标签：#ai

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

Part 2 of the Building Enterprise AI Automation Systems Series Introduction One of the biggest obstacles in enterprise AI is not choosing a model. It is finding data. Most tutorials assume that training data already exists. Reality is very different. Large organizations rarely share operational datasets. Financial transactions contain confidential information. Contracts contain sensitive agreements. Invoices reveal commercial relationships. Bank statements expose customer activity. For legal, regulatory, and competitive reasons, these datasets almost never become public. This creates a difficult problem for AI engineers. How do you build intelligent systems when the data you need cannot be accessed? The answer is synthetic data. Unfortunately, most synthetic datasets found online are little more than randomly generated CSV files. They contain names. Numbers. Dates. But they completely ignore something far more important: Business relationships. In this article, we'll explore how to design synthetic enterprise datasets that preserve real business logic and can be used for machine learning, automation, benchmarking, and AI engineering. Random Data Is Not Synthetic Data Many developers believe synthetic data simply means generating fake values. For example: Customer,Invoice,Amount John,INV001,500 Alice,INV002,1200 Bob,INV003,900 Technically, this is synthetic. Practically, it is useless. Why? Because enterprise systems are built around relationships. Invoices belong to contracts. Contracts belong to customers. Payments reference invoices. Purchase orders authorize invoices. Bank transactions settle invoices. Without these relationships, there is nothing meaningful to learn. A machine learning model trained on isolated records learns isolated patterns. Real enterprise automation requires connected data. Thinking Like an Enterprise System Before writing a single line of Python, ask one question: "How does the business actually operate?" Imagine a manufacturing company. A

As ASML CEO Christophe Fouquet told TechCrunch in May, what China can currently buy are older-generation deep ultraviolet tools — gear first shipped about a decade ago — the same machines the MATCH Act would now put off limits.

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

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

In its first earnings report since going public, the AI chipmaker forecast a narrower gross margin in its core business, scaring investors.

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.

Top AI researchers Jonas Adler and Alexander Pritzel are leaving Google for Anthropic, following departures from top scientists Noam Shazeer and John Jumper.

Revenue quadrupled to $41.45 billion compared with the same period a year ago. The company's profit, meanwhile, rose from $1.88 billion to an incredible $28.2 billion year-over-year.

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

If you're building AI agents that work with browser screenshots, you already know the pain. You take a full 1920×1080 screenshot, pass it to GPT-4o or Claude, and watch your token bill climb — while the model downscales the image anyway and blurs the exact text you needed it to read. There's a better way. The problem Vision LLMs are expensive for two reasons when you feed them full screenshots: Token cost — a full screenshot can cost 10–20x more tokens than a small crop Accuracy loss — models internally downscale large images, blurring fine text, labels, and UI elements But your agent already knows where to look. Browser automation tools like Playwright and Puppeteer give you getBoundingClientRect() — the exact pixel coordinates of any element on screen. So why are you sending the whole screenshot? The solution I built a stateless pay-per-use API that takes a screenshot and pixel coordinates, and returns just the cropped element as a lossless PNG — ready to pass directly to your vision LLM. POST /crop { "image" : "<base64 screenshot>" , "x" : 120 , "y" : 45 , "width" : 640 , "height" : 80 } Returns: { "success" : true , "data" : { "base64" : "iVBORw0KGgo..." , "mime" : "image/png" , "width" : 640 , "height" : 80 , "bytes" : 4821 } } A 4KB crop instead of a 2MB screenshot. Same information. 95% fewer tokens. How payment works Here's where it gets interesting. The API uses the x402 payment protocol — HTTP's long-dormant 402 Payment Required status code, finally put to use. There are no API keys. No accounts. No subscriptions. The agent pays $0.0005 USDC per crop on Base L2 automatically. The flow: 1. Agent POSTs to /crop (no payment header) ← 402 with payment instructions in headers 2. Agent transfers 0.0005 USDC to recipient wallet on Base (near-zero gas, ~2 second settlement) 3. Agent POSTs again with x-payment-tx-hash header ← 200 with cropped PNG The entire exchange happens inside the HTTP request cycle. No human intervention. No billing dashboard. The money lands

Your product might rank on page one of Google and still be invisible to AI. When someone asks ChatGPT "what's the best project management tool for small teams," does your product show up? For most SaaS companies under 50 employees, the answer is no. At Inithouse, we built Be Recommended to answer that question with a number: a single AI visibility score from 0 to 100 that tells you exactly where you stand across four major AI engines. Here is how the scoring works under the hood. What the score measures The Be Recommended score captures how often, how prominently, and how positively AI engines mention your product when users ask category-relevant questions. A score of 0 means no AI engine mentions you at all. A score of 100 means every tested prompt across all four engines names your product as a top recommendation. The four engines we test against: ChatGPT (OpenAI), Perplexity , Claude (Anthropic), and Gemini (Google). Step 1: Prompt generation We start by building a bank of 50+ real prompts that a potential customer would actually type into an AI assistant. These are not keyword-stuffed test queries. They mirror how real people ask for recommendations. For a CRM product, that looks like: "What CRM should a 10-person startup use?" "Best alternatives to Salesforce for small businesses" "Compare CRM tools with good API integration" "Which CRM has the best free tier in 2026?" We group prompts into three categories: direct (user names the product category), comparative (user asks for alternatives or comparisons), and situational (user describes a problem without naming a category). Each category tests a different signal: brand recognition, competitive positioning, and contextual relevance. Step 2: Multi-engine querying Each prompt gets sent to all four AI engines through their APIs. We capture the full response text, not just a yes/no for whether your product appeared. The raw responses go into a structured analysis pipeline. We run queries from neutral accounts with n

TL;DR - A finished app that only runs on one laptop is a private demo. Getting it online means connecting three things: a place to store the code (version control), a place to run it (a host), and an address people can type (a domain). The same AI tool that helped build the app can walk a beginner through all three, often without ever opening a terminal. An important step you don’t want to skip is the security check before going live, because the fastest way to ruin a launch is to ship with the database wide open. So you’ve done it. You built your first tool. And it works. The button does the thing. Now’s the moment. It’s time to get your tool online, but how? A project running on a laptop is real, but it lives in exactly one place, the machine it was built on. Nobody else can open it. Getting that project online is its own small skill, separate from building, and it trips up more beginners than the building did. A new coder can finish a working photo booth app in an afternoon and still have no idea how to hand it to a friend short of pulling up the GitHub link while sitting together over coffee. The good news is that the part that used to eat a whole weekend now takes a conversation. Three Things Every App Needs to Go Live Almost every deployment, whatever the tool, comes down to three things working together. Version control: This is a place to store the code and track every change made to it. For most people that means GitHub, which we’ve talked about before. The same way Google Docs keeps a version history, GitHub keeps one for a project. This piece does not re-explain it; the GitHub walkthrough covers the whole thing. A host: A host is really just a computer that stays powered on and connected to the internet with a public address of its own. When a visitor types in the app's address, their browser sends a request across the internet to that machine, the machine runs the code, and it sends the finished page back. A laptop was quietly doing both jobs during the

It's a spicy start for the AI company's collaboration with the chip maker.

The tokenmaxxing era was brief. We now appear to be entering the era of token rationing.

Privacy advocates and domestic violence groups say ID mandate is a big mistake.

Rep. Anna Paulina Luna (R-FL) says her staff used AI for "spellcheck" in an amendment summary for a major defense bill, but denies it was used for the bill text itself and says "NO Legislation is ever drafted with AI." Luna issued the response after accounts on X began sharing screenshots of an amendment summary […]

The AI arms race between China and the US has researchers on both sides worried about a “Chernobyl moment.”

Rockstar Games has finally given Grand Theft Auto VI a price ahead of the game's November 19th launch. But while announcing that the game would cost $79.99, Rockstar also confirmed that the physical versions of GTA VI won't include a disc, and instead will only contain a download code inside the box. Not only is […]