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 ] | +--

I didn’t expect to care this much about language tools. I started messing around with two different projects, Linguaboard and Parley , mostly out of curiosity. What I got was a surprisingly clear look at two very different ways we interact with language as developers and builders. Linguaboard: translation as exploration, not just output Linguaboard isn’t trying to give you the translation. Instead, it feels more like it’s saying: “Here are several valid ways this could be expressed, pick what fits your intent.” That shift is subtle but important. Most translation tools optimize for a single “correct” answer. Linguaboard leans into ambiguity in a way that actually helps you understand nuance instead of hiding it. I found myself thinking less like: “What does this mean?” and more like: “How should this sound in context?” Parley: learning through interaction, not memorization Parley takes a completely different angle. Instead of treating language as something to decode, it treats it as something to use. You’re not just passively consuming translations, you’re engaging with patterns, context, and recall in a more active loop. What stood out to me is how quickly it shifts you out of “study mode” and into “usage mode.” It feels closer to building intuition than studying rules. The interesting contrast What I didn’t expect is how well these two complement each other: Linguaboard → helps you understand nuance and meaning Parley → helps you internalize and use language One is about interpretation, the other about retention through interaction. Put together, they highlight something a lot of dev tools miss: Language work isn’t one problem. It’s at least two: understanding, and using. Why this matters (especially for devs) If you’re building anything with multilingual UX, AI translation, or global audiences, you’ve probably hit this wall: Translation APIs give you “correct” text But correctness ≠ clarity, tone, or intent These tools made that gap feel very obvious to me. And o

There's a specific kind of bad animation I notice immediately: the count-up stat that stutters as it ticks, the progress bar that lags a frame behind your scroll, the "active" tab underline that snaps instead of glides. None of it is broken, exactly. It just feels cheap. And nine times out of ten, the cause is the same — the animation is being driven through React state, so every frame triggers a re-render, and the main thread can't keep up. I build motion-heavy interfaces for a living, mostly in Next.js 16 and React 19, and I've landed on a small set of patterns that stay smooth because they bypass React's render loop entirely . They lean on Motion — the library formerly known as Framer Motion. It went independent and got renamed in 2025, so the package is now motion and the import you want is motion/react , not framer-motion ( the APIs are identical, only the import path changed ). Here are three I reach for constantly, plus the reduced-motion discipline that should wrap all of them. The mental model: MotionValues over state The single idea that fixes most jank: a MotionValue is a value Motion tracks outside of React. When it changes, Motion updates the DOM directly via transform or opacity — it does not call setState , so your component doesn't re-render. That's the whole trick. A number ticking from 0 to 4,200 should touch the DOM ~60 times a second and re-render React zero times. If a value changes every frame, it should live in a MotionValue, not in useState . State is for things that change when a user does something; MotionValues are for things that change continuously. Keep that line in your head and the rest of this falls out naturally. 1. A reading-progress bar with useScroll + useSpring The bar at the top of an article that fills as you read. The naive version listens to scroll events and sets state — which is exactly the re-render trap. Motion's useScroll hands you scroll position as a MotionValue already, so there's nothing to re-render. useScroll retu

Bilingual post · Post bilíngue Jump to: English · Português English {#english} Configuring CrabPascal with crabpascal.toml Every serious compiler needs project-level configuration. CrabPascal v2.22.0 reads crabpascal.toml from your project root — search paths, preprocessor symbols, Delphi vs FPC mode, output format, and runtime defaults. Where the file lives The compiler searches in order: crabpascal.toml (project root) .crabpascal.toml (hidden) config/crabpascal.toml If none exist, sensible defaults apply. Add the file when your project grows beyond a single .dpr . Starter configuration [compiler] version = "2.22.0" search_paths = [ "rtl/" , "lib/" , "examples/" ] defines = [ "CRABPASCAL" , "RELEASE" , "MSWINDOWS" ] mode = "DELPHI" # or "OBJFPC" strict = false warnings = true [preprocessor] enabled = true process_includes = true symbols = [] [output] error_format = "vscode" # or "gcc", "delphi" colors = true [runtime] default_http_port = 9000 [paths] rtl_path = "rtl/" output_path = "output/" Place this beside your .dpr file. All CLI commands ( check , run , build-exe ) pick it up automatically. Common use cases Delphi vs Free Pascal mode [compiler] mode = "DELPHI" defines = [ "CRABPASCAL" , "MSWINDOWS" ] Switch to FPC compatibility: [compiler] mode = "OBJFPC" defines = [ "FPC" , "UNIX" ] Mode affects parsing rules and RTL resolution under rtl/ . Custom unit search paths Large projects split units across folders: [compiler] search_paths = [ "rtl/" , "src/units/" , "src/services/" , "third_party/" ] This replaces hardcoded -U flags in scripts. Preprocessor symbols Match Delphi conditional compilation: [preprocessor] enabled = true symbols = [ "DEBUG" , "TESTING" ] Your Pascal code can use: {$IFDEF DEBUG} WriteLn ( 'Debug build' ); {$ENDIF} Run crab-pascal preproc MyApp.dpr to inspect expanded source. CI-friendly error output [output] error_format = "gcc" colors = false show_stacktrace = false GitHub Actions parsers often prefer gcc-style lines. Local development can

I'm in San Francisco, putting together a cracked research lab team of founders who think they can build ASI. If you are interested, let me know on LinkedIn: linkedin.com/in/eliaspfeffer submitted by /u/DasDouble [link] [留言]

Building a Resilient Real-Time Chat System with WebRTC, Faye, and WebSockets: A Practical End-to-End Building a Resilient Real-Time Chat System with WebRTC, Faye, and WebSockets: A Practical End-to-End Tutorial In this tutorial you’ll build a small, resilient real-time chat system that works across browsers, mobile devices, and constrained networks. You’ll learn how to architect a client/server model with signaling, leverage WebRTC data channels for peer-to-peer messaging when available, and fall back to a robust WebSocket-based relay when direct peer connections fail. The focus is on practical patterns, testable code, and observability to keep a live chat running under load or flaky networks. Key takeaways Understand when to use WebRTC data channels vs. WebSocket relays for real-time chat Implement a signaling server to establish WebRTC connections safely and efficiently Build a resilient message delivery pipeline with idempotent processing and retry semantics Instrument the system for latency, jitter, and message loss to avoid silent failures Test end-to-end flows with simulated network conditions and automated resilience tests Overview of architecture Clients (web and mobile) connect to a signaling server to negotiate WebRTC peers and/or WebSocket sessions. If a direct WebRTC peer connection is possible, use a data channel for low-latency chat messages. If WebRTC is blocked (firewalls, NAT), route messages through a relay server using WebSockets. The relay can also bridge peers that can’t connect directly. A lightweight message store (in-memory for demo, with an optional Redis-backed queue in production) provides at-least-once delivery guarantees and retry capabilities. Observability: metrics for connection attempts, handshake times, message delivery latency, retry counts, and error rates. Tracing spans help diagnose cross-service flows. Tech stack (example) Frontend: TypeScript, WebRTC DataChannel, WebSocket Backend: Node.js with Express for signaling API, ws or

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