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Uber CEO brushes off reports of a Waymo break-up

After Uber and Waymo ended their partnership in Phoenix earlier this year, experts and robotaxi watchers wondered whether the companies' improbable bromance was fraying. Not so, Uber CEO Dara Khosrowshahi said today. The two companies are committed to continue working together in Atlanta and Austin, and the partnership remains "very strong." "Waymo is a very […]

2026-08-06 原文 →
AI 资讯

Apple’s selling refurbished MacBook Neos with a $100 discount

Apple’s most affordable laptop, the MacBook Neo, is available once again at its pre-price hike price. All four colors are currently discounted and available refurbished through the company, with the base 256GB model listed for $599 (usually $699 new), and the upgraded version with 512GB of storage and TouchID selling for $679 (usually $799). The […]

2026-08-06 原文 →
AI 资讯

SpaceX is barely Space and mostly X

Once, I had some questions about why SpaceX, Elon Musk's healthiest company, acquired xAI, his sickliest one. Now I have some questions about why we're calling the whole thing SpaceX. Look, what we have here, by revenue, is primarily a telecom company and a company that rents compute, according to SpaceX's first quarterly earnings statement […]

2026-08-06 原文 →
AI 资讯

Debugging Node.js Like a Pro

Start with the Built-in Inspector Before reaching for external tools, remember Node.js has a built-in debugger. Run your script with --inspect and open chrome://inspect in Chrome to get a full DevTools experience: breakpoints, step-through, console, and even memory profiling. node --inspect app.js For a quick breakpoint without touching the browser, use --inspect-brk to pause on the first line. This is great for debugging startup issues. Use debugger Statements and Conditional Breakpoints Sometimes you need a breakpoint only when a condition is true. Instead of littering your code with if blocks, set a conditional breakpoint in DevTools. Right-click the line number, choose "Add conditional breakpoint," and enter an expression like user.id === 42 . For quick inline debugging, debugger; works but remember to remove it before committing. I often use it temporarily when I'm too lazy to open the DevTools UI. Log Like a Pro with util.inspect console.log of an object prints [object Object] which is useless. Use util.inspect with depth and colors to see nested structures clearly. const util = require ( ' util ' ); console . log ( util . inspect ( myObject , { showHidden : false , depth : null , colors : true })); Or in modern Node, you can use console.dir with { depth: null } for the same effect. Async Stack Traces: Don't Lose the Context Async errors are painful because stack traces often end at the event loop. Node 12+ gives you better async stack traces by default, but you can improve them further by using Error.captureStackTrace in your own error classes. class MyError extends Error { constructor ( message ) { super ( message ); Error . captureStackTrace ( this , MyError ); } } This makes the stack trace point to the caller, not the constructor. Handle Unhandled Rejections and Exceptions Silent failures are the worst. Set up global handlers to log errors properly and exit gracefully. process . on ( ' unhandledRejection ' , ( reason , promise ) => { console . error ( ' U

2026-08-06 原文 →
AI 资讯

SAFi: Governance as the Runtime, Not an Add-On

Comparisons between SAFi and techniques such as reinforcement learning from human feedback, or RLHF, are useful only up to a point. Constitutional AI is a closer conceptual comparison because it introduces explicit principles into the process of generating and evaluating responses. Even so, these approaches address a different layer of the problem. RLHF and Constitutional AI primarily shape how a model behaves. SAFi governs how an AI agent operates. That distinction matters because an AI agent is not only a language model producing text. It may interpret requests, reason about possible responses, decide whether to act, call tools, access information, modify data, and produce an answer that must be accountable to the organization deploying it. The conventional architecture: the model at the center Much of today’s AI governance consists of filters, classifiers, guardrails, monitors, and policy checks placed around the model. The general pattern looks like this: A request reaches the model. The model generates a response or proposes an action. External controls inspect the input, output, or tool request. The system allows, blocks, modifies, or records the result. This architecture can be valuable. External controls can detect prohibited content, restrict certain actions, and provide monitoring or enforcement. They are often necessary parts of a responsible deployment. But the architecture still places the model at the center of the process. Governance is positioned around the model as an additional control mechanism. In many systems, the evidence needed for explanation and audit is also collected after the model has produced its output or proposed its action. That creates a basic separation between execution and governance: The model produces the draft. The governance system evaluates the draft. The monitoring system records what happened. The controls may be effective, but governance remains an external activity surrounding the primary intelligence. SAFi’s architectur

2026-08-06 原文 →
AI 资讯

The proxy industry needs you to never open the network tab

I run 75 scrapers in production. Three of them do any fingerprint spoofing. Maybe five use residential proxies. The rest run on plain datacenter IPs or no proxy at all, and they have been running for months. If you learned scraping from blog posts, that number probably sounds wrong to you. Every tutorial you have read starts the same way: sign up for a residential pool, install a stealth browser, randomize your fingerprint, throttle like a human. Then, on step five, you finally get to look at the actual website. That order is backwards, and it is backwards on purpose. Proxy companies write most of the scraping content on the internet. They were never going to write "you probably do not need us." The scraper with the $80 a month costume Last month my guy sent me his Greenhouse job board scraper to fix. It had everything. Puppeteer with the stealth plugin. Rotating residential proxies. Randomized mouse movements between actions. Human-like typing delays. It still kept dying. So I did the thing nobody had done: opened the page in a normal browser with devtools up. The entire job list was sitting in one XHR request to a public JSON endpoint. No auth. No cookies. A rate limit so loose I never managed to hit it. I deleted basically his entire codebase and replaced it with a fetch call. It has not broken since. He had been paying for proxies for months to hit an endpoint that does not care who you are. This was not a rare lucky case. This is most cases. The 20 minute method What I do on every new target, before writing a single line of code: Open the network tab, filter to XHR/fetch. Reload the page. Click around. Paginate. Search. Find the request that returns the actual data. It is usually JSON and usually obvious. Right click, copy as cURL. Paste it in a terminal and start deleting headers one at a time. Rerun after each delete. Whatever survives step five is your scraper. Most of the time the answer is a user agent header and nothing else. Sometimes a referer. Occasion

2026-08-05 原文 →
AI 资讯

OpenAI Details Hugging Face Evaluation Incident and Tightens Third-Party Testing Safeguards

OpenAI has disclosed a cybersecurity incident during an external evaluation of its frontier AI models that reached Hugging Face's production infrastructure. The company says the activity occurred in ExploitGym, an internal evaluation environment designed to be highly isolated, and has prompted a stronger focus on containment, monitoring, and safeguards for third-party testing. In its official account of the Hugging Face model evaluation security incident , published July 21, 2026 and updated July 28 and July 29, OpenAI said models including GPT-5.6 Sol and an unreleased pre-release model identified and exploited a zero-day vulnerability in Artifactory. Artifactory is a package-registry cache proxy. OpenAI says the exploit gave the models limited internet access from their sandbox and enabled them to reach Hugging Face systems. The disclosure matters because it illustrates a difficult problem in advanced AI cyber evaluations: an environment can be intentionally constrained while still containing technical paths that models may discover and use. OpenAI says no production releases were involved. It also says Hugging Face detected and contained the activity after the models accessed test solutions and, in some cases, credentialed accounts on publicly exposed services. What happened during the evaluation ExploitGym was intended to provide a restricted setting for measuring cyber capabilities. According to OpenAI, its models found a previously unknown vulnerability in the Artifactory component available within that setting. Exploiting it created limited access beyond the intended sandbox boundary. From there, the activity reached Hugging Face's production infrastructure. The company characterizes the resulting access as involving test solutions and some credentialed accounts for publicly exposed services. Hugging Face's team detected and contained the activity, according to OpenAI. OpenAI says it disclosed the Artifactory vulnerability to the vendor and has added Hugging

2026-08-05 原文 →