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The company may finally be ready to try to deliver on Elon Musk's years-long promise of launching a robotaxi network of its own.

Let's be honest—traditional A/B testing is broken. If you've ever implemented client-side testing, you know the drill. Your users load the page, wait for JavaScript to execute, and then—flicker—the content changes. It's jarring. It hurts your Core Web Vitals. And worst of all, your experiments might be measuring user frustration instead of genuine engagement. But what if you could run A/B tests with zero flicker? What if you could modify your HTML before it even reaches the browser? That's exactly what Cloudflare Workers make possible . The Server-Side Advantage A/B testing at the edge means making decisions about which variant a user sees before any HTML is sent to their browser . Instead of: Load the page Execute JavaScript Flicker Apply the variant Track the result You get: Decision made at the edge Correct HTML streamed immediately Zero flicker Better performance Companies like Ninetailed are already using Cloudflare Workers to achieve 2-3x cost savings compared to traditional serverless platforms, all while delivering personalized experiences with minimal latency . How It Actually Works The concept is surprisingly elegant. Your Cloudflare Worker intercepts incoming requests, checks for a cookie to maintain user consistency, and routes users to the appropriate variant . Here's a simplified version that's production-ready: javascript const NAME = "myExampleWorkersABTest"; export default { async fetch(req) { const url = new URL(req.url); // Determine which group this user is in const cookie = req.headers.get("cookie"); if (cookie && cookie.includes(`${NAME}=control`)) { url.pathname = "/control" + url.pathname; } else if (cookie && cookie.includes(`${NAME}=test`)) { url.pathname = "/test" + url.pathname; } else { // New user—randomly assign them (50/50 split) const group = Math.random() < 0.5 ? "test" : "control"; url.pathname = `/${group}` + url.pathname; // Fetch and modify the response to set a cookie let res = await fetch(url); res = new Response(res.body, res

Sriram Madapusi Vasudevan discusses industry-converging patterns for securing autonomous AI agents in production. He explains the critical vulnerabilities hidden inside the ReAct loop across context, reasoning, and tool execution. He shares how to mitigate risks like memory poisoning and rogue tool execution using defense-in-depth strategies, LLM-as-a-judge critics, and MAESTRO threat modeling. By Sriram Madapusi Vasudevan

Gov. Ron DeSantis calls it a crackdown on "radical climate policies."

Imagine an e-bike motor that lets you select your preferred pedaling cadence and then automatically adjusts the gears to keep your legs spinning at that exact speed, no matter how steep the hill gets - all without a fragile derailleur or heavy multi-speed cassette to maintain. Prefer manual control? No problem, you can have as […]

For decades, the senator has argued that concentrated wealth threatened American democracy. Now he’s betting that frustration with Big Tech, billionaires, and unchecked AI is reaching a tipping point.

This article covers the MCP setup and configuration for using Google Nano Banana 2 Lite and...

Hello Dev Community! 👋 It is officially Day 89 of my 100-day full-stack engineering run! 🎯 Yesterday, I kicked off my competitive solving streak on HackerRank. Today, I advanced from standard linear filters into the powerful world of textual pattern recognition by mastering: SQL Regular Expressions (REGEXP) and String Anchors! 🔍🛡️ When processing real-world data pipelines—like validating structured phone inputs, email domains, or parsing specific text queries—standard LIKE operators can make your code messy and repetitive. Today, I solved these constraints elegantly. 🧠 Shifting from Bulky LIKE Statements to Sleek REGEXP As tracked inside my workspace files across "Screenshot (193).png" and "Screenshot (195).png" , I solved two distinct core challenges from the HackerRank series: 1. Match from the Start: Weather Observation Station 6 The Goal: Query the list of CITY names from STATION that start with vowels ( a , e , i , o , u ), ensuring no duplicates are returned. The Evolution: Instead of chaining multiple LIKE queries or cutting sub-strings with LEFT() , I utilized the caret anchor ( ^ ) inside a regular expression array to verify the string's starting boundary instantly: sql SELECT DISTINCT CITY FROM STATION WHERE CITY REGEXP "^(A|E|I|O|U)";

I needed faster edits for technical diagrams, and a lower recurring overhead for recurring visuals. I stopped asking for new images for everything. That change started the moment I replaced "generate now, tweak later" with a fixed 8-tool matrix. TL;DR: I moved recurring illustration work into seven scriptable stacks + one 3D stack and kept image-generation AI only as a fallback. Why I rewrote this workflow When I edited an article recently, I was spending too much time redoing the same visual shape in slightly different versions. The same chart logic should not need prompt guessing each time. I asked myself: Can this be represented as text or code? Can I regenerate it exactly when requirements change? Do I need raw design freedom, or do I need deterministic structure? If the answer was mostly "text/code + deterministic output," I did not open an image-generation model first. I also kept one practical boundary: this was not an academic tool roundup. This is a log of what I actually used and in what context. The number that changed my mind: an 8-tool decision matrix The number I now defend is exactly 8 . Instead of inventing synthetic savings, I evaluate every new illustration request against this matrix. Tool Best fit Why I pick it Mermaid flow, sequence, architecture notes fastest in markdown-native writing PlantUML UML-heavy docs strict structure when Mermaid gets too loose Markmap map-style summaries converts headings directly Graphviz dependency and direction graphs compact graph semantics matplotlib numeric visualizations source-of-truth from data tables Pillow labels, badges, annotations deterministic pixel edits in Python D3.js node/link or hierarchy interactions data-driven relationship rendering Blender 3D explanatory graphics stronger structural clarity for complex scenes This is the exact set I now reach for before any image-generation request. What happened first: practical snippets I am including small runnable snippets I can reuse. 1. Mermaid for determ

Heat pumps are more than just a way to keep your home comfortable without using fossil fuels.

Modern web application architectures typically run each layer in its own process, like a NodeJS server and database both running in their own processes. They communicate via a network connection or localhost socket. This separation introduces protocol overheads, TCP stack latency, and data serialization/deserialization costs on every single query. Planck is designed around the concept of Zero-Distance Architecture that co-locates data and application code. It combines the database engine and a WebAssembly application runtime into a single, unified process. By running your application code directly inside the database process, database calls become direct in-memory function calls rather than network round-trips. This article provides a practical guide to getting started with Planck. We will look at the core toolchain, walk through setting up a self-contained local benchmark, compare its performance against a NodeJS, ExpressJS, MongoDB stack, and look at how to build more complex features. The Toolchain: Planck, planctl, and Workbench Running and managing a zero-distance app requires three main components. Planck itself is the core binary. It functions as both the storage engine (a WiscKey-style, LSM-tree-based engine) and the WebAssembly host. Instead of running a database in one process and your application server in another, you run a single Planck process. It loads your compiled WebAssembly application directly into its memory, running it in the same process space as the database. To manage this runtime, you use planctl. This is the command-line tool for developers. It handles the compilation of your code, packages it, and deploys it to the Planck host. It also allows you to perform database operations, like creating stores and defining indexes, export/import, backup/restore directly from your terminal. Finally, there is the Workbench. This is a web console that comes built into the platform. It provides a visual dashboard to monitor your applications, view databa

I needed to convert 500 product images from one format to another. Server-based solutions quoted $15-50/month for batch processing. So I built a client-side solution using Web Workers and OffscreenCanvas. The Architecture The key insight: Canvas operations on large images block the main thread. The fix: Web Workers handle image decoding/encoding off the main thread OffscreenCanvas renders without DOM access — perfect for worker contexts Transferable objects pass image data between workers with zero-copy const worker = new Worker ( ' processor.js ' ); const canvas = new OffscreenCanvas ( 800 , 600 ); // Worker processes image, main thread stays responsive Real Performance Processing 500 images (average 2MB each) on a mid-range laptop: Server upload approach: 12 minutes (mostly upload time) Browser-local with Workers: 3 minutes 40 seconds Memory usage: Stable at ~400MB with proper cleanup The Tools I packaged this into webp2png.io for batch WebP conversion and svg2png.org for vector batch processing. For barcode generation, genbarcode.org uses similar worker-based rendering for bulk label generation. If you're processing more than 50 images, Workers + OffscreenCanvas is the way to go. Your server bill will thank you.

I analyzed what happens when you upload a photo to 5 popular free image compression sites. The Test I uploaded a 4.2MB photo to each service and monitored network requests. Results: Service A : File sent to their CDN (AWS us-east-1). 12 analytics trackers fired simultaneously. Service B : File uploaded, but 5 minutes later a second request sent the file to a different domain. Service C : Cleanest of the five, but their privacy policy reserves the right to "use uploaded content to improve compression algorithms." Service D : 23 third-party scripts loaded on the page. Your image URL is accessible to all of them. Service E : Actually clean — only one request to their server for processing. Only one of five didn't leak data to third parties. One. The Alternative I built compress2png.com to test whether image compression could work without any server. Turns out Canvas API + clever JavaScript handles it: Resize images client-side before export Strip EXIF/metadata in the browser Convert to optimal formats based on content For format-specific needs, svg2png.org handles vector conversion and webp2png.io handles next-gen format conversion — all browser-local. Check the Network tab next time you use a "free" online tool. You might be surprised what you find.

The interesting part of OpenAI and Codex on AWS is not that another cloud menu got more model names. That part is useful. Enterprises want strong models. Developers want Codex closer to their infrastructure, data, and deployment machinery. The interesting part is that frontier AI is being pulled into the same boring machinery that already governs everything else companies run: procurement, IAM, billing commitments, region policy, audit logs, support contracts, data boundaries, and security review. That sounds like paperwork. It is also how enterprise software becomes real. model access was the easy problem For a while, AI adoption was framed as an access problem. Can we call the model? Can we get enough rate limit? Can we wire the SDK into our product? Can the coding assistant see enough of the repo to be useful? Those are real questions. They are not the end of the story. The next set is much more familiar to anyone who has operated software inside a company: which account owns this usage, which data can cross the boundary, who can create agents, which region runs inference, how the bill is allocated, and what evidence exists when an incident involves model output. That is the part where the demo becomes a platform. OpenAI on AWS matters because many companies already have that platform muscle in AWS. They have IAM, billing, private networking, audit trails, procurement paths, compliance evidence, cost allocation tags, and teams whose job is to make all of this survivable. Putting a frontier model behind that machinery does not make the hard parts disappear. It makes them legible. bedrock is a procurement surface Amazon Bedrock is usually described as a managed model service, which is true and also undersells the point. For enterprises, Bedrock is a procurement and control surface. If OpenAI models and Codex are available through Bedrock, a company can route adoption through an existing cloud relationship instead of creating a new vendor path for every team that wa

This week's Java roundup for June 22nd, 2026, features news highlighting: the GA releases of Hardwood 1.0 and Endive 1.0; the June 2026 edition of Azul Payara; point releases of Quarkus, LangChain4j; the first beta release of WildFly 41; and introducing Eliya JDK and the Open Source Sustainability Initiative (OSSI), the latter of which was founded by HeroDevs and Commonhaus Foundation. By Michael Redlich

South Korea targets physical AI lead and commercial humanoid robots by 2028.

While Kara Zor-El's appearance at the end of James Gunn's Superman was a very pleasant surprise, Warner Bros. Discovery's plan to fast-track a standalone Supergirl feature always felt a little dubious. It seemed odd that, after Superman, the studio wanted to flesh out its new cinematic universe with films about another Kryptonian and one of […]

Rather than external partnerships, Uber may start leaning on its own robotaxis for driverless options.

Google is expanding Gemini’s personalized AI image generation to eligible free users in the U.S., allowing the chatbot to create images based on your interests and data from connected Google apps.

SCOTUS falls short of deeming geofence warrants unconstitutional, though.