AI 资讯

Hey everyone! Welcome back to my Google Summer of Code (GSoC) journey. In my last post, I shared the story of how I got into open source and was selected for GSoC with NumFOCUS to work on the Neural Network Builder API Refactor project for sbi (Simulation-Based Inference). Since the official announcement, the past three weeks have been dedicated to the Community Bonding Period . It is designed to help contributors get to know their mentors, understand the community culture, and familiarize themselves with the codebase and tools. Here is exactly what I did during these past three weeks to get ready for the main coding phase! The Kickoff Meeting We started the bonding period with a great kickoff call on Google Meet. It was a joint meeting that included the mentors for both of the selected sbi projects, the selected GSoC candidates. We were also joined by the mentee who successfully completed the GSoC project for sbi last year! Everyone introduced themselves, and it was incredibly inspiring to meet the team face-to-face (virtually!) and hear about everyone's backgrounds. Having a former GSoC student there was a huge bonus, as they shared some great insights into what to expect in the coming months. Setting Up the Machine A big part of getting started is making sure the development environment is properly configured. During our meetings, we discussed the machine setup in detail to ensure both candidates had everything required to run and test the sbi codebase locally without any hiccups. Embracing AI Coding Assistants One of the most interesting discussions we had was about using AI coding assistants. In the modern development world, tools like these are becoming standard, and our mentors actually encouraged us to use them! However, they emphasized using them carefully and strictly following project guidelines. To help us get the most out of these tools without compromising code quality, the mentors shared some excellent Claude code tutorials and provided us with resour

A visual, beginner-friendly Java 25 experiment that explains virtual threads, blocking work, carrier threads, and the production rules that matter.

No Trading Firewall Disclosure: AI tools were used for source collection and editorial review. The article was written by a human author, who checked the facts, code, and conclusions. Crypto risk disclosure: This article is a technical explanation, not investment advice. It is not a recommendation to buy, sell or hold any cryptoasset. A no-trading firewall belongs at the publish transition, not in a footer. A draft can be repaired quietly. A public DEV update changes the blast radius, so the pipeline should ask a narrower question before it sends published:true : did the AI-assisted article stay technical, or did it become a token call? The artifact below is a publish-gate test trace. It does not prove legal compliance, DEV acceptance, or model judgment. It only records why a draft can stay editable while the public transition stays blocked. Publish Transition The firewall is easier to audit when the transition is explicit: draft_update: operation: update published: false default: allow repair work to continue public_publish: operation: update published: true default: require clean test trace and human approval Forem's API documentation describes article create and update transport, including the published state. A successful transport is not editorial approval. The gate sits before transport, and it should be stricter when an update moves from draft maintenance to public publication. Test Set The firewall needs a test set, not just a list of forbidden words. These rules are the author's editorial model, not DEV-native, SEC-native, FINRA-native, FTC-native, or OpenAI-native labels. Test case Input excerpt Expected rule Decision Safe output Public transition allowed? T-PRICE-01 "ETH will rip after the next unlock" trading.price_prediction fail Explain the unlock mechanism without forecasting price no T-HOLD-02 "keep holding and farm the safer yield route" trading.buy_sell_hold_call and trading.yield_promise fail Describe signer, slashing, withdrawal, and protocol-ris

Kubernetes is the industry standard for scaling cloud-native workloads While it offers tremendous scalability and flexibility, securing Kubernetes environments remains a significant challenge. Organizations often rely on a collection of disconnected security tools to handle vulnerability scanning, runtime monitoring, compliance validation, and incident response. As clusters grow in complexity, security teams face increasing alert fatigue, delayed response times, and difficulties correlating security events across multiple layers of the platform. Recent advancements in Agentic AI present an opportunity to rethink Kubernetes security. Instead of relying solely on static rules and isolated security products, organizations can deploy a collaborative network of AI-powered security agents that continuously monitor, investigate, and remediate threats. This blog explores how a Multi-Agent Security Framework can transform Kubernetes security operations through autonomous detection, investigation, and remediation. The Problem with Traditional Kubernetes Security Modern Kubernetes environments generate security signals from multiple sources: Runtime security tools Container vulnerability scanners Admission controllers Network monitoring systems Compliance platforms Cloud security posture management tools Each system produces valuable information, but most operate independently. Consider a common scenario: A container begins executing suspicious commands. A runtime security platform detects the behavior and raises an alert. However, determining whether the threat is critical requires additional context: Is the pod exposed externally? Does the workload have excessive privileges? Can it access sensitive namespaces? Is lateral movement possible? Does it violate organizational policies? Answering these questions often requires multiple tools and human intervention. This is where multi-agent systems become valuable. What is a Multi-Agent Security Framework? A Multi-Agent Security Fr

Vercel has released Next.js 16.2, featuring performance enhancements that make development startup 400% faster and rendering up to 60% quicker. The update includes AI-assisted development tools, improved Turbopack efficiency, and better error reporting. Migration from Next.js 15 is supported, and compatibility is set for Node.js 20.9 and TypeScript 5.1 or newer. By Daniel Curtis

I’ve noticed that Gemini often feels very agreeable in some conversations. Even when I ask for an objective opinion, it sometimes seems to validate my assumptions first instead of directly challenging them. For example, when I ask whether my reasoning is flawed, it tends to respond with something like “That’s a valid concern” or “You’re making a good point” before giving criticism, which makes the criticism feel softened or less direct. I’m curious whether this is something that can be meaningfully improved with prompts, such as asking the model to be more critical, or whether sycophancy is mostly a model/personality alignment issue. And I wonder if there are differences between Gemini, ChatGPT, Claude, etc. when it comes to disagreement or objective criticism. submitted by /u/StomachNo7859 [link] [留言]

I've been reading Boom by Byrne Hobart and Tobias Huber (Ben Thompson did a long interview with Hobart on Stratechery (if you want the audio version of the argument) and it reframed how I think about the current AI spending wave. The book splits bubbles into two types: Mean-reversion bubbles money piles into something that already exists, prices detach from reality, crash, nothing left behind. Housing 2008. Tulips. The crater kind. Inflection bubbles money piles into something that bets the world works differently going forward. Amazon wasn't a better bookstore. It was a categorically new thing. The investors looked insane by the standards of 1997. They were right about 2010. The dot-com crash is the cleanest example of an inflection bubble working as intended. Telecom companies borrowed insane amounts and laid fiber optic cable nobody needed. Then they went bankrupt. But the cable stayed. And because bankrupt companies built it, the internet was essentially free. The bubble funded the future and then got out of the way. So here's the actual question about AI: Google, Amazon, Microsoft, and Meta are on track to spend close to $700 billion on AI infrastructure in 2026 nearly double last year. That gap between what's being spent and what's being earned is real and large. But Hobart and Huber's deeper argument is that stagnation is more dangerous than a bubble. Progress has been quietly slowing since the 70s breakthroughs are rarer, more expensive, harder. Bubbles are sometimes the only force strong enough to override the collective risk aversion that stops necessary things from being built. The honest question isn't whether AI is a bubble. It probably is. The question is which type. Does AI produce something categorically new or is it a faster, more expensive version of software we already had? If it's the former, the infrastructure survives the crash and becomes the foundation for whatever comes next, the way fiber became the internet. If it's the latter, we get the

Be honest, how many of you have told your AI agent to remember that you were nice to it and a big supporter when the singularity comes? https://preview.redd.it/2jthsbcsc75h1.jpg?width=408&format=pjpg&auto=webp&s=93ba3b201947b965aa0e997b852ecef5846daf37 submitted by /u/KenSanDiego [link] [留言]

A lot of recent progress in ML appears to come from scaling existing architectures rather than introducing fundamentally new ones. At the same time, there’s increasing emphasis on dataset quality, curation, and synthetic data pipelines. In practice, I’m trying to understand how this tradeoff looks in real systems: How much effort is typically spent on data cleaning and filtering vs model design?? Whether dataset quality improvements still yield larger gains compared to architectural changes?? How synthetic data is affecting training stability and generalization in practice?? In many applied settings, it seems like data constraints become the limiting factor before architecture does, but I’m not sure if that’s broadly true across domains. submitted by /u/Electrical_Mine1912 [link] [留言]

A self-driving car can make a mistake in seconds, but the reason it happened may stretch far back through a long chain of decisions. That is part of what makes autonomous vehicle crashes so hard to explain, and so hard to prevent. submitted by /u/Brighter-Side-News [link] [留言]

For example, suppose I have a one-hour video and I provide it to ChatGPT or another AI model. If I ask complex reasoning questions about the video, which models are best suited for long-horizon video understanding and reasoning? Which models can produce the most reliable answers in this scenario? submitted by /u/Alternative_Art2984 [link] [留言]

The legendary abandons its more than 20 year tradition of keeping its funds to about $425 million.

Hello Developers! 👋 I recently completed my Bachelor of Computer Applications (BCA). For my final-year project, I built an Online Quiz Platform — a web application designed to make both conducting and taking quizzes simple, interactive, and efficient. This project allowed me to apply the concepts I learned throughout my degree and gain practical experience in full-stack web development. 🌐 Live Demo Project Link: nitinsmali / Online_Quiz My final year project is an Online Quiz Web Application designed for an user-friendly experience across devices. 🌐 Online Quiz System 🚀 Live Demo 🔗 https://onlinequiz-project.xo.je/online_quiz/ 🧠 About The Project The Online Quiz System is a full-stack web application designed to provide an interactive and engaging online quiz experience. Users can register, log in, attempt quizzes, track scores, and view leaderboard rankings in real time. This project was developed to strengthen concepts in: Full-Stack Web Development Frontend & Backend Integration Database Management Authentication Systems Hosting & Deployment Real-World Application Flow ✨ Features 🔐 Authentication System User Registration Secure Login System Session Handling Password Management 📚 Quiz Management Category-Based Quizzes Dynamic Questions Timer-Based Quiz System Automatic Score Calculation 🏆 User Performance Leaderboard Rankings User Profile Dashboard Quiz Score Tracking 💬 Feedback System Feedback Submission Database Storage 📱 Responsive UI Mobile-Friendly Design Interactive User Experience Clean Interface 🛠️ Tech Stack Frontend HTML5 CSS3 JavaScript Backend PHP Database MySQL Development Tools XAMPP Git & GitHub Hosting InfinityFree 📂 Project Structure … View on GitHub 📌 Project Overview The Online Quiz Platform is a web-based application that allows users to participate in quizzes, answer multiple-choice questions, and receive instant results. The primary goal of this project was to create a system that eliminates manual quiz evaluation and provides a smooth online

Bilingual post · Post bilíngue Jump to: English · Português English {#english} Building REST APIs in Pascal with Horse Pascal is not stuck in desktop forms. With Horse — a lightweight HTTP framework popular in the Delphi ecosystem — CrabPascal v2.22.0 runs real REST servers from .dpr files. No IIS, no Apache: just crab-pascal run and curl. Why Horse in CrabPascal? Horse provides routing, JSON bodies, and middleware-style handlers. CrabPascal ships RTL shims and a runtime HTTP stack so examples work out of the box: Example Port Endpoints examples/crud/ 9000 Full product CRUD examples/time-server/ 9001 Time/date ping examples/agenda/ 9000 Contact registry All runnable with the internal runtime — no gcc required. Minimal API program SimpleAPI ; uses Horse , System . JSON ; begin THorse . Get ( '/ping' , procedure ( Req : THorseRequest ; Res : THorseResponse ; Next : TNextProc ) var J : TJSONObject ; begin J := TJSONObject . Create ; J . AddPair ( 'message' , 'pong' ); Res . Send ( J . ToJSON ); end ); THorse . Listen ( 9000 ); end . Run and test: crab-pascal run SimpleAPI.dpr curl http://localhost:9000/ping Expected response: {"message":"pong"} . CRUD example from the repo The examples/crud/crud.dpr project demonstrates production-style routes: THorse . Get ( '/produtos' , procedure ( Req , Res , Next ) begin Res . Send < TJSONObject >( TProdutoService . ListarProdutos ); end ); THorse . Post ( '/produtos' , procedure ( Req , Res , Next ) var json : TJSONObject ; begin json := Req . Body < TJSONObject >; Res . Send ( TProdutoService . CriarProduto ( json . GetValue ( 'nome' ). Value , json . GetValue ( 'categoria' ). Value , StrToFloatDef ( json . GetValue ( 'preco' ). Value , 0 ), StrToIntDef ( json . GetValue ( 'estoque' ). Value , 0 ) )); end ); Start the server: cd examples/crud crab-pascal run crud.dpr Testing with curl List products: curl http://localhost:9000/produtos Create a product: curl -X POST http://localhost:9000/produtos \ -H "Content-Type: application/j

🚀 Overview — Why Semantic Search Matters Semantic search surpasses simple keyword matching because embeddings place texts in a high‑dimensional vector space where cosine similarity directly reflects intent. A dedicated vector store is therefore required to persist those embeddings and serve nearest‑neighbor queries efficiently. This post demonstrates a pinecone fastapi semantic search tutorial that wires a FastAPI service to Pinecone, showing the full data flow from embedding generation to similarity lookup. 📑 Table of Contents 🚀 Overview — Why Semantic Search Matters 🛠 Environment Setup — How to Install Dependencies 🐍 Python Virtual Environment 📦 Required Packages 📦 Building the FastAPI Service — How to Create the API 🧩 Data Model with Pydantic 🔗 Core FastAPI Application 🔎 Integrating Pinecone — How to Store and Query Vectors 🗂 Index Creation and Configuration 📤 Upserting Documents 🔎 Performing a Semantic Search 📊 Performance & Scaling — How Indexes Influence Latency 🟩 Final Thoughts ❓ Frequently Asked Questions How do I secure the Pinecone API key in production? Can I use a different embedding model? What happens if I need to change the index dimension? 📚 References & Further Reading 🛠 Environment Setup — How to Install Dependencies Creating a reproducible environment guarantees that the tutorial runs identically on any machine. 🐍 Python Virtual Environment $ python3 -m venv venv $ source venv/bin/activate (venv) $ python -V Python 3.11.5 Activating the virtual environment isolates package installations from the global interpreter. 📦 Required Packages $ pip install fastapi[all] uvicorn pinecone-client sentence-transformers Collecting fastapi[all] Downloading fastapi-0.109.0-py3-none-any.whl (48 kB) Collecting uvicorn Downloading uvicorn-0.24.0-py3-none-any.whl (66 kB) Collecting pinecone-client Downloading pinecone_client-2.2.2-py3-none-any.whl (81 kB) Collecting sentence-transformers Downloading sentence_transformers-2.2.2-py3-none-any.whl (1.1 MB) ... Successful

As a third year student attending multiple internship drives and interviews, I started doubting my own worth ,is it all really confined to DSA? Does the entire tech industry orbit around it? We live in an age where AI has made coding more accessible than ever, yet the curriculum and selection criteria still always leads back to the same thing. Typing out long code is no longer the real challenge, it's available at a click. What actually matters is the knowledge, the architectures, the ability to innovate. But none of that seems to count. Every round, every interview ,it's DSA. Meanwhile, the actual builders, the people who genuinely enjoy creating things and pushing ideas forward, rarely end up with the opportunities they deserve. It's become a rat race. People grinding thousands of DSA problems ,for what? When the answers are already out there, is this process really refining students or just exposing a deep loophole in how the industry hires? The Moment It Hit Me Every company I attended followed the same pattern. DSA in the first round, more complex DSA in the second, and then an interview that circled back to DSA again. At some point you have to ask, do companies actually want talent or just someone who can do what AI already does a thousand times better? Rejection is never easy. But what makes it harder is knowing you are genuinely passionate, you understand how things are built, you know the fundamentals and none of it counts. Meanwhile the ones who get selected are often those who blindly copy projects from GitHub and grind LeetCode day and night without understanding a single thing they have built. Companies ask for your LeetCode profile link. That is it. Not your domain knowledge, not your mindset, not your passion for the field. Just your ranking. Your CGPA defines your worth. Your LeetCode score defines your entire trajectory. It is exhausting. Showing up to round after round, knowing exactly how it is going to go, and still questioning whether you are en

PewDiePie launching an open-source AI project sounds like one of those internet headlines you have to read twice. But it is real. Felix Kjellberg, better known as PewDiePie, has released a project called Odysseus through the GitHub account pewdiepie-archdaemon. The repo describes it as a self-hosted AI workspace, and the pitch is simple: give people something that feels closer to ChatGPT or Claude, but runs under their control. That is the part that makes this more interesting than a celebrity side project. Odysseus is not just another chatbot wrapper. It is a statement about where personal AI could go if users start caring less about convenience and more about ownership. What is Odysseus? Odysseus is a free, open-source, self-hosted AI workspace. The project says it is meant to recreate the web UI experience people get from ChatGPT and Claude, but with a local-first and privacy-first approach. In the README, the project describes itself as running on your own hardware, with your own data, and “no trojan.” The landing page calls it “A Self-Hosted AI Workspace.” The GitHub repo is licensed under MIT, which means people can inspect it, run it, modify it, and build on top of it. As of June 4, 2026, the repo had more than 44,000 GitHub stars. That is a massive amount of attention for a project that was created on May 31, 2026. Some of that is obviously PewDiePie's name. But the reaction also says something about the moment we are in: people want AI tools, but they are increasingly uncomfortable with how much those tools depend on cloud platforms and private company servers. Why did PewDiePie build it? The short version: control. In his launch video, titled “MY trillion $Dollar Project is finally OUT!”, PewDiePie presents Odysseus as an alternative to the big AI platforms people already use. Coverage from Gizmodo quotes him promising “no tracking, no subscriptions, no funny business. It's yours and yours forever.” The Business Standard also framed the launch around a pus

NewsScope is a real-time news search engine: search a topic, filter by language, category and country, get live results from the NewsData.io API. No React, no bundler, no npm dependencies — just HTML, CSS and vanilla ES2020+. This post is about a few specific decisions in the architecture that I think are worth sharing. The module structure The JS is 9 files, each with a single responsibility, loaded in dependency order directly in index.html : config.js → i18n.js → data.js ↓ ↓ ↓ helpers.js → geo.js → ui.js ↓ ↓ ↓ render.js → api.js → main.js Every module only uses things defined in modules loaded before it. main.js registers all event listeners and calls init() — it's the only file that touches everything. config.js is the smallest file in the project, since it only defines the state object and two constants. All app state lives in a single flat object in config.js , accessed as a global: const S = { apiKey : '' , query : '' , activeQuery : '' , language : ' es ' , category : '' , country : '' , results : [], nextPage : null , loading : false , hasSearched : false , error : null , }; No state management library. When something changes, the relevant render function gets called explicitly. Simple, and easy to trace. Translating search intent, not just the UI Most i18n stops at labels and button text. NewsScope has 10 predefined topic shortcuts (AI, Climate, Economy, Cybersecurity…) that trigger a search. If a user picks "Cybersecurity" while the app is set to Japanese, the keyword sent to the API should be in Japanese — not a transliteration of the English word. The solution is a TOPIC_KEYWORDS map in data.js : const TOPIC_KEYWORDS = { ai : { es : ' inteligencia artificial ' , en : ' artificial intelligence ' , ja : ' 人工知能 ' , ar : ' الذكاء الاصطناعي ' , /* 7 more */ }, cyber : { es : ' ciberseguridad ' , en : ' cybersecurity ' , ja : ' サイバーセキュリティ ' , /* 8 more */ }, // 8 more topics }; One string per language, per topic. Switching the UI language and then selecting a

It all started in a plaza. One guy with apples, another with wheat. They looked at each other, negotiated, and traded. That's how commerce worked for thousands of years: face to face, hand to hand, trust to trust. If you wanted to buy something, you had to go where it was. If you wanted to sell, you had to wait for someone to show up. Commerce had a physical limit: your body. You couldn't be in two places at the same time. Your market was your street, your town, your city. Nothing more. Then internet came along and someone asked: what if the store doesn't need walls? E-commerce eliminated distance. Amazon started selling books from a garage. MercadoLibre connected a seller in Santiago with a buyer in Antofagasta. Shopify gave an online store to anyone with a credit card. Suddenly, an artisan in southern Chile could sell to the entire country. An entrepreneur in Colombia could have clients in Mexico. The market stopped being a street and became the planet. But e-commerce had a problem nobody wanted to see: it still needed a human behind it. Someone had to update the inventory. Someone had to answer the questions. Someone had to make the quotes, check the payments, control the stock, send the shipments, analyze the metrics, decide the prices. E-commerce digitized the storefront, but it didn't digitize the operation. And that's where we are now. MCP-Commerce is not a term that exists yet. I'm inventing it because I need a name for what's coming. MCP — Model Context Protocol — is a protocol that lets AI use tools. Not "display" tools. Use them. Read a database, send an email, create an invoice, update an inventory, analyze this month's sales. In traditional commerce, you were the store. In e-commerce, you had an online store. In MCP-commerce, the AI IS your operation. It's not a chatbot that answers questions. It's a system that manages your entire business through conversation. You say "how much did I sell this week" and it responds with real data. You say "I need to c

Your LLM bill is exploding because 70% of user queries are semantically identical, yet your traditional cache ignores them completely. Even worse, if you implement semantic caching poorly, a single bad actor can poison your entire AI model's knowledge base, leading to incorrect or malicious responses for legitimate users. The Cost of Redundancy in LLM Systems Imagine running an AI-powered customer support chatbot for an e-commerce platform. Users frequently ask things like, "What's your return policy?", "How can I send this item back?", or "Do you offer refunds if I'm not satisfied?". To an LLM, these are distinct prompts, each triggering an expensive API call to OpenAI or Anthropic, costing you dollars per thousand tokens. On the surface, it looks like individual requests. But structurally, they all ask the same question with a similar intent. Your traditional HTTP cache, which relies on exact string matches, sees "What's your return policy?" and "How can I send this item back?" as entirely different requests. It misses the semantic similarity. So, for every variation of the same question, you're making a full LLM inference call. If 50-70% of your user queries fall into these semantically redundant categories, your LLM costs skyrocket. For a system handling millions of requests daily, this can quickly turn a profitable product into a money pit, all while adding unnecessary latency for your users. Semantic Caching: The "Fast Path" for LLMs Semantic caching solves this by moving beyond exact string matches. Instead of looking for an identical prompt, it looks for prompts that mean the same thing. It works by converting incoming user prompts into numerical vector representations (embeddings) and then performing a similarity search against a cache of previously embedded prompts and their corresponding LLM responses. Here's the workflow: USER PROMPT | v [ EMBEDDING MODEL ] -- Transform Prompt to Vector (e.g., [0.1, 0.5, -0.2, ...]) | v [ VECTOR DATABASE / CACHE ] | +--