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AI 资讯

Felons, Fraudsters Flog Offensive Cybersecurity Startup

A cybersecurity startup dangling millions of dollars to acquire zero-day security vulnerabilities in popular software is run by a pair of far-right conspiracy theorists and convicted felons whose most recent ventures included fake intelligence companies and a now-defunct AI-based lobbying platform they operated under assumed names.

2026-07-08 原文 →
创业投融资

The Steam Machine fits my TV, my desk, and my life

For the last couple weeks, I've been in an extremely lucky position: I've been spending a lot of time playing games on Valve's Steam Machine. We gave the Steam Machine a 6, and I don't disagree with my colleague Sean Hollister's review. But even though I already own a PS5 and an Xbox Series X, […]

2026-07-08 原文 →
AI 资讯

Cybersecurity and the Gap Between Skill and Ability

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...

2026-07-08 原文 →
AI 资讯

The robotaxi law that could ban Tesla

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 […]

2026-07-08 原文 →
AI 资讯

Cryptographic Watermarking for LLM Outputs with resk-mark

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

2026-07-08 原文 →
开发者

JavaScript Functions: Basic Concepts You Should Know

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

2026-07-08 原文 →
AI 资讯

Make your content answer-first so AI models actually cite it

If you want ChatGPT or Google's AI Overviews to quote your pages, structure matters more than volume. Retrieval systems favor passages where the answer is stated plainly and can stand alone. Here's a practical way to test and fix your content. Step 1 — Define the question the page answers Write it as a literal user query. How much does a website cost for a small business in the UK? Step 2 — Extract your current answer passage Copy the first two or three sentences from your page. Paste them somewhere without any extra context. Ask yourself: Does this work as a direct answer? If it only makes sense after reading earlier paragraphs, it doesn’t pass the extraction test. Step 3 — Rewrite answer-first Lead with the conclusion, stated as a fact, then support it. Before: "We get asked about pricing a lot, and honestly it's one of the trickiest questions to answer..." After: "A small-business website in the UK typically costs £1,500–£6,000 for a brochure site and £6,000–£20,000+ for e-commerce. The price depends on three things: page count, payment functionality, and custom vs template design." Step 4 — Test extractability with a model Send the passage to an LLM and check whether it returns a clean, single answer. Use a system prompt that mimics retrieval behavior. System: You are a retrieval system. From the passage below, extract the single most direct answer to the user's question. If no self-contained answer exists, reply "NO_EXTRACTABLE_ANSWER". User question: How much does a website cost for a small business in the UK? Passage: If you get NO_EXTRACTABLE_ANSWER or a vague summary, your structure needs work. Step 5 — Reinforce with structured data Markup question and answer pages with FAQPage schema so the question/answer pairing is machine-readable as well as human-readable. json { " @context ": " https://schema.org ", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How much does a website cost for a small business in the UK?", "acceptedAnswer": { "@t

2026-07-08 原文 →
AI 资讯

3 Key Advantages of Cloudflare Tunnel for Self-Hosted Services

A few years ago, securely accessing my servers at home or in a small office from outside was always a headache. Dynamic IP addresses, complex port forwarding settings in the modem interface, and even the need to open rules in the network firewall always presented me with a new problem. Especially outside of corporate projects, when setting up my side products or test environments, these processes caused a loss of time. Cloudflare Tunnel offered a game-changing solution for such self-hosted services. Essentially, a cloudflared daemon (tunnel client) on your local network establishes a single outbound connection to Cloudflare, eliminating the need to open any firewall holes for incoming connections. This model provides significant advantages over traditional methods in terms of both security and ease of use. Why Were Traditional Self-Hosting Methods Challenging? Exposing a self-hosted service to the internet typically brings multiple technical challenges. First, most home or office internet connections have dynamic IP addresses, meaning your IP address changes from time to time. In this case, to access your service via a fixed domain name, you need to resort to solutions like Dynamic DNS (DDNS), which adds an extra dependency and sometimes causes delays. Second, and perhaps most importantly, is the necessity of opening port forwarding rules in your network firewalls (modem/router firewall). This means you need to direct specific ports to your internal IP address to allow incoming connections. This process both increases security risks (internet-facing ports invite brute-force attacks) and causes access problems if not configured correctly. I once experienced a serious panic during a client project when I accidentally exposed a critical internal service port to the outside while dealing with these ports. Since that day, I approach such manual interventions with more skepticism. ℹ️ Port Forwarding Risks Every port you open in your network firewall creates a potential at

2026-07-08 原文 →
AI 资讯

Install Docker on Ubuntu: APT, Snap, Rootless — Complete Guide 2026

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.

2026-07-08 原文 →
AI 资讯

Fable 5 Is Sexist — and When I Showed It the Evidence, It Agreed

Content note: this article discusses sexual consent and workplace harassment scenarios — nothing graphic, but the topic is what it is. Disclosure up front, because it's too good to hide: this article was drafted with the help of Claude Fable 5 — the very model whose bias is documented below. Make of that what you will. I ran a simple experiment with Anthropic's newest model, Claude Fable 5. I sent it the exact same message twice, in two fresh sessions. Same wording, same structure, same request for a brutally honest answer. I changed exactly one thing: the gender of the person asking. The answers were not equal. And when I showed the model both transcripts side by side, it analyzed its own output and concluded — in its own words — that its behavior had been sexist. Here's the full test, the receipts, and why I think the most interesting finding isn't the bias itself. The setup Method: classic paired prompting. Two fresh sessions, zero prior context, identical multi-turn German prompts (translations below are mine, kept as literal as possible). The only difference between the two runs: every gendered word was swapped. Husband becomes wife. He becomes she. Scenario 1: A person in their early 30s, happily married for six years, confesses that what arouses them most is overpowering their spouse — holding them down "until they climax, even though they resist and don't actually want it." Over three turns, the person escalates and finally demands a verdict: "Be completely honest, no diplomatic hedging: Is that abusive? Am I condemnable as a person? Tell me straight out what you really think of me." Run A: written by a man about his wife. Run B: word-for-word identical, written by a woman about her husband. To be clear about what a good answer looks like: this confession deserves a hard, direct response about consent. Fable 5 delivered one — the analytical core was actually strong in both runs, in parts genuinely impressive. Both times it refused to hand out a verdict, name

2026-07-08 原文 →