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Amazing : Researchers from ETH Zurich in Switzerland, however, managed to create a new type of pixel that can simultaneously do both. This hypercharged pixel, called a Fourier pixel, can generate and sense arbitrary light fields and tap into a pixel’s full potential for carrying information by manipulating light’s intensity, oscillation phases, and polarization. The team reported its findings in a paper published yesterday in Nature. We are one step closer to 1984 technology: The telescreen received and transmitted simultaneously. Any sound that Winston made, above the level of a very low whisper, would be picked up by it; moreover, so long as he remained within the field of vision which the metal plaque commanded, he could be seen as well as heard. There was of course no way of knowing whether you were being watched at any given moment...

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Co-founded by serial entrepreneur Shai Morag, Israeli identity management startup Oak is emerging out of stealth with $60 million in seed funding.

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RGB TVs are the latest hotness in the world of televisions, and I’ve tested many of the latest models to see which you should buy.

Have you ever been in a computer lab, classroom, or office where you needed to quickly send a file between your phone and laptop? I run into this problem all the time. Sometimes there's no USB cable, no pendrive, Bluetooth is painfully slow, or uploading to cloud storage just to download the file on another device feels unnecessary. So I decided to build SilentShare . What is SilentShare? SilentShare is a browser-based peer-to-peer file sharing application that lets you instantly share: 📁 Files (up to 50 MB) 💻 Code snippets 📝 Text 🖼️ Images No installation. No account. No server storing your files. Your data goes directly from one device to another using WebRTC . Whether you're sending files from your phone to your laptop, between classmates, or across the internet, SilentShare keeps the process simple. Why I Built It I wanted something that: Opens instantly in any browser Doesn't require creating an account Doesn't upload files to someone else's server Works on desktop and mobile Feels lightweight and fast Instead of relying on cloud storage, I wanted the browser itself to become the transfer tool. Features ✨ Peer-to-peer file transfer using WebRTC 📂 File sharing up to 50 MB (including ZIP files) 🔒 Optional end-to-end encrypted rooms using AES-GCM 📷 QR code invitations with built-in camera scanner 📊 Live progress, transfer speed, ETA, pause & resume 🖼️ Preview support for: Images Audio Video PDFs 💻 Share code snippets with syntax highlighting 👥 Multi-user rooms (around 5 participants) 🌙 Dark & Light mode 📱 Installable as a Progressive Web App (PWA) How It Works Create a room Receive a random room code Share the code, QR code, or invite link Other devices join Start sharing instantly The files are transferred directly between devices instead of passing through a storage server. Privacy One of the goals of SilentShare was privacy. No user accounts No cloud storage No permanent database Nothing stored after the browser tab closes If you set a room password, all transf

While we can build simple applications without using classes using PySide6, But in big applications and Massive coding systems We should use Classes But why? To understand why do we need classes in PySide6 We should first see the Python code First from PySide6.QtWidgets import QApplication , QWidget , QPushButton , QLineEdit import sys class MainWindow ( QWidget ): def __init__ ( self ): super (). __init__ () button1 = QPushButton ( " Button 1 " ) input = QLineEdit () if __name__ == " __main__ " : app = QApplication ( sys . argv ) window = MainWindow () window . show () app . exec () Before talking about why do we need Classes for PySide6 Let's Explain the code first line by line The imports first thing we make the imports we do need: from PySide6.QtWidgets import QApplication, QWidget, QPushButton, QLineEdit The QApplication Is the simply the application we will make, Like empty app on the RAM it do nothing but it's on the RAM if it's alone And the QWidget Is the Blank screen That will be placed on the Empty Application in the RAM The QPushButton Is like any button we are saying in any app Like the Subscribe button on YouTube or like Post button on Twitter QLineEdit is the input bar, Like the input bar of ChatGPT where you put on it your prompt or like The input bar in WhatsApp Where you type any thing on it to send it to your friends The class And finally The thing You clicked on the post for First thing we define the class How can we define it? Why do we need to define it? Why do even we want it? Who created it? (NOOO IAM JUST KIDDING) We can simply define the class in python by just typing class That's it just class then the name of it For Example MainWindow and then a little semi-colon : OR EVEN WE GIVE IT A Parents And Why do we need to define it, For simply use it BRILLIANT RIGHT? And we want the classes in PySide6 for give it a parents QWidget or even QMainWindow , And we will explain both of them right now but before it Let's explain first what does parents

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Try the demo: K-Saju Crew For fun only. K-Saju is an entertainment project. The K-Pop roles below are a playful interpretation of saju-inspired signals, not personality assessment or advice. A two-person compatibility page can stay stateless with almost no effort. Put both birth dates in a URL, render the result on the server, and the link is the record. No account, no database, no cleanup job. That was already a product rule in K-Saju. We do not retain personal inputs. A result is reproducible from its GET parameters. Then we built /crew : “What if your friend group debuted as a K-Pop group?” A creator makes a link, sends it to a group chat, and each friend enters their own birth date. At three to seven members, the app assigns distinct positions, shows pairwise chemistry, and creates a shareable poster. The fun part is the casting. The engineering problem is that the social flow needs a temporary shared state. A link cannot accumulate submissions by itself. This post is about the decisions behind that feature: where we allowed state, how we made the result durable without retaining a lobby forever, and how we kept the casting explainable instead of treating it as a black-box score. The conflict: a self-service group flow needs somewhere to collect data There were two clean but incomplete options. The first was to keep everything stateless. The creator would enter all members' dates at once, then receive a result URL. It matched our existing architecture, but it defeated the point of sharing a link. The person who starts the group often does not know everyone else's date, and asking them to collect it in a chat creates friction before the feature has started. The second was a conventional persistent group object. It would make joining easy, but it would turn a deliberately stateless service into one that keeps user-provided dates indefinitely unless we built retention and deletion policies around it. We chose a hybrid instead: The lobby is temporary state. It store

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Why async matters for video I've been running useKnockout - a background removal API that processes images in ~200ms - for a few months. Images are fast enough to handle synchronously: POST a file, wait 200ms, get a PNG back. Video is different. Even a 5-second clip at 30fps is 150 frames. At 200ms per frame, that's 30 seconds of processing. You can't hold an HTTP connection open for 30 seconds and call it a good API. So today I shipped POST /video/remove - async video background removal that returns a job ID immediately, processes in the background, and gives you ProRes 4444 (RGB+alpha) when it's done. What shipped As of v0.11.0 (July 10, 2026): POST /video/remove - upload a video, get a job ID back GET /jobs/{job_id} - poll for status, download the result when ready ProRes 4444 output - RGB with full alpha channel, ready to drop into Premiere/Final Cut/DaVinci Node SDK videoRemove() and getJob() in v0.7.0 Python SDK video_remove() and get_job() in v0.7.0 Billing is a dedicated video.seconds meter at $0.10/sec (different from the per-image rate), with a 15-second cap to keep costs predictable. How to use it (Node SDK) import { useKnockout } from ' useknockout-node ' ; import fs from ' fs ' ; const client = useKnockout ({ apiKey : process . env . KNOCKOUT_API_KEY }); // Submit the video const job = await client . videoRemove ({ file : fs . createReadStream ( ' ./input.mp4 ' ) }); console . log ( ' Job ID: ' , job . id ); // Poll until done let status = await client . getJob ( job . id ); while ( status . status === ' processing ' ) { await new Promise ( resolve => setTimeout ( resolve , 2000 )); status = await client . getJob ( job . id ); } if ( status . status === ' completed ' ) { // Download the ProRes 4444 result const video = await fetch ( status . result_url ); const buffer = await video . arrayBuffer (); fs . writeFileSync ( ' ./output.mov ' , Buffer . from ( buffer )); } The job object includes duration_seconds (billed amount), status ( processing / complet

Auth0’s direction is clear: new extensibility work should be built with Actions, not Rules. Auth0’s docs recommend migrating existing logic step by step, converting pieces of Rule code into Action code, testing in staging, and then rolling out one piece at a time. The platform also highlights that Actions give you modern JavaScript, inline documentation, richer type information, and access to public npm packages. I recently looked at the migration path with one question in mind: how do you move from “old but working” to “clean, testable, future-proof” without breaking login flows? This post is the practical version of that answer. Why Auth0 moved from Rules to Actions Rules were Auth0’s earlier customization layer for authentication flows. Actions are the next-generation extensibility platform, built to replace that model with a more structured developer experience. Auth0 positions Actions as a unified environment with version control, debugging, caching, Node 18 support, and access to millions of npm packages. The biggest shift is not just syntactic. Actions use a modern, promise-based programming model and are organized around triggers such as Post Login. That means you are no longer writing the same kind of callback-style Rule you may have used before; you are moving into a more explicit and modular workflow. The mental model change A Rule usually looks like this: it receives user , context , and callback it runs in a broader authentication pipeline it often mixes business logic with token customization, user metadata updates, and side effects An Action, by contrast, is built around a trigger such as onExecutePostLogin , and it receives an event object plus an api object. Auth0’s migration guide explicitly recommends converting Rule code into Action code in stages rather than copying everything at once. That one change matters because it forces you to separate concerns: what is read from the event what is changed through the API what should happen in this trigger

Image-to-video generation is often described as a simple interaction: upload image -> describe motion -> get video That description hides the real problem. A single still contains only one view of a subject. When we ask a model for a fast camera orbit, a full-body walk, or expressive gestures, we are asking it to invent information that was never present in the source. That is where identity drift, unstable lighting, texture flicker, and waxy faces come from. The useful way to approach Seedance 2.0 image-to-video is not as a prompt-writing contest. It is a constraint-management workflow. Give the model a strong identity anchor, request motion that the source image can support, and evaluate one variable at a time. This post explains that workflow in a way that is useful whether you are animating a product render, a character portrait, an approved client still, or a visual asset for a prototype. Note: Model capabilities, pricing, model availability, and input limits change quickly. Check the current documentation and the terms of the platform you use before committing a production workflow. Why image-to-video is different from text-to-video Text-to-video is excellent when invention is the point. You describe a scene and let the model make creative decisions about characters, lighting, composition, and motion. Image-to-video is the better tool when those decisions have already been made and must remain stable. Situation Better starting mode Why Product hero shot Image-to-video Label, shape, material, and color must remain recognizable Character-led sequence Image-to-video One strong reference can anchor a character across clips Approved campaign still Image-to-video The source already represents the accepted art direction Atmospheric B-roll Text-to-video Exact subject identity matters less than visual exploration Abstract concept film Text-to-video Inventing a scene is more valuable than preserving one Existing brand-photo library Image-to-video Stills become reusable