标签：#AI

The 2026 edition of Summer Game Fest just wrapped up, and it was surprisingly hectic. The nearly week-long event came at a challenging time for the games industry, and for the most part the big keynotes were used as a chance to show some strength through major announcements, while largely ignoring pesky details like hardware […]

A2A has become like a standard for enabling agent to agent communication, we could use the a2a-sdk for running and configuring the a2a server and its features such as agent card, agent skills, agent executor, request handler etc. However we are going to go with a simplified approach here with strands where the agent card will be fetched automatically. Let's get started! Server Initialize a uv project for the a2a server and switch to that directory. uv init ~/strands-a2a-server cd ~/strands-a2a-server Add the required packages. uv add python-dotenv == 1.2.2 strands-agents[a2a] == 1.42.0 Change the code in main.py to look like below. $ cat main . py from dotenv import load_dotenv from strands import Agent from strands.multiagent.a2a import A2AServer load_dotenv () def main (): agent = Agent ( callback_handler = None , description = " A sample strands agent " , model = " us.amazon.nova-micro-v1:0 " , ) a2a_server = A2AServer ( agent = agent ) a2a_server . serve () if __name__ == " __main__ " : main () I like the simplicity here, as you see above, it's quite simple to start a basic a2a server from with in strands, with just a couple of lines of code, we didn't have to install the a2a-sdk separately. Run the code, to start the a2a server. $ uv run main.py INFO: Started server process [18006] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://127.0.0.1:9000 (Press CTRL+C to quit) Client Let's now do the client part on a separate terminal. Initialize the project and switch the directory. uv init ~/strands-a2a-client cd ~/strands-a2a-client Modify main.py code to look as follows. import asyncio from strands.agent.a2a_agent import A2AAgent async def main (): agent = A2AAgent ( endpoint = " http://localhost:9000 " ) agent_card = await agent . get_agent_card () print ( " Invoking remote agent with agent card: " ) for key , value in agent_card : print ( key , " : " , value ) print ( ' - ' * 20 ) while True : prompt = input

Whenever I heard about harmonics, I thought they were only related to large substations, transmission systems, and industrial facilities. I assumed harmonics were something utility engineers dealt with and not something connected to everyday devices. Phone chargers can create harmonics. Laptop chargers can create harmonics. LED lights can create harmonics. Even a UPS sitting under a desk can create harmonics. Today, modern power systems use many power electronic devices such as EV chargers, solar inverters, battery energy storage systems (BESS), UPS systems, data centers, and Variable Frequency Drives (VFDs). While these technologies bring many benefits, they can also introduce harmonic distortion. The more power electronic devices we connect to the grid, the more important harmonic analysis becomes. In this article, I will explain what harmonics are, what causes them, how they affect power quality, how they can be analyzed using PSCAD, and why they are becoming more important in modern power systems. Before we talk about harmonics, let's first understand electrical loads, because this is where harmonics usually begin. What Is an Electrical Load? An electrical load is any device that uses electrical energy to perform useful work. For example, think about a typical evening at home. You turn on a ceiling fan, LED light, laptop, air conditioner, and phone charger. All of these devices use electricity, so they are called electrical loads. Examples of electrical loads include motors, heaters, fans, computers, air conditioners, lighting systems, and EV chargers. However, not all electrical loads use electricity in the same way. Some draw current smoothly, while others draw current in short pulses. This small difference is actually where the story of harmonics begins. Linear vs Non-Linear Loads To understand harmonics, we first need to understand the difference between linear and non-linear loads. Although both types of loads consume electricity, they draw current from the

A step-by-step, beginner-friendly guide to stripping out Bootstrap and setting up the blazing-fast Tailwind CSS v4 compiler in your .NET Blazor Hybrid or Web app. Tailwind CSS v4 is officially here, and it is a complete game-changer! It scraps the old, bulky JavaScript configuration files ( tailwind.config.js ) and moves your theme settings directly into standard CSS. Plus, its new native compiler is faster than ever. If you are building a .NET Blazor app (whether it's Blazor WebAssembly, Server, or a MAUI Blazor Hybrid app) and want to swap out the default Bootstrap styles for utility-first Tailwind bliss, you can do it all without ever leaving your IDE. Let's get this configured step-by-step using Visual Studio 2022 ! Prerequisites Before we start, make sure you have: Node.js installed on your development machine. A Blazor project opened in Visual Studio 2022 . Step 1: Say Goodbye to Bootstrap in Solution Explorer Blazor templates ship with Bootstrap by default. Let's use Visual Studio's Solution Explorer to clean that out so our styles don't conflict. In the Solution Explorer window, expand your wwwroot folder. Expand the css subfolder. Right-click the bootstrap folder and select Delete . Now, open your main HTML entry file inside wwwroot (this will be index.html for Blazor Hybrid/WASM or App.razor inside the Components folder for Blazor Web). Locate the <head> section and delete the line linking the Bootstrap stylesheet: <link rel= "stylesheet" href= "css/bootstrap/bootstrap.min.css" /> powershell Step 2: Open the Developer PowerShell & Install Tailwind v4 Tailwind v4 splits the design library from the command-line build tool, so we need to install both. We can use Visual Studio's built-in terminal for this. In the top menu of Visual Studio 2022, go to Tools > Command Line > Developer PowerShell . A terminal window will open at the bottom of your IDE, already navigated to your project folder. Paste and run the following commands: i. Initialize a package.json fil

A step-by-step, beginner-friendly guide to stripping out Bootstrap and setting up the blazing-fast Tailwind CSS v4 compiler in your .NET Blazor Hybrid or Web app. Tailwind CSS v4 is officially here, and it is a complete game-changer! It scraps the old, bulky JavaScript configuration files ( tailwind.config.js ) and moves your theme settings directly into standard CSS. Plus, its new native compiler is faster than ever. If you are building a .NET Blazor app (whether it's Blazor WebAssembly, Server, or a MAUI Blazor Hybrid app) and want to swap out the default Bootstrap styles for utility-first Tailwind bliss, you can do it all without ever leaving your IDE. Let's get this configured step-by-step using Visual Studio 2022 ! Prerequisites Before we start, make sure you have: Node.js installed on your development machine. A Blazor project opened in Visual Studio 2022 . Step 1: Say Goodbye to Bootstrap in Solution Explorer Blazor templates ship with Bootstrap by default. Let's use Visual Studio's Solution Explorer to clean that out so our styles don't conflict. In the Solution Explorer window, expand your wwwroot folder. Expand the css subfolder. Right-click the bootstrap folder and select Delete . Now, open your main HTML entry file inside wwwroot (this will be index.html for Blazor Hybrid/WASM or App.razor inside the Components folder for Blazor Web). Locate the <head> section and delete the line linking the Bootstrap stylesheet: <link rel= "stylesheet" href= "css/bootstrap/bootstrap.min.css" /> powershell Step 2: Open the Developer PowerShell & Install Tailwind v4 Tailwind v4 splits the design library from the command-line build tool, so we need to install both. We can use Visual Studio's built-in terminal for this. In the top menu of Visual Studio 2022, go to Tools > Command Line > Developer PowerShell . A terminal window will open at the bottom of your IDE, already navigated to your project folder. Paste and run the following commands: i. Initialize a package.json fil

There is a class of projects that teaches you more about a language than any tutorial ever could. Building a PDF engine from scratch in Go is one of them. It is not glamorous. It is not trendy. But it forces you to confront memory management, binary serialization, concurrency safety, interface design, and performance profiling all at once, in a domain where correctness is non-negotiable. This article walks through the lessons learned building GoPdfSuit (~500 Github ⭐), a production PDF engine written in Go that generates 1.5 million financial PDFs in roughly 45 minutes on a single node, achieves PDF/A-4 and PDF/UA-2 compliance, and exposes itself as a REST API, a Go library, and Python CGO bindings simultaneously. Note : While I have six years of overall experience including two years working specifically with Go, I rarely encountered these types of challenges in my day-to-day work, as my role focused primarily on implementing new features within an existing architecture. Working on gopdfsuit was an excellent learning experience; it allowed me to dive deep into performance optimization and taught me a great deal. Below are some of the key takeaways. Building GoPdfSuit from a blank editor to a production-grade PDF engine-one that ships PDF 2.0 , PDF/A-4 , PDF/UA-2 , PKCS#7 signing, merge/split, XFDF fill, secure redaction, and a public gopdflib API-forced a shift from “business logic” to “systems engineering.” When you chase ~2,000+ aggregate ops/s on a mixed financial workload (48 workers, PDF/A on) and sub ~10 ms PDF generation, you stop debating frameworks and start fighting the allocator, cache lines, and ISO 32000 semantics. These fifty lessons are drawn from the actual codebase ( internal/pdf , pkg/gopdflib , benchmark harnesses under sampledata/ , and documented optimization passes in guides/cursor/ ). They mix specification pain with Go runtime craft and production reality-not generic blog advice. Part 1: Structural Hurdles & PDF Specification Nightmares Deco

How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day job, but I know I still have so much to learn because the field is broad, and to truly become expert requires dangerously ambitious levels of work ethic. I think I'm a functional data scientist who learns more as I encounter new problems daily. I'm writing this piece because in the last week or two, precisely three people have asked me questions related to transitioning into data science. As such, I thought to unify my thoughts around the topic so that I can refer anyone else who asks here--if anyone else ever asks. This article assumes you're already familiar with some of the data science entails such as data analysis, model training, prediction, etc, so I will not be doing a lecture series, just addressing some of the disconnects I have observed in conversation with people looking to transition to the field. Initial Excitement In 2026, it's easy to see what claude or chatGPT is doing and go "What sorcery is this? I must learn this trick!" and then reach out to the closest person you know who has ever mentioned anything about data or machine learning to find out how you can transition into AI. First of all, transitioning into "AI" is such a broad way to look at it. It is analogous to saying "I want to emigrate to Africa, show me how". But that's forgivable too. To cut short your initial excitement, or maybe redirect it, playing with a locally hosted LLM or making API calls to the DeepSeek endpoint is not data science, or machine learning or "AI". It's coding. And if you want to go down that route, you're better of focusing on software engineering. I say this because when you work with LLMs, the finished models to be specific, it's like using any other SaaS API out there. The difference being that you're interacting with a much less deterministic interface. But the rest of the work you do around it is pretty much a det

Most merchants know they have unused files. Far fewer realize they're accumulating AI-generated media they never intended to keep. There's a problem quietly growing inside thousands of Shopify stores right now. It's not abandoned carts. It's not slow page speeds. It's not even the 400 unused product images you already know you should deal with. It's something newer, and most merchants have no idea it's happening. The Rise of AI-Generated Commerce Content Over the past two years, AI image tools have gone from novelty to routine. Shopify Magic. Canva AI. Midjourney. ChatGPT image generation. Adobe Firefly. Background removers. Lifestyle photo generators. Product shot enhancers. Merchants are using these tools constantly — to mock up new products, test background options, generate seasonal variants, create ad creatives, experiment with lifestyle photography. The workflow feels clean: generate a few options, pick the best one, move on. Here's what's actually happening on the backend. Every time you use Shopify's native AI tools to generate, edit, or enhance an image, Shopify quietly deposits files into your media library. Not just the one you kept. All of them. The rejected generations. The experimental edits. The "let me try one more variant" files. The abandoned attempts from six months ago when you were testing a new product that never launched. Every. Single. One. Most merchants assume the files they don't choose disappear. They don't. The lifecycle looks something like this: ┌─────────────────────┐ │ AI Image Generation │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Rejected Variants │ │ • Drafts │ │ • Test Images │ │ • AI Edits │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Hidden Media Files │ │ Accumulate Over Time│ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ AI Clutter │ │ Invisible Technical │ │ Debt │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Reduced Media │ │ Governance │ │ • More Noise │ │ • Less Visibility │ │ • Hard

This year's Summer Game Fest turned out to be a good one for fans of Japanese RPGs. First, the conclusion of the Final Fantasy VII remake trilogy was announced at SGF Live, and now we have the long-awaited return of the Persona series. Atlus confirmed Persona 6 exists with a brief teaser, but aside from […]

Notion's head of product said he was "astonished" at “the amount of people RT-ing this."

Minecraft Dungeons 2, a sequel to Microsoft's dungeon crawler spinoff Minecraft Dungeons, will be released on September 29th. The company originally revealed the game in a brief trailer in March, promising a fall 2026 release window. Here's how Microsoft described it at the time: Return to the world of Minecraft Dungeons in an all new […]

Just a few days after pushing Fable out of 2026, Microsoft showed off more footage of Fable, the first new entry in the storied RPG franchise since 2010's Fable III, at its Xbox Games Showcase on Sunday. The company also announced a specific release date: February 23rd, 2027. Though if you get the Premium Edition, […]

As part of its Xbox Games Showcase on Sunday, Microsoft revealed new details about Halo: Campaign Evolved, the upcoming remake of Halo: Combat Evolved's campaign mode. The remake will debut on Xbox Series S / X, PC, and PS5 on July 28th. Today's mission trailer includes a first look at Operation: Meteorite, a new three-mission […]

Apparently, the "return of Xbox" means a retreat from other platforms. At its Xbox Games Showcase today, Microsoft revealed that Gears of War: E-Day - which was previously rumored for a PS5 launch in addition to Xbox and PC - will not be coming to PlayStation. It'll be an Xbox console exclusive and is launching […]

Have you ever wondered what the most indispensable app is for your favorite musician or how the world’s tech CEOs stay focused? Well, that’s the sort of thing we aim to uncover in our Verge Weekend Questionnaire. Think of it as a spiritual successor to Five Minutes on the Verge. Every Saturday, a different guest […]

The console industry is in a weird place, and both Xbox and PlayStation have a chance to change the narrative a bit with their showcases at Summer Game Fest. Sony did that by focusing on the single-player titles it’s known for, and then it was Microsoft’s turn. The Xbox Games Showcase was focused mainly on […]

"Chat is dead" — at least, according to a senior OpenAI employee.

The revamped AI tool will roll out in the coming weeks, according to The Financial Times.

I built something I desperately needed: daily AI audio lessons from real developer trends (plus a Dutch coach for inburgering B1). The hardest part wasn't the AI. It was figuring out how to score genuine rising skills vs. one-day noise. I ended up building a cross-day momentum signal that rewards skills accelerating over 3+ days and dampers spikes. But I'm stuck on the next problem: how do you personalize this without storing user data? (I'm privacy-first, so no subscriber DB — Telegram holds the member list.) If you've solved this, I'd love your take. And if you're learning Dutch + coding, I'd appreciate you trying it and telling me what's useless. What I'm curious about: Is the momentum signal actually working — am I surfacing real trends or just noise? Would the Dutch coach be useful for expat developers in NL, or is it too niche? Technical details (for those who care): • 7 sources: GitHub Trending, HN (Who-is-Hiring + front page), Stack Overflow tag deltas, dev.to, Reddit, Lobste.rs • Map-reduce skill extraction with deterministic attribution (corpus scan, not LLM tally) • Grounded briefs: reads actual source text via Jina + Exa, cited sources • Delivered via Telegram (audio + PDF), Spotify podcast, email • Privacy: PII redacted at ingestion, no subscriber data stored Live: https://yusuprozimemet.github.io/LearnX-Radar/ GitHub: https://github.com/Yusuprozimemet/LearnX-Radar (P.S. This is still beta — I'm looking for feedback, not users. If you try it, tell me what's useless, not what's good.)

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built SpendWise AI is a free tool that audits your AI tool spending (Cursor, Copilot, Claude, ChatGPT, Gemini, Windsurf) against verified vendor pricing and tells you exactly where you're overspending and what to do about it. I originally built this as a week-long assignment for a startup. The problem it solves is simple: founders and engineering managers pay for multiple AI tools but have no idea if they're getting ripped off. SpendWise gives them that answer in under a minute, no signup needed. The interesting part is that the core audit engine has zero AI in it. It runs 6 hardcoded rules against verified pricing data, so every recommendation is reproducible and verifiable. AI (Groq's Llama 3) only kicks in to write a friendly summary paragraph on top of the structured results. I made this choice because financial recommendations need to be deterministic. Same input, same output, every time. The stack is Next.js 16, TypeScript, Tailwind + shadcn/ui, Supabase for the database, Groq for AI summaries, Resend for emails, and Vitest for testing. Deployed on Vercel. Live app: spendwise-ai-test.vercel.app Source code: github.com/Karam-999/SpendWise-AI Demo The original audit tool: The comeback (re-audit on pricing change): You can try the Round 1 version live at spendwise-ai-test.vercel.app . Pick a tool like Cursor on Teams plan at $40/mo, run the audit, and see the full savings breakdown. The Round 2 features (pricing change detection, re-audit diff view) are on a separate branch and not merged to main yet, but the demo video above walks through the complete flow. The Comeback Story Where it was: The original version was basically a calculator. You fill in your AI tools, it shows you where you can save money, and that's it. If Cursor changed its pricing the next week, your audit was already stale and you'd never know about it. It worked fine as a one-time thing. It had the form, the audit engine, AI