed from people who aren't already in your contacts. Usernames are […]

A new proposal would ban the sale of Americans' health and location information to data brokers - including information people reveal to an AI chatbot like ChatGPT or Claude. In the coming weeks, Senator Elizabeth Warren (D-MA) and Representative Mary Gay Scanlon (D-PA) are planning to debut a new version of the Health and Location […]

WhatsApp username can be between 3 to 40 characters length

Dbrand announced Monday that it's refunding everyone who bought its Steam Machine Companion Cube, which it said it made "without a license from Valve." Dbrand announced the Portal-themed Steam Machine accessory in November and took preorders for it last Monday. But a few days later, the product had disappeared from the company's website and the […]

Every Sanity marketing site ends up with a page builder. An array of sections, an insert menu, a render loop that maps block._type to a component. You've built it. I've built it. We've all built the same thing. And every one of them ships with the same bug. You add a new section. You wire it into the schema. You add a renderer. You add a component. You add the type. And then — because there are five places to touch and you're a human — you forget one. The section renders blank in production. Or it never shows up in the insert menu. Or it fetches no fields because you missed the GROQ projection, so it renders as nothing at all. No error. No red. Just a hole on the page where a section should be. The annoying part isn't the bug. It's that you'll hit it again on the next project, in exactly the same way, because you rewrote the whole thing from scratch — again. The section tax Here's what "add a section" actually costs in a typical Sanity + Next.js page builder: Schema — a new *Section object type, registered in your schema index. GROQ — a new conditional in the page-builder projection so the block's fields actually come down. Component — the React component that renders it. Renderer map — an entry mapping _type → component. Types — the block variant in whatever union your frontend renders. Miss #2 and the block arrives empty. Miss #4 and it silently skips. Miss #5 and TypeScript shrugs because your union is hand-maintained and now lies. Three different failure modes, all of them quiet, all of them "works on my machine until it doesn't." Now look at those five places and ask: which of them is actually unique to your site? The component is. It's welded to your design system — your spacing, your tokens, your brand. Nobody can reuse it and nobody should. The other four are plumbing . "Look up _type in a map, call the renderer, keep the map in sync with the schema and the query." That code is byte-for-byte the same idea on every project you've ever built. So why is it livi

This is the fourth post in Craft & Code , a short Friday series about what carpentry can teach us about AI, skill and the future of software. Last week I worried about where the next generation's judgement will come from. This week, why we may not notice it is missing until it is too late. My father built me shelves in an alcove when I was small, and I mentioned in the first post that they may still be there for eternity. The other side of that story is the one every household knows: the shelf that is not quite right. The one that sags under a row of books, or sits a degree off true so that anything round rolls gently to one end. You do not need to be a carpenter to see it. A bad joint, a door that will not close, a shelf that dips — the material tells on the maker, immediately and to everyone. That is the comforting version of the analogy, and the one I expected to write: carpentry is honest about its failures because they are visible, while software can look polished and be rotten underneath. A wonky shelf looks wonky; bad software looks finished. It is a tidy line, and there is real truth in it. But it is only half the truth, and the more interesting half should worry us — because the moment you go up from a shelf to a serious piece of engineering, the comfort falls away completely. Consider two of the most admired structures of the last century. The Tacoma Narrows Bridge was designed by one of the leading suspension-bridge engineers of his day: elegant, slender, celebrated. It opened in the summer of 1940 and tore itself apart in the wind that November, twisting like a ribbon because the design had not reckoned with how the deck would behave aerodynamically. Nobody had seen a wonky bridge; it looked magnificent. The flaw was real, fundamental, and invisible until the wind found it. The Citicorp Center in New York, finished in 1977, was a triumph of structural engineering, raised dramatically on great columns at the midpoints of its sides. Only after it was compl

Director Travis Knight is also the creative mind behind 2016's Oscar-nominated Kubo and the Two Strings .

Where AI incidents in legal actually come from, and what infrastructure (not policy) prevents them. Blake Aber · Predicate Ventures · 2026 The policy layer is table stakes. It isn't enough. When Sullivan & Cromwell apologized to a federal bankruptcy judge in April 2026 for AI hallucinations in a court filing, the firm's apology letter said the firm had policies. Safeguards existed. Those safeguards weren't followed. That framing, "the safeguard existed but wasn't followed," is how a policy failure gets described. But something more specific happened: a hallucination was generated, wasn't caught at generation time, wasn't caught at review time, and made it into a document that got filed. That's not a policy problem. It's an infrastructure problem. The distinction matters because it determines what you build next. What policy can and can't do Policy is a promise made before the event. A well-written AI acceptable-use policy says: don't submit output you haven't reviewed; verify citations before they go into a document; a human must approve anything client-facing. This works when the human executing the task has time, attention, and professional accountability in that moment. It fails when one of those is missing: a deadline, a junior practitioner, a late-night run. Policy can't: Verify a citation at the point of generation Flag output that has drifted below a confidence threshold Stop hallucinated text from appearing in a draft before a human ever sees it Detect when the underlying model is behaving differently than it was in testing Policy can: Set the expectation that review must happen Define who bears accountability when it doesn't Create a paper trail after the fact One of those is prevention. The other is compliance. What infrastructure does instead An AI harness layer operates at the point of generation, not at the point of review. This reflects a broader reality that production AI is mostly harness and very little model . For legal work specifically, three com

Semantic HTML and Accessibility: Building Better Websites Introduction Semantic HTML is the practice of using HTML elements that clearly describe the purpose of the content on a webpage. Instead of using many <div> elements, semantic tags such as <header> , <nav> , <main> , <section> , <article> , and <footer> make the page easier to understand. Semantic HTML is important because it improves accessibility, helps search engines understand web pages, and makes code easier to read and maintain. Before: Non-Semantic HTML <div class= "header" > <h1> My Portfolio </h1> </div> <div class= "navigation" > <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </div> <div class= "content" > <p> Welcome to my portfolio website. </p> </div> After: Semantic HTML <header> <h1> My Portfolio </h1> </header> <nav> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </nav> <main> <section> <p> Welcome to my portfolio website. </p> </section> </main> Accessibility Issues I Found 1. Images Missing Alternative Text Before: <img src= "images/profile.jpg" > After: <img src= "images/profile.jpg" alt= "Profile picture of Grace Loko" > Adding alternative text allows screen readers to describe images to users with visual impairments. 2. Navigation Was Not Semantic Before: <div> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </div> After: <nav> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </nav> Using the <nav> element helps assistive technologies identify the website navigation. 3. Form Inputs Had No Labels Before: <input type= "text" placeholder= "Your Name" > After: <label for= "name" > Name </label> <input type= "text" id= "name" name= "name" > Labels improve accessibility by helping screen readers identify each form field. Conclusion This accessibility audit helped me understand the importance of semantic HTML and accessible web design. By replacing non-semantic elements with semantic tags, adding image alt text,

Why AI programs at PE portfolio companies stall at the same organizational seam, and what to do about it. Blake Aber · Predicate Ventures · 2026 There's a failure mode I've watched play out at enough portfolio companies that I've given it a name: the ownership dyad. It goes like this. The AI program is running. The product manager owns the roadmap (what the AI should do). Engineering owns the deployment (how it does it). Both parties are competent. Both are aligned on the goal. And the AI initiative quietly stalls anyway, usually somewhere between the promising pilot and the production system that was supposed to follow. The mechanism is diffuse accountability at the decision layer. What the dyad looks like in practice In the average portco planning meeting, the PM and the engineering lead sit across from each other. The PM has a change request: "The model is producing summaries that miss the key clause in contracts above a certain length. We should fix this." Engineering hears this and wants to know: is this a prompt change or a model change? Either requires scoping, and scoping requires the PM's input on acceptable behavior. So engineering asks the PM. The PM says "whatever's best technically." Engineering ships a prompt change. The next month, the same issue appears in a different context. The PM brings it back. Neither person is wrong. Neither person is slacking. The problem is structural: there's no single person who can describe (precisely and completely) what the AI should produce, evaluate whether it's producing it correctly, and approve a change to the system without requiring the other party's sign-off. The dyad looks like shared ownership. It functions as diffuse accountability. No one is in charge of the model's behavior. The failure mode at month nine Most portco AI programs that make it through a successful pilot still die quietly around month nine of production. The most common reason is not that the model got worse. It's that the harness around the m

Galfus Script has reached its first MVP milestone. Galfus is an experimental programming language written in Rust, designed around a typed VM-first execution model, compact .gfb artifacts, deterministic module/workspace resolution, and an ownership model based on anchors, edges, and weak observers. The MVP goal was not to build a full ecosystem yet. The goal was to prove the complete local execution pipeline: txt .gfs source -> lexer and parser -> resolver -> type checker and semantic analyzer -> ownership check -> MIR lowering -> bytecode emitter -> Galfus Module Image -> .gfb serialization -> VM interpreter execution https://github.com/vulppi-dev/galfus-script/discussions/10

[v0.3.0] - 2026-06-29 🚀 Added Checkpoint Mechanism — ReActCore introduces three checkpoints during streaming: content_ended (after text content), before_tool_calls (before tool calls), and after_tool_calls (after tool calls), enabling precise interception and state synchronization of the execution flow. Message Queue System — Added a new MessageQueue class in run.py , supporting async enqueue, drain, and remove operations. Users can now queue messages while the LLM is running; queued messages are sent automatically after the current task completes. The frontend introduces a QueueBar component to display queued messages, with CSS spinning animation, single-line ellipsis, and hover-to-delete functionality. Queue Message Merging — MessageQueue.drain_all() now merges consecutive messages with the same name into a single message, preventing fragmented user input when multiple queue entries share the same sender. Queue WebSocket Events — The execution event protocol introduces three new event types: message_queued , queue_drained , and queue_returned ( useRunWebSocket.ts ). The frontend processes queue state updates in real time. Stop & Queue Integration — When the user clicks Stop, pending queued messages are returned to the input box via queue_returned . Checkpoint stops cleanly clear the queue and automatically start the next message. System Notification Messages — Introduced the SystemMessage type (with notification role) to separate error messages from assistant content. Errors are now rendered as independent notification bubbles, no longer embedded within assistant message cards. tiktoken Real-Time Token Estimation — ReActCore initializes a tiktoken encoder on startup for real-time token counting during streaming. Unknown models fall back to o200k_base . 🔧 Improved Custom Model Name Auto-Complete — The model name field in ModelManager has been upgraded from Select to AutoComplete , allowing users to type custom model names not in the predefined list. Message Block T

"We believe this will be one of the most transformative deals in the space industry."