标签：#m

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Beluga whales were in danger of getting euthanized after a Canadian theme park went bust. WIRED spoke with some of the scientists behind the groundbreaking rescue mission.

Like the Konami code, but for Roku remotes.

After canning an extraction shooter in the franchise, it seems a new open-world Far Cry game is in development.

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Framework's latest modular laptop trades one set of problems for another.

Framework laptops always come with compromises in exchange for their unique DIY premise. Even though you pay extra for one compared to sleeker options from competitors, it's easier to excuse minor flaws for modular ports, easy repairs, and an impressive range of upgradeable parts. So what happens when Framework goes "Pro"? We get a one-of-a-kind […]

The cost of QD-OLED gaming monitors is going down, even as many other PC components are still soaring above their normal prices. If your budget is locked at $300, Samsung’s 27-inch 1440p model costs exactly that much thanks to a recent $50 discount at Amazon. Finding a good QD-OLED monitor under $500 these days is […]

San Diego Comic-Con took place over the weekend, and as per usual it was full of tidbits of news across film, TV, and gaming. Marvel revealed the next Ghost Rider (Ryan Gosling) and Black Panther (David Jonsson), while we also got release dates for God of War: Laufey (February 16th) and the fourth and final […]

Outside the small town of Paducah, Kentucky, a wealth of uranium is locked away in thousands of storage cylinders filled with waste material from a now-closed nuclear enrichment facility. Lasers could help get it out. A company called Global Laser Enrichment (GLE) is looking to reprocess this old material with a new technology called laser…

Nike wants to make sure no one gets cold feet

If you can't find the watch face you want in the Play Store, you can create your own Galaxy Watch face from scratch.`

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submitted by /u/cheerfulboy [link] [留言]

This week's Java roundup for July 20th, 2026, features news highlighting: two JEPs proposed to target for JDK 28; new JEPs 540 and 541, Simple JSON API (Incubator) and Deprecate the macOS/x64 Port for Removal, respectively; the July 2026 Critical Patch Update; the GA release of Embable 1.0; Azul Payara 7.2.0; and Helidon 4.5.1. By Michael Redlich

How a dormant HTTP status code and a $500 billion valuation fight tell the same story: money is finally becoming native to the internet. For three decades, the HTTP specification carried a status code that did nothing. Code 402 — "Payment Required" — was reserved in the 1990s for a future in which the web would have money built in. That future never arrived. Payments were bolted on instead: card forms, PayPal buttons, checkout redirects, subscription walls. On July 14, 2026, that changed formally. The Linux Foundation announced the operational launch of the x402 Foundation , the open-governance body that now stewards the x402 protocol — the standard, contributed by Coinbase, that turns HTTP 402 into a live payment negotiation layer. Forty organizations have joined. The premier member list reads like a peace treaty between industries that spent a decade fighting each other: Visa, Mastercard, American Express, Stripe, Adyen, Fiserv, Google, AWS, Shopify, Coinbase, Circle, Cloudflare, Ripple, MoonPay , plus the Solana, Stellar, and Monad foundations. When card networks, cloud providers, and crypto issuers all sign the same governance charter, something structural is happening. That something is AI agents — and the question of how they pay for things. From press release to protocol The x402 story started in September 2025, when Coinbase and Cloudflare announced their intent to create a foundation around a simple idea: if AI agents are going to consume APIs, data, compute, and content autonomously, they need a payment method that works the way machines work — inside the HTTP request itself, with no account signup, no card form, and no human in the loop. The mechanics are deliberately boring. A client, typically an AI agent, requests a paid resource. The server responds with status 402 and a machine-readable header describing what it wants: the amount, the asset (usually a stablecoin like USDC), the network, and the payment scheme. The agent signs a payment authorization,

Not every arb opportunity is simultaneous. My bot uses a “legged” approach: it buys one side when it’s heavily underpriced, then waits for market sentiment to shift and buys the other side later for a total cost under $1.00. This strategy shines in volatile non-crypto markets (elections, sports playoffs, news-driven events). Careful inventory and timing controls turned it into a consistent contributor to the bot’s $130k+ track record. The sample source is in https://github.com/cryptomoonday/polymarket-arbitrage-bot

The same parser, four times Over the last year I kept running into the same shape of problem: A docs site generator that wanted Markdown chapters turned into navigation JSON. A RAG ingestion script where each Markdown file needed to become a list of text chunks plus its frontmatter metadata. An n8n flow that took Markdown emails and extracted only the tasklists. A static-site backend that accepted user Markdown and needed to validate structure before persisting. Each one is small on its own. But every time I reached for a different library — remark here, gray-matter there, marked once, a hand-rolled regex once too many — and every time one of them broke on the same edge cases: Nested GFM tasklists where the checked state was silently lost YAML frontmatter that included quoted booleans (parsed as strings, not booleans) Tables whose headers contained spaces (regex parsers treated them as one key) Code blocks containing Markdown — re-parsed as Markdown instead of fenced code So I built one endpoint that does it once, properly. What it returns POST /v1/parse takes a Markdown body ( text/markdown ) or a JSON envelope ( application/json ) and returns one stable JSON shape: { "success" : true , "data" : { "title" : "Project Alpha" , "frontmatter" : { "title" : "Project Alpha" , "status" : "shipping" }, "headings" : [ { "level" : 1 , "text" : "Project Alpha" , "id" : "project-alpha" } ], "sections" : [ { "heading" : { ... }, "children" : [ ... ], "content" : [ ... ] } ], "lists" : [ { "ordered" : false , "items" : [ "ship MVP" , "write README" ] } ], "tasklists" : [ { "items" : [ { "text" : "ship MVP" , "checked" : true } ] } ], "tables" : [ { "headers" : [ "Module" , "Status" ], "rows" : [{ "Module" : "API" , "Status" : "Done" }] } ], "codeBlocks" :[ { "lang" : "js" , "value" : "..." } ], "links" : [ { "text" : "..." , "url" : "https://..." } ], "paragraphs" :[ "..." ], "ast" : null } } The sections tree is the part I care most about. It's not just a flat list of headings

Cloud migration used to be a simple pitch: move off your servers, save money, scale on demand. For Indian enterprises today, the decision is more layered. Compliance rules have tightened. Cloud bills have grown unpredictable. And the assumption that a global hyperscaler is automatically the right fit is being questioned more often, especially by mid-size companies with real workloads and real budgets on the line. If your organisation is planning a migration, here's what actually matters before you sign a contract. Start with why you're migrating Most migrations get justified with one of three reasons: cost, scale, or compliance. Rarely all three at once, and the reason should shape the plan. If cost is the driver, look closely at your current spend. Bandwidth charges, storage tiers, and auto-scaling fees add up in ways that rarely match the sticker price teams budgeted for. If scale is the driver, the question is whether your workload actually needs the breadth a hyperscaler offers, or whether you're paying for hundreds of services you'll never touch. If compliance is the driver, data residency and audit requirements should be the first filter, not an afterthought. Data residency and compliance For Indian businesses, DPDP Act requirements, along with RBI and SEBI guidelines for regulated sectors, increasingly dictate where data can legally sit. This isn't a checkbox. It determines your shortlist of providers before pricing even enters the conversation. Confirm three things with any provider: where the datacentres physically are, whether the billing entity is India-registered, and whether the provider can produce compliance documentation on request, not just a marketing claim. A provider that can name the datacentre city and the entity name without hesitation has usually done the legwork. One that answers in generalities probably hasn't. The real cost of a migration Sticker price is the easiest number to compare and the least useful one. The real cost includes egress

Probabilistic Graph Neural Inference for bio-inspired soft robotics maintenance with ethical auditability baked in I remember the moment it clicked. I was hunched over a workbench in my home lab, staring at a tangled mess of silicone tentacles—a soft robotic octopus arm I’d 3D-printed and embedded with pneumatic channels. The arm was supposed to mimic the graceful, adaptive movements of a real cephalopod, but after a few cycles, it had developed a slow leak at one of the joint interfaces. The pressure sensors were giving erratic readings, and my traditional rule-based diagnostic script was useless. I’d spent weeks training a simple neural network to detect anomalies, but it kept flagging benign sensor noise as critical failures. That’s when I stumbled upon a paper on probabilistic graph neural networks (PGNNs) for molecular dynamics, and I realized: soft robotics maintenance isn’t about deterministic predictions—it’s about reasoning under uncertainty over a complex, interconnected system. This article is the story of how I built a PGNN-based inference system for bio-inspired soft robots, with ethical auditability baked in from the ground up. Technical Background: Why Soft Robotics Needs Probabilistic Graph Inference Soft robotics is fundamentally different from rigid robotics. A rigid arm has well-defined joints, links, and sensors; failures are often binary (motor burnout, gear slip). But a soft robotic tentacle is a continuum of deformable material with distributed sensing and actuation. The system’s state is a high-dimensional, partially observable probability distribution over material strains, pressures, and temperatures. Traditional diagnostic models—like support vector machines or feedforward neural networks—treat each sensor as an independent feature, ignoring the spatial and temporal dependencies that define soft robot behavior. In my research of graph neural networks, I realized that a soft robot is naturally a graph: each sensor node (pressure, strain, te