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As part of the mass layoffs hitting Xbox, Doom developer id Software has laid off around 50 percent of its staff, according to Game Developer. One source claimed to the publication that the cuts equate to more than 90 redundancies. Another source said that id's QA department was significantly impacted. The report was published the […]

Everyone knows Next.js is the default full-stack JavaScript framework in 2026. The job ads say so,...

Redux setup is a ceremony. You create a store, compose your reducers into a root tree, wrap your app in a Provider, register middleware, and configure enhancers — all before you write a single line of feature logic. SDuX Vault™ replaces that entire ceremony with two function calls and zero root configuration. Redux Store Ceremony A typical Redux application requires several files and configuration steps before state management is operational. Here is what a minimal Redux setup looks like for a single feature: // store.ts import { createStore , combineReducers , applyMiddleware } from ' redux ' ; import thunk from ' redux-thunk ' ; import { userReducer } from ' ./reducers/userReducer ' ; const rootReducer = combineReducers ({ users : userReducer , }); export const store = createStore ( rootReducer , applyMiddleware ( thunk ) ); // App.tsx — Provider wrapper required import { Provider } from ' react-redux ' ; import { store } from ' ./store ' ; function App () { return ( < Provider store = { store } > < UserList /> < /Provider > ); } That is 20+ lines of configuration across multiple files — and it only covers one feature. Add a second feature and you are back in the combineReducers file, composing another slice into the tree. Add middleware and you are threading enhancers through applyMiddleware . Add DevTools and you are composing composeWithDevTools on top. Every new feature touches the root configuration. Redux Requirement What It Does createStore() Creates the single global store instance combineReducers() Composes feature reducers into a root tree applyMiddleware() Registers middleware (thunk, saga, etc.) Provider Makes the store available to all components via context composeWithDevTools() Enables Redux DevTools integration ⚠️ Warning: Every entry in that table is root-level configuration. Adding a new feature means editing the root reducer composition, possibly the middleware stack, and potentially the Provider hierarchy. Root configuration is a shared depende

It's another bad week for the video game industry. Microsoft outlined a series of layoffs on Monday that Xbox CEO Asha Sharma described as "the most significant restructure in Xbox history." But buried in Sharma's memo was a curiously optimistic statement: "I want Xbox to be one of the few companies that entertains more than […]

In a world regulated by devices, humanity has become disconnected from the physical world—from stick-shift cars to postcards.

The lab will beam back data to train AI models to predict how proteins behind age-related diseases like Alzheimer’s and certain cancers behave.

In 2008, Richard Thaler and Cass Sunstein needed an example simple enough to explain the whole of economic theory from a single application. They used a cafeteria menu. By moving the fruit to eye level and sending the fries to the periphery, they demonstrated that a slightly adjusted environment could convince thousands of students to make healthier choices. No one is stopped from choosing fries and the fries-loving student still chooses fries. But the undecided student chooses the apple, simply because it is now right in front of him. In 2017, Thaler was awarded the Nobel Prize in Economics for his body of work, including the development of “The Nudge Theory” with Sunstein. In the years that followed, product teams building mobile applications have taken to referring to any call-to-action on-screen as a nudge. The design of an effective nudge There are two requirements for a nudge to work reliably. The user should have an apparent desire for the thing they are being nudged toward, and the nudge should appear at the moment when this desire is at its peak. The first requirement ensures the user has a mental model of the target action, meaning they understand roughly what they are supposed to do. The second requirement serves to remove any extraneous context, ensuring the nudge does not fail due to poor timing. A checkout confirmation modal shown straight after launching the application will fail miserably as a nudge. The desire to checkout is non-existent at the launch moment, and the context of the action has little to do with the action itself. A tooltip shown after a specific number of unsuccessful attempts to checkout after viewing the cart, however, is a nudge. It has the desired behavior and the right timing. Why the difference shows up in the numbers In the example with Duolingo, the product team has effectively used the framing of an existing streak as a reference point for the next level of engagement. The players who saw streak-based progress notifications

In bioinformatics we are handling a lot of tabular data. Be it VCF files, tabular Blast output, or just creating a CSV or TSV samplesheet. Actually, one of my favorite tabular formats is by using SeqKit to convert Fasta or FastQ files to tabular format, as this allows to do various filtering operations by row , using standard unix tools if so wished. Scrolling through this type of data in the terminal can be messy to say the least though. Although CSVs can of course be imported into a spreadsheet software for viewing, it would be very powerful to be able to view them comfortably right from the terminal, isn't it? To take one example that fits within the code window of a blog post, let's take a selected set of columns from the CSV output from the Mykrobe tool. And to make it emulate another common problem with many csv formats, let's also use tr to convert the _ :s in the headers into real spaces (Mykrobe does not do this, but many other tools do): $ cat SOME_SAMPLE.csv | cut -d , -f 2,3,10,14,15,17,18 | tr '_' ' ' > selection.csv $ cat selection.csv "drug" , "susceptibility" , "kmer size" , "phylo group per covg" , "species per covg" , "phylo group depth" , "species depth" "Amikacin" , "S" , "21" , "99.672" , "98.428" , "372" , "347" "Capreomycin" , "S" , "21" , "99.672" , "98.428" , "372" , "347" "Ciprofloxacin" , "S" , "21" , "99.672" , "98.428" , "372" , "347" "Delamanid" , "S" , "21" , "99.672" , "98.428" , "372" , "347" "Ethambutol" , "S" , "21" , "99.672" , "98.428" , "372" , "347" "Ethionamide" , "S" , "21" , "99.672" , "98.428" , "372" , "347" "Isoniazid" , "R" , "21" , "99.672" , "98.428" , "372" , "347" "Kanamycin" , "S" , "21" , "99.672" , "98.428" , "372" , "347" "Levofloxacin" , "S" , "21" , "99.672" , "98.428" , "372" , "347" "Linezolid" , "S" , "21" , "99.672" , "98.428" , "372" , "347" "Moxifloxacin" , "S" , "21" , "99.672" , "98.428" , "372" , "347" "Ofloxacin" , "S" , "21" , "99.672" , "98.428" , "372" , "347" "Pyrazinamide" , "S" , "21" , "99.672"

I wanted a media library that knew the difference between what should exist and what does. Most automation I tried picked one side. Some tools search well and never track what you already have. Others move files and assume the move worked. I wanted the gap between those two things to be the thing the software acted on. So I built Mediary Scout . You name a movie or a show. An LLM agent searches your indexers, transfers the best match into your own cloud drive, then reads the drive back to confirm what landed and what is still missing. It runs self-hosted. You bring your own drive, your own model, your own metadata key. There are desktop builds for Mac and Windows if you just want to double-click and run it, and a read-only demo if you want to watch one acquisition play out first. The drives it speaks today happen to be Chinese cloud storage (115, Quark, GuangYaPan). That detail does not matter for the rest of this post. The part that took real work was different: handing an LLM tools that move and delete files, and stopping it from doing something dumb with them. Three bugs taught me most of what I now believe about that. The shape of the thing The web app does almost nothing interesting. It writes a row to a Postgres queue and returns. A long-running worker picks up the row and starts a sandboxed agent. The agent gets a small set of tools: search resources, transfer a candidate, list a directory, move files into a season folder, mark episodes as obtained. Every tool runs through a deterministic workflow that owns the actual side effect. The agent proposes. The workflow decides whether the proposal is allowed, performs it, and reads the world back. That split is the whole design. The model is the part I cannot fully predict, so it gets the smallest possible blast radius. The deterministic code around it holds every irreversible action and every check. When I violated that split, things broke. They broke in the order below. Bug 1: the agent searched sixteen times and

Forterra has deployed more than 100

"Cheapest residential proxy" is a search query with a hidden trap: the lowest price per GB and the lowest cost per successful request are not the same number. This post breaks down ten budget-to-mid-tier residential proxy providers from a cost-and-reliability angle, plus a script for measuring the metric that actually matters before you commit real traffic. The trap: price per GB vs. cost per success A proxy at $0.50/GB that fails half your requests is more expensive than one at $1.40/GB with a 98% success rate, because you're paying for retries, wasted bandwidth, and engineering time spent debugging "random" failures. Before comparing sticker prices, calculate: real_cost = traffic_price + failed_request_overhead + retries + setup_time + support_delays Concretely, here's a quick way to model it: def cost_per_success ( price_per_gb , success_rate , avg_response_kb = 50 , retry_overhead = 1.3 ): """ price_per_gb: advertised price success_rate: 0.0-1.0, measured against YOUR target site, not the vendor ' s claim retry_overhead: multiplier for bandwidth wasted on failed/retried requests """ effective_price = price_per_gb * retry_overhead gb_per_request = avg_response_kb / ( 1024 * 1024 ) cost_per_request = gb_per_request * effective_price return cost_per_request / success_rate # Example: cheap provider, mediocre success rate print ( cost_per_success ( 0.50 , 0.75 )) # looks cheap, isn't once failures are priced in # Example: pricier provider, high success rate print ( cost_per_success ( 1.40 , 0.98 )) # often cheaper in practice Run this with your own measured success rate (see the test harness further down), not the vendor's advertised uptime number. What to actually compare Before looking at price, check whether the provider covers: IP pool size and quality (pool size alone tells you nothing about freshness or block rate) Country vs. city-level targeting Sticky session support (for anything stateful) Rotation controls (for scraping/data collection) HTTP(S) and SOCKS5

Imagine you are planning a surprise birthday party. You need invitations, food, decorations, and a cake. You call different places to get these things. You tell each one exactly what you need. "I need 20 red balloons." "I need a chocolate cake for 10 people." This is how many computer programs talk to each other. They use something called an API (Application Programming Interface). An API is like a menu. You pick what you want. You get exactly that. It works well for simple tasks. But what if your party plans change? What if you decide on a theme mid-conversation? Traditional APIs can feel a bit rigid then. They don't always remember your past requests. They don't understand the bigger picture. Now, imagine talking to a super-smart party planner. You start by saying, "I'm planning a party." The planner asks, "For how many people?" You say, "About 20." Then you mention, "It's for a birthday." The planner instantly suggests a cake size. It recommends decorations based on your earlier answers. This smart planner remembers everything you said. It understands your overall goal. It uses something like MCP (Model Context Protocol). MCP is a new way for computers to talk. It's like having a real conversation. It's much smarter than a simple menu order. You will soon understand why this difference is a game-changer. Traditional APIs: The Fixed Menu Approach Let's start with what you might already know. Many apps you use every day rely on APIs. An API is like a waiter in a restaurant. You look at the menu. You tell the waiter your exact order. "I want a cheeseburger with fries." The waiter takes your order to the kitchen. The kitchen prepares only that specific meal. Then the waiter brings it back to you. This is how most apps work together. One app sends a very specific request. It asks for a certain piece of information or to perform a specific action. The other app performs that task. It sends back a very specific response. Think of ordering from an online store. You click

Quick Note In my previous article, I mentioned that Linux Troubleshooting Flow for Beginners would be the final post in this series. While preparing it, I realized there were a few practical Linux skills every beginner should learn first. These topics will make the troubleshooting guide much easier to understand and follow. Before we wrap up the series, we'll cover: Package Management Finding Files & Text Viewing Files Efficiently File Compression Then we'll bring everything together in the final Linux Troubleshooting Flow for Beginners. Introduction Installing software on Linux is very different from Windows. On Windows, you usually download an .exe installer. On Linux, software is typically installed and managed using package managers . This is one of the most practical skills every Linux beginner should learn early. What is a Package? A package is a ready-to-install bundle that contains: The main program Required libraries Configuration files Documentation Examples: nginx , git , docker , curl , vim Think of a package as a ready-to-install software box. What is a Package Manager? A package manager is a tool that installs, updates, removes, and manages software packages. Instead of downloading software manually, you simply run a command. Example: sudo apt install git The package manager automatically: Downloads packages from trusted repositories Install required dependencies automatically Upgrade installed software Removes them cleanly Instead of manual downloading, you just run one command. Why Use a Package Manager? Without package managers, you would have to: Search for software manually Download files from websites Install dependencies yourself Update each application separately Package managers automate all of this. What is a Repository? Package managers download software from repositories. A repository is a trusted online collection of software packages maintained by your Linux distribution. Instead of downloading software from random websites, Linux install

If you've ever built a scraper that worked perfectly in dev and then got blocked or CAPTCHA'd the moment it hit production traffic volume, you already know why proxy choice matters. This post breaks down residential proxies from a practical, implementation-focused angle: what they are, when to use them vs. alternatives, how to wire them into common tools, and how the major providers stack up. TL;DR Residential proxies route requests through real ISP-assigned IPs, so they're harder for anti-bot systems to fingerprint than datacenter IPs. Rotating residential proxies are for scraping/data collection. Sticky sessions (or static ISP proxies) are for anything stateful — logins, checkout flows, long-lived account sessions. Nstproxy is a good default pick if you want residential, static ISP, and mobile proxies under one API/dashboard instead of juggling multiple vendors for different parts of your stack. For large-scale enterprise scraping, Oxylabs and Bright Data have the most mature tooling. For budget/prototype work, IPRoyal, DataImpulse, and Webshare are worth testing. Proxy types, quickly Type Use for Pros Watch out for Residential Scraping, SERP checks, ad verification Looks like real user traffic Usually billed per GB Static ISP Long-lived sessions, account workflows Fast + stable IP Less useful for high-volume rotation Datacenter Speed-sensitive, low-stakes tasks Cheap, fast Easiest to fingerprint/block Mobile Mobile-first platforms/apps Strongest trust signal Most expensive per GB A production-grade scraping/automation stack often uses more than one of these at once — e.g., rotating residential IPs for crawling, and static IPs pinned to specific browser profiles for anything that requires a login. Wiring a residential proxy into your code Most providers give you a host:port endpoint plus username:password auth, and let you control rotation/session stickiness through the username string. A typical setup looks like this: Python ( requests ): import requests proxy_ho

A new report suggests Netflix viewers aren’t sticking around for Season 2. The bigger issue may be that binge-watching itself is no longer the advantage it once was.

SK Hynix is experiencing a boom credited to AI. It will ride that to a multibillion-dollar U.S. IPO, expected to take place on Friday.

Automation is the part of DevOps that actually earns the title. Day 6 was two of the most everyday automation tasks there are, and both had a trap sitting in plain sight. One Linux task, one AWS task. Schedule a recurring job with cron, and launch an EC2 instance from the AWS CLI. The tasks come from the KodeKloud Engineer platform if you want the same lab to work through. Cron: five fields, one rule that trips everyone Cron is the scheduler that has quietly run Linux automation for decades. You hand it a job and a schedule, and its daemon, crond, wakes up every minute to check whether anything is due. If that daemon is not running, nothing fires, so the first move is making sure it is installed and enabled. # Become root sudo su - # Install the cron daemon if it is missing yum install cronie -y # Enable it at boot, start it now, and confirm it is alive systemctl enable crond.service systemctl start crond.service systemctl status crond.service cronie is the package name on RHEL-family distros. enable makes the service survive a reboot, start brings it up right now, and status is how you confirm it is actually running instead of assuming it is. Now edit the crontab: # Edit the current user's crontab crontab -e # minute hour day-of-month month day-of-week command 0 2 * * * /path/to/script.sh # List what is currently scheduled crontab -l That line runs the script every day at 2am. The five fields, in order, are minute, hour, day of month, month, and day of week: minute: 0 to 59 hour: 0 to 23 day of month: 1 to 31 month: 1 to 12 day of week: 0 to 7, where both 0 and 7 mean Sunday Here is the rule that quietly breaks schedules. When you set both the day-of-month field and the day-of-week field to something other than a star, cron treats them as OR, not AND. So 0 0 13 * 5 does not mean midnight on Friday the 13th. It means midnight on every 13th of the month, and also every Friday, whichever lands first. If you actually want the AND, you restrict one field in cron and che

Four years of optimizing React and Next.js projects taught me one thing: unoptimized images are everywhere, and nobody wants to fix them. Every project has the same pattern. Heavy PNG and JPG files are sitting inside /public , there is no consistent image pipeline, and some of those files have no business being that large in a production codebase. This is especially common in small and mid-sized projects. There is no CDN transformation layer or dedicated asset pipeline. Images get added while the product is moving quickly, and the cleanup becomes a task for “later.” Later, of course, never comes. Then, at 1am, while refactoring an extremely vibe-coded Next.js project, I found myself doing the cleanup manually again. Find an image. Upload it to an online compressor. Hit the free limit. Open another tool. Convert a few more. Download everything. Replace the original files. Hunt through the codebase for every import and src path. Hope I did not miss one. And I finally thought: I am a developer. Why am I doing this by hand? So I built pixcrush . npx pixcrush . One command to convert the images, compress them, and update their matching code references automatically. “But doesn’t Next.js already optimize images?” Yes, and if your application uses next/image consistently, you should absolutely take advantage of it. The Next.js <Image> component can resize images for different devices, lazy-load them, and serve modern formats such as WebP. Files inside /public can be referenced from the root URL, while statically imported images also give Next.js access to their intrinsic dimensions. The official Next.js image documentation explains these runtime optimizations in detail. But that solves a different layer of the problem. I wanted to clean up the source assets themselves: Replace heavy PNG and JPG files with smaller WebP files when conversion is worthwhile. Update existing imports and string-based image paths across the repository. Identify images that are no longer reference

We are two friends building Pageonaut , a collection of 126 free browser tools (converters, calculators, PDF and image utilities, dev helpers). Early on we committed to one constraint: everything runs client-side . No file uploads, no accounts, no server-side processing. That one decision shaped the whole architecture, and it broke things in ways I did not expect. Here are the lessons, including the one where our server filled up with 419 GB of cache and took the site down twice. Why client-side only Every time I needed a quick converter, the top search results wanted me to upload my file to their server, create an account, or pay to remove a watermark. For work that a browser can trivially do locally. So the rule became: drop a file into one of our converters and it never leaves your device. You can watch the network tab while using it. This is great for privacy and trust, and it has a nice side effect: our server does almost nothing per user, so hosting stays cheap even if a tool gets popular. The cost: some tools are genuinely harder to build. PDF manipulation in the browser (we use client-side libraries instead of a server queue), image processing on the main thread without freezing the UI, and no "just call an API" escape hatch. When a tool truly needs the network (say, fetching a URL you give it), the page says so explicitly. Lesson 1: Unbounded URL params + ISR = a full disk This is the expensive one. We built shareable challenge pages: beat my score, try this color, that kind of thing. The URLs look like /tools/<slug>/challenge/<value> , where <value> is user-generated. With Next.js ISR, every unique URL that renders gets persisted to the filesystem cache. You can see where this is going. The value space is infinite. Bots found the pattern and started enumerating it. .next/server/app grew to 419 GB . The disk filled up, the site went down, and because we did not understand the root cause immediately, it happened a second time a few days later. The fix was tw

Microsoft is cutting an additional 3,200 jobs beyond those in the gaming division.