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If you've ever watched a hacker movie and seen someone plug in a USB and own a machine in seconds — that's not Hollywood magic. That's a Rubber Ducky. And I built one for under ₹150. Here's exactly how I did it, what it taught me, and why every security student should build one. What Even Is a Rubber Ducky? A Rubber Ducky is a USB device that pretends to be a keyboard. The moment you plug it in, the operating system trusts it completely — because keyboards don't need driver approvals or admin permissions. Once trusted, it starts "typing" pre-programmed commands at superhuman speed. We're talking 1000 keystrokes per second. By the time you blink, it's already opened PowerShell, run a script, and closed the window. The original Hak5 Rubber Ducky costs around $80. I built mine for ₹150. What I Used DigiSpark ATtiny85 — ₹120–150 on Amazon India Arduino IDE — free A Windows test machine (my own laptop) 15 minutes That's it. No soldering. No special skills. Just a tiny microcontroller the size of a thumb. Setting It Up Step 1 — Install Arduino IDE Download from arduino.cc and install normally. Step 2 — Add DigiSpark Board Support Go to File → Preferences and paste this into Additional Board Manager URLs: http://digistump.com/package_digistump_index.json Then go to Tools → Board → Board Manager, search Digistump and install. Step 3 — Install Drivers DigiSpark needs Micronucleus drivers on Windows. Download from the official Digistump GitHub and run the installer. Step 4 — Write Your First Payload This opens Notepad and types a message — my first ever "attack": cpp#include "DigiKeyboard.h" void setup() { DigiKeyboard.delay(2000); DigiKeyboard.sendKeyStroke(KEY_R, MOD_GUI_LEFT); // Win+R DigiKeyboard.delay(500); DigiKeyboard.print("notepad"); DigiKeyboard.sendKeyStroke(KEY_ENTER); DigiKeyboard.delay(1000); DigiKeyboard.print("Hello. Your keyboard is now mine."); } void loop() {} Upload it, plug in the DigiSpark, and watch it type on its own. That moment hits different when y

Introduction In the early 2000s, email spam was rampant, cluttering inboxes with unsolicited messages promising quick riches or promoting dubious products. Fast forward to today, and a similar phenomenon is occurring in the world of open-source software: Pull Request (PR) spam. Much like its email predecessor, PR spam is becoming a major nuisance for developers and maintainers, disrupting workflows and compromising the integrity of collaborative software projects. This blog post explores the parallels between early 2000s email spam and contemporary PR spam, examines the motivations behind this new wave of digital clutter, and discusses potential solutions to mitigate its impact. The Rise of PR Spam The Allure of Contribution Metrics One of the primary drivers behind PR spam is the increasing emphasis on contribution metrics in the open-source community. Platforms like GitHub have made contributing to projects more accessible, and many developers are eager to showcase their activity through public repositories. However, this focus on quantity over quality can lead to an influx of low-effort or irrelevant PRs. An example of this is Hacktoberfest, an annual event encouraging contributions to open-source projects. While well-intentioned, it has, in some instances, resulted in a deluge of superficial PRs. Contributors seeking to meet participation thresholds often submit changes that are trivial or unnecessary, much like the spam emails of old that inundated our inboxes with irrelevant or nonsensical content. Automated PR Generators Another factor contributing to the rise of PR spam is the use of automated tools that generate pull requests. These tools can be beneficial for routine tasks such as dependency updates or code formatting. However, when misused, they can lead to a flood of PRs that lack genuine human oversight or consideration, akin to the automated email spam generators that once plagued communication networks. For instance, a tool might automatically submit

Hey Dev Community, If you are running enterprise-scale web scrapers, pricing monitors, or data ingestion pipelines for LLMs, you’ve probably spent sleepless nights dealing with network latency and sudden 403 blocks. When choosing an infrastructure partner, every provider pitches the same script: "99.9% uptime guarantees, millions of residential IPs, and lightning-fast response times." But in the trenches of real-world data collection, we all know that marketing numbers rarely match production reality. Last quarter, my team ran an exhaustive infrastructure audit to compare proxy providers pricing performance and infrastructure stability. If you want to dive straight into our live dataset, telemetry scripts, and interactive monitoring utilities, you can check out the full workbench at ProxyVero . Here is a technical breakdown of how we built our benchmarking matrix, and the architectural gaps we discovered across mainstream enterprise proxy services. 📊 1. The Core Metrics: Uptime vs. Success Rates The biggest lie in the networking industry is confusing Server Uptime with Request Success Rate . A proxy gateway server can maintain a 99.9% uptime while the underlying residential peer network is failing 20% of your data collection requests due to strict target WAFs or high peer churn. When conducting our proxy providers uptime guarantees performance benchmarks , we evaluated three core parameters: TCP Handshake Latency : The time it takes to establish a connection with the proxy endpoint. TTFB (Time to First Byte) : Critical for parsing dynamic JavaScript targets. HTTP Status Code Reliability : Tracking the exact ratio of 200 OK vs. 403 Forbidden / 429 Too Many Requests . ⚖️ 2. The Big Three: Oxylabs vs Bright Data vs SmartProxy Comparison To provide an objective proxy network performance benchmarks comparison , we deployed standard headless browser worker instances (Playwright/Puppeteer) routed through different enterprise gateways. Below is a high-level summary of our a

AI COO that runs your team in tools you already use Discussion | Link

Backed by Mayfield and Aramco Ventures, Vishal Sikka’s new venture brings together veterans from SAP, Infosys, and VianAI.

Right now he's merely a several-hundred-billionaire, according to Bloomberg's Billionaires Index.

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Thariq Shihipar, engineering lead for the Claude Code team, recently published a blog post (Using Claude Code: The Unreasonable Effectiveness of HTML) arguing that HTML, with its richer visualizations, color, and interactivity, improves the productivity of human-agent communication in many settings, especially when compared to default Markdown outputs. By Bruno Couriol

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The silicon race is heating up amid the struggle to keep up with demand.

While AI dominates the layoff narrative, engineers as a share of total new hires have actually increased, according to SignalFire data.

"Contract values for these efforts ballooned from nearly $2.8 billion to $5.9 billion."

Just days after a gunman killed conservative activist Charlie Kirk, it became clear that President Donald Trump would use the assassination to fuel a crackdown on free speech. To avenge Kirk's death, the administration vowed to go after so-called "antifa" (otherwise known as antifascist) terrorists. Now that promise is bearing fruit. This week, eight Texas […]

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Microsoft just added a cheaper 12-inch Surface Pro and 13-inch Surface Laptop to its lineup. Both models come equipped with 8GB of RAM instead of 16GB, costing $849 for the specced-down Surface Pro and $949 for the Surface Laptop, as spotted earlier by Windows Central. When the 12-inch Surface Pro and 13-inch Surface Laptop launched […]

got annoyed that pastebin and similar sites log everything and keep your text forever, so i built one where the server literally cant read what you paste. heres how the encryption actually works and what i learned building it the problem most paste sites work like this: you type something, it goes to their server as plain text, and it sits in their database. they can read it. their employees can read it. anyone who breaches them can read it. and a lot of them keep it forever even after you think its gone. i didnt want to just promise not to look at your stuff. i wanted it so that i cant look even if i wanted to. the idea: encrypt before it leaves the browser the trick is that all the encryption happens on your side, in the browser, before anything gets sent. the server only ever sees scrambled bytes. the key never touches the server at all, it lives in the part of the url after the # , which browsers dont send in requests. so the flow is basically: you paste text browser generates a random key text gets encrypted with that key only the encrypted blob goes to the server the key gets stuck in the link after a # whoever opens the link decrypts it locally the actual code modern browsers have the Web Crypto API built in, so you dont need any library for this. heres the encrypt part, stripped down: \ `js async function encrypt(text) { const key = await crypto.subtle.generateKey( { name: "AES-GCM", length: 256 }, true, ["encrypt", "decrypt"] ); const iv = crypto.getRandomValues(new Uint8Array(12)); const encoded = new TextEncoder().encode(text); const ciphertext = await crypto.subtle.encrypt( { name: "AES-GCM", iv }, key, encoded ); // export the key so we can put it in the url const rawKey = await crypto.subtle.exportKey("raw", key); return { ciphertext, iv, rawKey }; } ` \ the ciphertext and iv go to the server. the rawKey gets base64'd and dropped into the link after the # . decrypting is just the same thing in reverse with crypto.subtle.decrypt . the thing that tripped

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Hey dev community! 👋 As developers, our inboxes often turn into a graveyard of job alerts (LinkedIn, Indeed, ZipRecruiter) and tech newsletters we subscribe to with the intention of "reading later" but never actually open. The result? Important emails get lost, and we get the dreaded "Account storage is almost full" notification. Recently, I hit that wall. I had thousands of accumulated emails. While Gmail allows you to create filters for incoming mail, it doesn't have a native feature to say: "Delete this email automatically after 7 days" . So, I decided to solve it the way we solve everything: by writing some code. 🛠️ The Solution: Google Apps Script + JavaScript Since the Google Workspace ecosystem runs on a JavaScript-based environment, I put together a custom script. Fun fact: a simple loop originally failed due to Google's strict 6-minute execution limit. To fix this, I optimized the code to process emails in batches of 100 , preventing the server from timing out. Here is the final production-ready script: function cleanSpamTsunami() { // 1. Loop to delete ALL Job Board emails in batches of 100 var continueJobSearch = true; while (continueJobSearch) { var jobThreads = GmailApp.search('computrabajo OR indeed OR linkedin OR OCC OR neuvoo OR talent.com OR jooble', 0, 100); if (jobThreads.length > 0) { Logger.log('Deleting a batch of ' + jobThreads.length + ' job alert emails...'); GmailApp.moveThreadsToTrash(jobThreads); } else { Logger.log('No more job alerts found!'); continueJobSearch = false; // Break the loop } } // 2. Loop to delete old Newsletters (older than 7 days) in batches of 100 var continueNewsletters = true; while (continueNewsletters) { var newsletterThreads = GmailApp.search('unsubscribe OR "cancelar suscripción" older_than:7d', 0, 100); if (newsletterThreads.length > 0) { Logger.log('Deleting a batch of ' + newsletterThreads.length + ' old newsletters...'); GmailApp.moveThreadsToTrash(newsletterThreads); } else { Logger.log('No more old newslett

One universal AI agent sounds convenient. One agent to read tickets, write code, generate tests,...

NASA will begin mapping the galactic bulge with a mission later this summer.