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A former software manager claims Wisk rushed software testing ahead of a crucial 2025 flight test.

It only works for a few divisions thanks to a lot of added materials.

You've probably written a CommandExecutor before. Everyone who's touched Bukkit has. Declare the command in plugin.yml , implement onCommand , cast args[0] to whatever you need, hope nobody fat-fingers the input. It compiles. It runs. It's confusing to debug. And it's the wrong way to do it in 2026. # plugin.yml commands : punish : description : Opens the punishment GUI usage : /punish <player> public class PunishCommand implements CommandExecutor { @Override public boolean onCommand ( CommandSender sender , Command command , String label , String [] args ) { if (!( sender instanceof Player staff )) return true ; if ( args . length < 1 ) return true ; Player target = Bukkit . getPlayer ( args [ 0 ]); if ( target == null ) { sender . sendMessage ( "Player not found." ); return true ; } // ... open the GUI return true ; } } Tie it together in onEnable() with getCommand("punish").setExecutor(new PunishCommand()) , add a separate TabCompleter implementation to handle suggestions, and you're done. Seems perfectly fine... totally not confusing at all... (if you understood any of that, you're doing better than I am :P) This implementation has many issues... like Bukkit.getPlayer(args[0]) only matching an exact, currently-online name. No selectors. No partial matching. You write all of that yourself or not at all. Tab completion lives in a second method you keep in sync with parsing by hand. Change one, forget the other, and tab completion starts "lying" to your players (a problem that has taken me HOURS to solve in the past... i'm getting flashbacks ;-;). And the tree itself is static, fixed in plugin.yml . Want /report to take a severity argument only when severities are configured? You can't say that in plugin.yml and you end up with a tangled mess that is almost never clean (either to you, or the players). Paper ships Mojang's Brigadier (the same framework vanilla Minecraft uses for everything) through a lifecycle hook: LifecycleEvents.COMMANDS . You register a tree of

The latest Puppet Enterprise releases are out and this one has a huge load of improvements, fixes, and security patches included! Puppet Enterprise (PE) 2025.11 released! The full PE 2025.11 release notes are always the best way to get a full detail on what has changed, but here are some highlights of PE 2025.11! Certificate Authority (CA): Database-backed Storage This new optional feature adds support for storing CA data in a PostgreSQL database instead of the file system. This improves performance and reliability and introduces API-driven capabilities and enhanced backup and recovery handling. PostgreSQL 17 Supported PE-managed installations will automatically upgrade from verson 14 to 17 as part of the upgrade process, or you can update yourself before upgrading to PE 2025.11 Infra Assistant Goes GPT-5 GPT-5 series models are now running under the hood of Infra Assistant, improving the quality of responses and the consistency for queries. Advanced Patching Enhancements The advanced patching feature now has improvements across a variety of areas New puppet_run_concurrency setting allows you to get better performance out of patch group enrollment Improved validation of scheduled and immediate jobs to reduce risk of unintended or skipped executions. Cron scheduling has better user experience and improved validation across features. New configurable option to enable Puppet to run after patch jobs to refresh pe_patch facts New Endpoints for Classifier and Activity Service APIs The Classifier API introduced new tags , add-tags and remove-tags endpoints to manage node group tags. The Activity service API now has subscriptions endpoints to create subscriptions, list subscriptions, or fetch/delete a specific subscription. Agent Platform Updates, Resolved Issues, and Security Fixes The macOS 26 platform is now supported for both ARM and x86_64, while support has been removed for Ubuntu 18.04 and Ubuntu 20.04. Nearly 60 CVEs were addressed in this release, along with many r

The company announced a new slate of executive hires meant to help turn things around, as Gravity SUV sales are not taking off as expected.

We are so excited to finally announce the winners of the GitHub Finish-Up-A-Thon Challenge, our...

The company delivered more than 480,000 EVs globally, seemingly thanks to geographic expansion and cheaper versions of the Model 3, Model Y, and Cybertruck.

Why Our AI Agent Still Stumbles on Full-Stack Apps We've all been there. You're riding high on the AI hype, picturing your agent effortlessly spinning up features, leaving you free for higher-level architectural decisions. You feed it a prompt like, "Build me a simple user profile page with authentication, connected to a database, using Next.js and TypeScript." You hit enter, grab a coffee, and expect magic. More often than not, what you get back is… well, it's something . It might be syntactically correct, perhaps even impressive in parts. But when you try to integrate it, to make the pieces talk to each other harmoniously, it often feels like trying to connect a square peg to a round hole. The agent struggles, and frankly, so do we trying to fix its output. The Seams, Not Just the Parts: Why Full-Stack is More Than Sum of Its Halves In my experience, AI agents, especially Large Language Models, are fantastic at generating code for isolated problems. Need a React component? A SQL query? A utility function? They'll often nail it. But a full-stack application isn't just a collection of frontend, backend, and database parts. It's the intricate, often implicit, contracts between them. Think about a modern Next.js application. It’s a beautifully complex dance: Server Components vs. Client Components: This paradigm shift fundamentally changes where state lives, where data is fetched, and how interactivity is handled. An AI might generate a useState hook inside a Server Component, completely missing the architectural intent. Data Fetching Strategies: getServerSideProps , getStaticProps , route handlers , fetch directly in Server Components – each has specific implications for caching, performance, and where your data lives at runtime. An AI might pick an inefficient or incorrect strategy based on a simplified prompt. Type Safety Across Boundaries: TypeScript is a lifesaver, but defining types that perfectly mirror your database schema, API responses, and frontend state re

The company now expects to ship a few thousand more vehicles by the end of 2026 than it previously expected, after launching its R2 SUV last month.

Originally published on productize.life . Quick answer: gstack is an open-source (MIT) skill set that Garry Tan, president of Y Combinator, builds with every day. It turns Claude Code into a team of 23 specialists, CEO, engineers, designers, QA, and a release engineer, forcing every change through a multi-lens review before shipping. The point is not speed; it is taste written into software. Last week I was going through a repo that collects skills for coding, several of them. Most share one theme: helping AI write code in a systematic way, and faster. But one made me stop longer than the rest, called gstack, for two reasons. One: its owner, Garry Tan, president and CEO of Y Combinator, took the stack he actually builds with every day and opened it for free. Two: it does not sell "code faster," it sells "review before you ship." Once I actually opened it, it was not just a toolbox but one of the clearest examples of an idea I have been interested in for a while. On the day AI can write code very fast, the bottleneck of the work is no longer speed. I will tell it in three parts, starting with what it is , then what gstack believes , and closing with lessons for people who build products, not just people who write code . Terms, gathered here in one place agentic coding letting an AI agent run the coding work in its own steps, from planning to writing to review to shipping, not just autocompleting a line at a time. skill a packaged set of instructions an AI agent (like Claude Code) can call, like a shortcut that wraps one way of doing one thing. review lens reviewing one piece of work from several roles, for example as a CEO, an engineer, a designer. taste the sense and judgment of what is good and what is bad, what to build and what not to ship. The part that is still human. Part 1: What gstack is Garry Tan describes gstack in the README plainly, as the way he works. "It turns Claude Code into a virtual engineering team: a CEO who rethinks the product, an eng manager

Stop Treating Databases Like Dumb Storage! A Modern Approach to Data Layer Optimization Introduction In the rapidly evolving landscape of cloud-native applications, the database often remains the last bastion of outdated architectural thinking. Too many development teams, even in 2026, treat their databases as little more than dumb storage – a simple receptacle for data. This oversight invariably leads to an insidious problem: what was once perceived as a cost-saving cloud server rapidly transforms into an expensive, resource-hungry bottleneck that devours compute cycles, memory, and, most critically, developer sanity. The knee-jerk reaction to performance woes—throwing more hardware at an unoptimized SQL database or poorly designed NoSQL schema—is not scalable backend design; it's procrastination. This approach might temporarily mask symptoms, but it fundamentally ignores the root cause, leading to spiraling costs and increasing technical debt. Modern backend design demands a paradigm shift: treating your data layer as a strategic, highly optimized component rather than a generic storage utility. The path to true scalability, resilience, and cost-efficiency begins with intelligent data management from day one. Architectural Walkthrough: Embracing Smart Data Strategies Instead of "sharding your problems" through reactive, unguided horizontal scaling, embrace smart data partitioning . This isn't just about distributing data; it's about strategically organizing it to align with your application's access patterns and business domains. 1. Smart Data Partitioning & Query Patterns: Imagine an e-commerce application. Instead of sharding all orders data uniformly, consider partitioning by a natural business key, like customer_id or product_category . This ensures that common queries (e.g., "get all orders for customer X") are localized to a single partition, minimizing cross-partition operations. // Conceptual Service for Order Management class OrderService { private final

The conversation around Agentic AI often focuses on one goal: making agents more autonomous. More tools. More reasoning. More planning. More independence. It sounds like progress. But is more autonomy always the right answer? As software engineers, we rarely optimize for "more." We don't build distributed systems when a monolith is sufficient. We don't introduce microservices because they're fashionable. We choose architectures that balance capability with complexity. The same principle applies to AI agents. The question isn't "How autonomous can my agent be?" It's "How autonomous should my agent be?" Autonomy Is a Design Decision When people talk about autonomy, they often think of it as a feature that an agent either has or doesn't have. In reality, autonomy is a design decision. Every time we allow an agent to make another decision on its own, we are increasing its responsibility. That responsibility comes with benefits, but it also introduces new engineering challenges. More autonomy means the agent can adapt to situations that weren't anticipated during development. It can make progress toward a goal without being guided through every step. At the same time, it becomes harder to predict, validate, debug, and trust. Autonomy isn't free. Thinking in Terms of an Autonomy Spectrum Instead of treating autonomy as a binary concept, it helps to think of it as a spectrum. At one end are systems that simply generate responses. They have no authority to take action. As autonomy increases, agents begin suggesting actions, invoking tools, planning multiple steps, and eventually deciding how to achieve a goal with minimal human involvement. The important observation is that every step along this spectrum increases both capability and complexity. That's why the objective shouldn't be to reach the highest level. It should be to stop at the level your problem actually requires. More Autonomy Isn't Always Better Imagine building an internal HR assistant. Its primary responsibil

The Bublue BuVortex V5 ditches conventional skimming for a vortex-powered design that’s fascinating to watch, even if it’s severely impractical.

I ripped open $892 worth of Pokémon packs on my phone in under 15 minutes and walked away with 62 cents. My adrenaline rush felt like the future of gambling.

Your comments on a dangerous rule putting politicals in charge of science can matter.

Every blog post needs an OG image. Without one, your links look blank on Twitter, LinkedIn, and Slack — just a plain URL that nobody clicks. Most developers solve this by spinning up a headless browser, loading an HTML template, taking a screenshot, and uploading it somewhere. It works, but now you're maintaining a Puppeteer instance, dealing with font rendering quirks, and burning server resources on something that should be simple. There's a faster approach: design your OG images as HTML templates and let a screenshot API handle the rendering. The Idea: HTML Templates as OG Images Think of your OG image as a tiny webpage. You already know HTML and CSS. Build a 1200×630 template with your blog title, author name, maybe a gradient background — whatever fits your brand. Host it or pass it as raw HTML. Then call an API to screenshot it. Done. A basic template might look like this: <div style= "width:1200px;height:630px;display:flex;align-items:center; justify-content:center;background:linear-gradient(135deg,#1a1a2e,#16213e); font-family:Inter,sans-serif;padding:60px" > <div style= "color:#fff;text-align:center" > <h1 style= "font-size:48px;margin:0" > {{title}} </h1> <p style= "font-size:24px;color:#8892b0;margin-top:20px" > {{author}} · {{date}} </p> </div> </div> Replace the placeholders on your server, then send the resulting HTML (or a URL pointing to it) to the API. Calling the API With ScreenshotRun , a single curl request captures the rendered template as a PNG: curl -X POST "https://api.screenshotrun.com/v1/screenshot" \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "url": "https://yourblog.com/og-template?title=My+Post+Title", "viewport_width": 1200, "viewport_height": 630, "format": "png" }' The response gives you the image file. Save it to your CDN, set the og:image meta tag, and you're done. No browser to manage, no Chrome binary eating RAM on your CI server. Wiring It Into Your Build If you publish with a static sit

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

We did what most engineering teams do. Bought an OpenAI API key. Shared it on Slack. Told everyone to start using AI in their workflow. It felt like the right move. Productivity went up. Developers were happy. Managers were impressed. Then the invoice arrived. Nobody could explain it. We could not tell which team spent what, which model was being used, or whether anyone had accidentally sent customer data to an external provider. We had full AI adoption and zero visibility. That is when we realized we had confused access with governance. The problem is not the AI. It is the missing layer between your team and the API. Most teams operate with raw provider keys floating around in .env files, Slack messages, and IDE configs. When someone leaves, you hope they did not take the key with them. When a pipeline misbehaves overnight, you find out from the billing alert, not from your own monitoring. We started asking ourselves some uncomfortable questions: Who on the team is using GPT-4o versus a cheaper model? Is anyone sending PII to an external provider without knowing it? What happens if our OpenAI key gets exposed in a public repo? Can we switch to Anthropic without rewriting half our tooling? None of these are exotic concerns. They are the natural consequences of scaling AI access without an infrastructure layer to govern it. What actually helped We needed something that sat between our developers and every AI provider, handling authentication, enforcing limits, logging every request, and letting us swap providers without touching application code. Think of it the way an API gateway manages microservices. Same idea, but for LLM traffic. Developers point their tools like Cursor, Continue.dev,...at a single endpoint. Two environment variables. Nothing else changes. Behind the scenes, every request is logged, every token counted, every provider key protected. Governance without friction. That is the only kind developers will actually tolerate. If your team is using AI wit

You bought the hardware. Now you’ll need to subscribe for “expanded access” to the most advanced features.

Originally published on wp-nota.com . You installed an SSL certificate and moved your WordPress site to HTTPS — but the browser still shows "Not Secure" in the address bar, or a padlock with a warning. This is the classic mixed content problem: your pages load over secure HTTPS, but some resources on them — images, scripts, or stylesheets — are still being requested over insecure HTTP. Browsers flag the whole page as not fully secure until every resource is served over HTTPS. Here's how to fix it for good. What "Mixed Content" Actually Means When a single page mixes secure (HTTPS) and insecure (HTTP) resources, that's mixed content. The page itself may be secure, but if it pulls in an image or script over http:// , the browser can't guarantee the whole page is safe — so it drops the padlock or shows a warning. The cause is almost always old http:// URLs still saved in your database or hardcoded in your theme. Step 1: Confirm the Certificate and Site URLs First, make sure the foundation is right. Your host must have a valid SSL certificate installed (most offer free Let's Encrypt certificates). Then, in WordPress, go to Settings → General and confirm both WordPress Address (URL) and Site Address (URL) start with https:// . If they still say http:// , update them, save, and log back in. Step 2: Find What's Loading Over HTTP To see exactly which resources are insecure, open the problem page in your browser, right-click and choose Inspect , and look at the Console tab. Mixed content warnings list each http:// resource by URL — often images in old posts, a hardcoded logo, or an asset from a plugin or theme. This tells you precisely what needs fixing. Step 3: Update Old HTTP URLs in the Database The most common fix is a database search-and-replace that swaps every http://yourdomain.com for https://yourdomain.com . Two safe ways to do it: The easy way — the free Really Simple SSL plugin detects insecure URLs and rewrites them to HTTPS automatically, which resolves most mix