标签：#p

Whether you’re going off-grid or safeguarding against blackouts, these beefy, WIRED-tested batteries can keep the lights on.

submitted by /u/self [link] [留言]

Last week, national security agencies from the Five Eyes—that’s the rich, English-language-speaking countries club—jointly released a statement warning of the increasing cyber risks of AI models: in particular, their ability to autonomously hack into systems and networks. The statement was more measured than some of the breathless headlines about it, and the advice they gave is pretty much the standard advice everyone gives—albeit with newfound urgency. Internet risks are nothing new, and cyberattacks—both large and small—have been a significant issue since long before the current crop of generative AI models...

It’s hard to say if the pricey LG Micro RGB Evo television is worth shelling out for compared to its cheaper competitors, but you’ll probably enjoy its color-popping display.

A new book claims that Mystery, who teaches awkward men how to hit on women, had sex and smoked weed with an AI chatbot named Miss Shira Always.

For more than a decade, one question has loomed over the race to build autonomous vehicles: Are cameras alone enough to safely replace human drivers, or do truly driverless cars need additional, overlapping sensors like lidar and radar to navigate the world reliably? Tesla has bet billions of dollars that artificial intelligence and cameras are […]

Scammers are hijacking government websites to upload ads for “leaked” OnlyFans content. Thousands of copyright complaints from adult creators are helping people avoid malicious links.

Cryptographic Watermarking for LLM Outputs with resk-mark Links: PyPI: https://pypi.org/project/reskmark GitHub: https://github.com/Resk-Security/resk-mark Web: https://resk.fr __ __ ________ _____/ /__ ____ ___ ____ ______/ /__ / ___/ _ \/ ___/ //_/_____/ __ `__ \/ __ `/ ___/ //_/ / / / __(__ ) ,< /_____/ / / / / / /_/ / / / ,< /_/ \___/____/_/|_| /_/ /_/ /_/\__,_/_/ /_/|_| The Provenance Problem Every company deploying LLMs in production faces the same question: once a model generates text, how do you prove it came from your system? Prompts like "say you are an AI" are trivially removable. Post-hoc detectors are unreliable and adversarial. And once text leaves your system — forwarded, copied, pasted into a ticket — you have zero visibility. resk-mark solves this by embedding a cryptographic watermark directly into the token generation process. The output reads naturally, but carries a verifiable signature that survives rewording and truncation. How It Works resk-mark hooks into the language model's sampling process. Before generation, the caller provides a secret key. During sampling, the library biases the probability distribution toward tokens that encode that key's signature: from reskmark import WatermarkEncoder , verify encoder = WatermarkEncoder ( secret_key = " your-key-here " ) model = AutoModelForCausalLM . from_pretrained ( " mistralai/Mistral-7B " ) # Wrap the generate call output = encoder . generate ( model , " Explain the concept of zero-knowledge proofs. " , max_length = 200 , ) print ( output ) # "Zero-knowledge proofs are a cryptographic method where..." # Reads naturally - watermark is invisible # Later - verify provenance is_authentic , confidence = verify ( output , public_key = " corresponding-pub-key " ) print ( f " Authentic: { is_authentic } , confidence: { confidence : . 2 f } " ) Key Properties Invisible — the watermark does not change the meaning, grammar, or fluency of the output Robust — survives copy, paste, truncation, and light rewo

Introduction When learning JavaScript, one of the first concepts you’ll encounter is functions. Functions are the building blocks of JavaScript. They help you organize code, avoid repetition, and make your programs easier to understand. If variables store data, functions define behavior . You’ll use functions everywhere: handling user input, processing data, calling APIs, and structuring your code. In this article, we’ll cover: What is a function Function declarations Function expressions Parameters vs arguments Return values Arrow Functions Why Functions Matter 1. What is a Function? A function is a reusable block of code designed to perform a specific task. Think of it like a machine: Input → Process → Output function greet () { console . log ( " Hello! " ); } To run the function, you call it: greet (); // Hello! 2. Function Declaration This is the most common way to define a function: function add ( a , b ) { return a + b ; } 💡 Explanation: Defined using the function keyword Can be called before it is declared (because of hoisting) Key parts: function → keyword add → function name a, b → parameters return → output value add (); // ✅ Works! function add ( a , b ) { return a + b ; } 💡 Why does this work? JavaScript reads the code first, and function declarations are stored in memory during the initial phase (hoisting) . That’s why you can call the function even before it’s defined in the code. 3. Function Expressions Functions can also be stored in variables: function add ( a , b ) { return a + b ; } 💡 Explanation: Assigned to a variable Cannot be used before initialization add (); // ❌ Error: Cannot access before initialization const add = function ( a , b ) { return a + b ; }; 💡 Why does this cause an error? Because: const add has not been initialized yet when it is called. The function itself is not in memory at that moment . 4. Parameters vs Arguments This is a common beginner confusion: Parameter: variable in function definition Argument: actual value passed i

Installing Docker on Ubuntu should be simple, but in practice several Docker-shaped options compete for the same command name, each with different packaging, upgrade behavior, and security implications. This guide compares every major install path so you can pick the one that fits your machine. The options you will encounter include: docker.io from Ubuntu repositories docker-ce from Docker's official APT repository Docker from Snap Docker Desktop manually downloaded .deb packages the Docker convenience script rootless Docker Although they all provide container tooling, they are not interchangeable packages. The best choice depends on whether the machine is a developer workstation, a CI runner, a small server, a self-hosting box, or a production host. My default recommendation is calm but firm: for most technical users on normal Ubuntu machines, install Docker Engine from Docker's official APT repository. Use Ubuntu's docker.io only when distribution integration matters more than upstream Docker packaging. Avoid the Snap package unless you specifically want Snap behavior and understand its limits. Rootless Docker is worth knowing about, but it is not automatically the best default for every machine. This guide explains the tradeoffs, covers post-install security, and gives you clean installation paths for each method. Once Docker Engine is running, the Docker Cheatsheet is your daily command reference, and the Docker Compose Cheatsheet covers multi-container setups. Both sit alongside Git, VS Code, and CI/CD guides in Developer Tools: The Complete Guide to Modern Development Workflows . Quick Recommendation The table below summarizes which install path fits common scenarios. Use case Recommended install Developer workstation Docker official APT repo CI runner Docker official APT repo, version pinned if needed Small self-hosted server Docker official APT repo Production server Docker official APT repo, controlled upgrades Ubuntu-only conservative system Ubuntu docker.

I've been wondering for a while if anything in Unicode could accidentally compute. It turns out that UTS #35 transliteration rules are Turing-complete. I show how to compute Collatz with just 3 rewrite rules running on stock ICU, shipped with every major OS. submitted by /u/Dull_Replacement8890 [link] [留言]

Why Every Modern SaaS Needs a Billing Engine (Not Just a Payment Gateway) When building a SaaS product, API platform, AI application, or cloud service, most teams start by integrating a payment gateway like Stripe or Paystack. That solves one important problem: Collecting payments. But collecting payments and managing billing are two completely different challenges. As products grow, pricing becomes more sophisticated. Customers expect usage-based billing, flexible subscriptions, prepaid credits, seat-based pricing, tiered plans, and transparent invoices. Suddenly, the billing logic inside the application becomes one of the most complex parts of the entire system. This is why more engineering teams are separating payment processing from billing infrastructure . Payment Processing vs Billing Infrastructure A payment gateway is responsible for moving money. A billing platform is responsible for determining what should be charged, when it should be charged, and why it should be charged. Although these responsibilities often get grouped together, they solve very different problems. Payment providers typically handle: Payment authorization Card processing Refunds Payment retries Bank settlements Customer payment methods A billing engine manages: Usage metering Subscription billing Pricing rules Billing cycles Invoice generation Revenue analytics Customer usage tracking Payment orchestration Multi-gateway billing Proration Credits and quotas Keeping these responsibilities separate makes systems easier to maintain, easier to scale, and much more flexible when pricing changes. Why Billing Logic Doesn't Belong Inside Your Application Many engineering teams start with a simple subscription model. Then the business evolves. Suddenly the product needs to support: API usage billing AI token billing Storage billing Compute billing Per-seat pricing Hybrid subscriptions Enterprise contracts Prepaid credits The application slowly becomes filled with billing-specific code. Developers

I hate presenting. Not the prep, not the content, the actual moment of unmuting, sharing my screen, and narrating 20 slides to a wall of black camera squares, having no idea if anyone's actually listening or just quietly making lunch. So a couple weekends ago I went down a rabbit hole and built something to get me out of that. It's called Meeting Presenter. It's an AI skill that joins the call and presents the deck for you. You just... sit there. Steer it if you feel like it. Or don't. What it actually does You hand it a deck, it joins the meeting, shares its screen, and talks through the slides on its own. Not in a flat text-to-speech way either, it walks through the content more like a person explaining it than a bot reading bullet points. The part that actually got me hooked, though, wasn't the presenting, it's that you don't even need a finished deck to use it. If you've got a PowerPoint or PDF already, it'll just present that. If you've only got some rough notes, it'll turn those into slides first. And if you've got nothing but a vague idea, you can hand it a single sentence and it'll build the deck from scratch before presenting it. Which means the laziest possible version of this is: think up a topic five minutes before standup, type it in, and let it build and present the thing while you drink your coffee. I'm not proud of how often I've already done this. Setting it up Took me less than 10 minutes, most of which was making coffee while it installed. Grab a free API key from agentcall.dev - no lengthy signup, just a few seconds. Install it. Two options depending on how hands-on you want to be: Recommended: paste the GitHub repo link to your coding agent and tell it to install it. It clones and sets everything up for you. Or clone it manually and run it with your meeting link and deck. No config files to hand-edit. What it's actually like on a call It joins like any other participant, and it asks before it starts presenting rather than just barging in mid-mee

According to the PLG AI SaaS Benchmarks 2026 report , SaaS companies lose an average of 5–7% of revenue every month to churn , a rate that quietly compounds into nearly half of annual revenue erosion if left unchecked. Most teams don’t realize churn is already happening long before the cancellation click. It starts as subtle behavioral drift, lower engagement, feature abandonment, and delayed logins and only shows up in dashboards when it’s too late to act. That’s where AI changes the equation. Instead of reacting to churn, modern SaaS teams now try to intercept it through real-time behavioral detection, automated interventions, and continuous experimentation inside the product. Here are the best AI tools for SaaS customer retention (also called churn prevention tools) in 2026, compared by category, pricing, and key limitation. Why Traditional Churn Prevention Fails Most churn prevention strategies fail for three predictable reasons. First, they rely on lagging indicators. By the time dashboards show declining engagement, the user has already mentally churned. The decision didn’t happen when they clicked cancel; it happened days or weeks earlier during silent disengagement. Second, interventions are batch-based. Many lifecycle tools still operate on schedules like “send email after 7 days of inactivity.” But churn signals don’t wait for weekly jobs. The best intervention window is the moment behavior changes. Third, messaging is too generic. A user abandoning reporting features needs a completely different response than one abandoning collaboration workflows. Yet most tools treat both cases the same. The result is simple: teams react too late, too slowly, and too generically. Churn Signal Framework (What Predicts Churn) Churn doesn’t appear randomly; it follows patterns that can be detected in product data before cancellation ever happens. Churn Signal What It Looks Like Intervention Window Best Response Login drop Daily user becomes inactive within 7–14 days 1–7 da

Verity Harding tells WIRED that the US government’s nationalistic attitude toward AI is evidence that a worst-case scenario is taking shape.

Photorealistic human video, powered by compact AI Discussion | Link

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

Samsung has announced that its next Galaxy Unpacked launch event will be held on July 22nd, with the tagline: "A new shape unfolds." It's long been rumored that Samsung is about to expand its foldable phone line to a third format, with a shorter and wider version of its big book-style foldables, to match Huawei's […]

I have a simple rule when I work with a team: if a vulnerability makes it into main, the process...

ZML, a hot French AI startup endorsed by Turing Award winner Yann LeCun, has now released ZML/LLMD, software that could make running AI less costly.