AI人工智能最新资讯、模型发布、研究进展
See how Wasmer used Codex with GPT-5.5 to build a Node.js runtime for the edge, accelerating development 10x to 20x and shipping in weeks instead of months.
Hello everyone, I've been working on a PyTorch library for solving Differential Algebraic Equations (DAEs) that supports vectorized execution and GPU acceleration. The library implements several algorithms that are not currently available in the Python ecosystem, including Generalized-Alpha integration, Dummy Derivatives index reduction, and adjoint sensitivity methods for DAEs. My motivation was to enable differentiable DAE simulation workflows in PyTorch for applications such as system identification, scientific machine learning, and physics-informed modeling. I'd be very interested in feedback on the numerical methods, API design, and potential ML use cases. GitHub: https://github.com/yousef-rafat/torchdae submitted by /u/Otaku_7nfy [link] [留言]
submitted by /u/jrdi_ [link] [留言]
When they're being eaten, bean plants release chemicals that draw in parasitic wasps.
Sepehr Khosravi discusses the evolution of developer productivity tools. Evaluating the strengths of tools like Cursor and Claude Code, he explains actionable techniques for senior engineers - including context engineering, custom rules, and Model Context Protocol (MCP) integrations. He shares real-world benchmarks and strategic frameworks for balancing AI adoption with clean code quality. By Sepehr Khosravi
Researchers are using machine learning algorithms to decrypt historical pencil-and-paper ciphers.
By the time the Children's Hospital closed its doors to trans patients, Sage had already stopped taking testosterone. A nonbinary high school student, they originally received treatment for the rapid onset of puberty. The changes their body experienced felt frightening and sudden. They developed PMOS, a relatively common hormonal disorder that can lead to hair […]
The company’s RTX Spark chips might finally turn the “AI PC” into reality.
This is not about the money. It’s about the principle. We are constantly told that AI is here to "help" us, but multi-million dollar companies like Perplexity are weaponizing their own AI to steal from regular users, stonewall our complaints, and blatantly violate consumer rights. It is systemic corporate greed, and they are getting away with it because people are too exhausted to fight back against a machine. Well, I am fighting back, and you should too. Here is the absolute scam Perplexity is running right now. How they steal your money: Living in Latvia, I pay for my Education Pro subscription in Euros (equivalent to $10/month). April 27: A payment was due, but my card declined. Fair enough. Perplexity froze my account immediately. I had ZERO access to Pro features. May 16: I manually paid for my subscription to reactivate it. The payment cleared. May 29: Barely 13 days later, my account was stripped of its Pro status and locked again. When I demanded an explanation, their billing system's "logic" was revealed: They took my May 16 payment and retroactively applied it to the "past due" period of April 27 - May 16. A period where my account was completely frozen and the service was actively withheld. They effectively charged me for a full month of service, gave me 13 days of access, and pocketed the rest. This isn’t a glitch; it’s unjust enrichment. It is theft. Enter "Sam" the AI If you try to get your money back, you don't get a human. You get "Sam, the AI Support Agent." I tried to explain that under European law, you cannot charge a customer for digital services you didn't provide. Sam’s response? A pre-programmed loop denying my refund, claiming I was "outside the 14-day EU refund window." Here is the most infuriating part: I did submit a ticket well within that window. But their automated system closed it without resolving it. When I pointed this out, the AI literally replied: "I don't have access to separate ticket histories." They use their o
How your front fork and rear shock work, so you can hurt less and have more fun.
OpenAI outlines its public policy agenda for AI, including safety, youth protection, workforce transition, and global standards to ensure AI benefits society.
OpenAI outlines a blueprint for U.S. governance of frontier AI, proposing a federal framework for safety, resilience, and national security.
Google, Microsoft, and other hyperscalers have come under scrutiny for their impact on water quality and availability.
Several real estate listings in the San Francisco Bay Area are offering to exchange a home for a piece of the AI startup.
CAP Theorem Explained: Choosing Between Consistency, Availability, and Partition Tolerance in Databases Imagine you're trying to book a flight online, and just as you're about to pay, the website crashes. When you try to book again, you find that the flight is now sold out, even though the website initially showed available seats. This frustrating experience is a classic example of a database trade-off between consistency, availability, and partition tolerance. The CAP theorem, first introduced by Eric Brewer in 2000, states that it's impossible for a distributed data store to simultaneously guarantee more than two out of these three principles. In this post, we'll delve into the world of CAP theorem, exploring its fundamentals, real-world database examples, and design implications. Introduction to CAP Theorem Understanding the Basics of CAP Theorem The CAP theorem is based on three primary principles: Consistency : Every read operation will see the most recent write or an error. Availability : Every request receives a response, without guarantee that it contains the most recent version of the information. Partition Tolerance : The system continues to function and make progress even when network partitions (i.e., splits or failures) occur. Importance of CAP Theorem in Distributed Systems In distributed systems, where data is spread across multiple nodes, the CAP theorem plays a crucial role in understanding the trade-offs between these principles. By grasping the CAP theorem, developers can design more resilient and scalable databases that meet the specific needs of their applications. Brief Overview of the Blog Post This post will explore the CAP theorem in depth, using real-world database examples to illustrate the trade-offs between consistency, availability, and partition tolerance. We'll discuss the fundamentals of CAP theorem, examine CA, CP, and AP systems, and provide guidance on designing for each combination. By the end of this post, you'll have a solid un
AI coding tools make development much faster. You describe a feature, the AI generates code, and within minutes you have something working. That's great for productivity, but it also makes it easy to miss security issues. When coding becomes faster, we often skip important security checks. Here are 6 quick things I always check before using AI-generated code. Don't Expose Secrets in the Frontend AI tools sometimes put API keys or secrets directly into frontend code. Remember: Anything in frontend code is public. In Next.js, variables starting with NEXT_PUBLIC_ are visible in the browser. Secret keys, database URLs, and service tokens should only exist on the server. Quick check: Search your code for words like sk_, secret, or key=. Frontend Validation Is Not Security A form may check emails, required fields, and formats in the browser. That's good for user experience, but attackers can bypass it. Always validate data on the server as well. Rule: If you validate something on the frontend, validate it again on the backend. Be Careful with dangerouslySetInnerHTML AI often uses dangerouslySetInnerHTML to display HTML or markdown content. If that content comes from users, it can lead to XSS attacks. To stay safe: Sanitize content before rendering. Use libraries like DOMPurify. Avoid rendering raw HTML whenever possible. 4. Check New Dependencies AI may install packages automatically when generating code. Before using them: Verify the package is legitimate. Check download counts and maintenance status. Run npm audit to find known vulnerabilities. Be careful of fake package names that look similar to popular packages. Review Authentication and Permissions Carefully Authentication is not just "Is the user logged in?" You must also check: Can this user access this resource? Does this user have permission to perform this action? For anything related to login, roles, permissions, or tokens, re view the code yourself. Never Paste Real Secrets into AI Chats Don't share: .env fil
A client asked me for a simple thing. Not ChatGPT. Not an agent. Not a multimodal assistant that can explain invoices, generate React components, and write poetry in three languages. Just a small classifier embedded into a website. The job sounded boring in the best possible way: take some text, classify it, return a result, keep it fast. So I started looking at the usual solutions. And then I had one of those moments where you stop reading documentation, lean back, and ask: Are we seriously doing this? Because the answer I kept running into looked like this: download a huge runtime download a huge model initialize a big ML stack then classify one small piece of text In one setup, the path was getting close to something like 250 MB per user . For a simple classifier. On a website. From a server. Every time. No. Sorry. That is insane. The problem The web has a strange habit now. You ask for one small AI feature, and the answer is often: bring the entire construction company. But sometimes I do not need a construction company. I need one person on the construction site. One task. One tool. One result. This is especially true for simple classification, embeddings, semantic search, routing, filtering, ranking, small local decisions. Not every AI problem needs an LLM. Not every website needs a full inference engine. Not every user should pay a 250 MB download tax because we were too lazy to think smaller. So I started digging I wanted something simple: runs in the browser does not require a server for inference small enough to actually ship works with transformer-style models can tokenize text can run BERT-like forward inference can produce embeddings or classification input does not bring ONNX Runtime, Candle, ndarray, or half the internet with it At first I thought: “Surely someone already made the tiny version.” There are great tools out there. Transformers.js is powerful. ONNX Runtime Web is powerful. Candle is powerful. But that was exactly the problem. They are pow
I'll be honest, when I first heard about Astro, I was skeptical. Another JavaScript framework? I already had React, Next.js was doing fine. Why bother? Then I actually used it. I built the website for Zimnovate, my AI-native digital product studio based in Harare, Zimbabwe, with Astro, ran it through Google PageSpeed Insights, and got scores I'd never seen before on a site I actually built myself. That changed everything for me. Let me tell you what Astro is, why it's architecturally different, and why it's worth learning, especially if you care about performance and SEO. What Even Is Astro? Astro is a web framework built around one radical idea: ship zero JavaScript by default . Most modern frameworks (React, Vue, Svelte) are component-based and hydrate the entire page on the client. Even if your page is mostly static content, the user's browser still downloads and runs JavaScript to render it. Astro flips this. It renders your components to pure HTML at build time. JavaScript only runs in the browser when you explicitly need it, and only for the specific components that need it. This isn't just a config option. It's the core architecture. The Architecture: Islands Astro uses a pattern called Islands Architecture . Think of your page as a static ocean with interactive "islands" floating in it. The ocean (static content, headings, text, images) ships as plain HTML. The islands (a navbar with a dropdown, a contact form, a live counter) are the only parts that hydrate with JavaScript. --- // This runs only at build time zero runtime cost const services = await fetch('/api/services').then(r => r.json()) --- <html> <body> <!-- Pure static HTML, no JS needed --> <h1>Zimnovate</h1> {services.map(service => <ServiceCard service={service} />)} <!-- This island hydrates only when visible --> <ContactForm client:visible /> </body> </html> The client:visible directive tells Astro: "only load this component's JavaScript when it scrolls into the viewport." You get full interacti