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This will be the first Starship test flight for SpaceX as a public company, testing the market's appetite for the company's "fly, fail, fix" approach to rocket development, which often ends in fireballs.

A panel on data ownership challenged the definition of "ownership," arguing it must extend beyond simple account control to include structural independence, interoperability, and community governance. Speakers like Zenna Fiscella, Paul Frazee, Boris Mann, and Robin Berjon emphasised the need for shared standards, unbundled platforms, and better tools to support user sovereignty. By Olimpiu Pop

If you like going on a long road trip, you might want to steer clear of these vehicles.

Someone would ask a question in a public Slack channel. Every so often a couple of people would start to answer. Then the manager would step in, say what was going to happen, and the thread would go quiet. On its own, it looks like nothing. A decisive manager keeping things moving. But it was a team going quietly into debt, and the dead Slack thread was one of the interest payments. You already know tech debt. You cut a corner in the code to ship faster, and you pay interest on it later in bugs, slow changes, and the one file nobody wants to touch. Culture debt works the same way, except the corners you cut aren't in the code. They're in the norms, the expectations, and the relationships that decide how people actually work together. But tech debt is visible. You can see it, point at the file, write a ticket, argue about whether it's worth paying down. Culture debt is more dangerous because it gives you none of that. You don't watch it accruing. You see the symptoms, and by the time they show up, the debt has already compounded. Let me tell you how a team I joined got there. The reward was volume. The only thing that reliably got praised was pushing a lot of code. The manager was open about it...their whole framing of the job was being able to out ship anyone on the team. Everyone else stayed quiet. Nobody ever stood up and argued against quality. If you'd asked, the manager would have agreed that testing mattered and that quality mattered. Those things just never got prioritized. So over and over, what actually got rewarded (volume) quietly beat what everyone said they wanted. This didn't happen out loud. The reward silently won every time. You can guess what that bought. Planning went first, so features shipped in half finished states and got abandoned there. Testing basically didn't exist. We had a QA person, but things slipped through constantly. Bugs were everywhere. Plenty of features barely worked, and some just didn't. The human side hollowed out at the same

Washington D.C. has become a battleground for Uber and Waymo's competing views.

Davide de Paolis discusses the realities of rolling out cloud infrastructure compliance without fracturing developer relations. Drawing from a real-world platform team reboot at Sevdesk, he explains how to implement "minimum viable governance" on AWS, utilize event-driven Slack alerting to automate policy feedback, and shift from rigid enforcement to high-empathy, data-driven collaboration. By Davide de Paolis

Experts explain how they work, what they can do, and what's still unsettled.

By some benchmarks, Julia code can run 10X to 1,000X faster than Python—but there’s a reason it’s not a very popular programming language.

The web has changed . Applications are no longer simple HTTP servers. Today we build real-time dashboards, AI-powered services, multiplayer systems, APIs, microservices, and applications that need to handle thousands of connections with minimal overhead. But our frameworks are still mostly designed for yesterday's problems. So we asked a simple question: What if a * Go * framework was built from the ground up for modern workloads? Meet Breeze . A high-performance Go framework designed around one idea: Performance should not come at the cost of developer experience. Why Breeze ? Go already gives us incredible performance. But the framework layer often becomes the bottleneck. Too much abstraction. Too many allocations. Too much hidden complexity. Breeze takes a different approach: ⚡ High-performance networking powered by gnet 🔥 Real-time WebSocket architecture built in 🧩 Modular middleware system 📚 Automatic Swagger/OpenAPI generation 🎨 Built-in SPA template engine 🚀 Optimized worker pool architecture 🗄️ BreezeORM for efficient database operations Everything you need to build production-grade applications — without assembling dozens of unrelated tools. The Future Is Real-Time Modern applications are moving toward instant experiences: Live collaboration Trading platforms AI assistants Gaming backends Monitoring systems Real-time analytics Breeze is designed for this world. Instead of adding real-time capabilities later, Breeze treats them as a first-class citizen. Less Glue Code. More Building. A common problem in backend development: You start with a simple API... Then suddenly you need: Authentication Documentation WebSockets Background workers Database optimization Frontend integration Your stack becomes a collection of disconnected pieces. Breeze tries to bring these pieces together into one coherent ecosystem. Built With Go Philosophy Go was created around simplicity, performance, and reliability. Breeze follows the same principles: Simple APIs. Predictable behavi

Most people assume better AI performance means a bigger bill. That assumption is quietly being proven wrong. The "Don't Touch It" Trap in AI Products There's a psychological pattern that shows up in almost every team running a live AI-powered product: once something works, nobody wants to mess with it. And honestly, that instinct makes sense. You've tuned your prompts, worked out the edge cases, trained your users, and finally gotten the thing stable. The idea of swapping out the underlying model - the engine of the whole operation - feels like pulling a thread that might unravel everything. So teams stay put. They watch new model releases come out, read the benchmark comparisons, and quietly decide it's not worth the risk. The phrase you hear most often is "if it ain't broke, don't fix it." The problem is that this logic made sense when model upgrades were expensive and disruptive. That's no longer the default reality. What's actually happening now is that AI providers are competing hard on price-per-token while simultaneously improving quality. That combination - better output, lower cost - breaks the old mental model most product people are still operating with. What a Model Migration Actually Involves Let's be clear: switching AI models isn't a one-click operation. But it's also not the months-long project many teams imagine it to be. At its core, a model migration for an AI agent involves three things: re-evaluating your prompts (because different models respond differently to the same instructions), running parallel tests to compare output quality on your real use cases, and updating any API parameters that differ between versions. That's the actual work. For most small-to-medium deployments, that's days of effort, not weeks. The bigger shift is in how you think about model versions. Rather than treating the model as permanent infrastructure, it helps to think of it more like a dependency in your software stack - something you update deliberately, test careful

Like Google Maps, Waze is going all-in on Gemini.

Waze is getting an AI makeover. Google is integrating its flagship AI assistant, Gemini, into the driving app with the goal of letting users personalize their trips a little more. Of the four new updates, only two are being described as involving Gemini. Waze says its updating its conversation reporting feature, first introduced in 2024, […]

Videos are one of the heaviest assets you can add to a web page. Loading videos too early can significantly impact your application's performance. The good news is that modern browsers are starting to support lazy loading for video elements , allowing you to defer loading until users are likely to watch them. However, there's one important thing to know: 👉 This feature is not yet part of the Baseline web platform , so browser support is still limited. At the time of writing, lazy loading for <video> elements is supported in Chromium-based browsers such as Google Chrome , Microsoft Edge , and Opera , while browsers like Firefox and Safari do not yet support it natively. In this article, we'll explore: What lazy loading videos is Why it's important for web performance How to implement it Browser support considerations Best practices for optimizing video loading Let's dive in. 🤔 What Is Lazy Loading for Videos? Lazy loading means delaying the loading of a resource until it's actually needed. Instead of downloading every video immediately during page load, the browser waits until the video is close to entering the viewport. This helps reduce: initial network requests bandwidth usage page load time memory consumption Especially on pages with multiple videos, the difference can be significant. 🟢 What Problem Does It Solve? Imagine an e-commerce page with several product videos. Without lazy loading: every video starts downloading immediately bandwidth is consumed even for videos users never watch page rendering may become slower This negatively impacts; Largest Contentful Paint (LCP), Time to Interactive (TTI), and overall user experience. Most visitors won't watch every video on the page. So why load them all? Lazy loading ensures videos are fetched only when they're actually needed. 🟢 How to Lazy Load a Video The easiest approach is using the loading="lazy" attribute. Example: <video controls loading= "lazy" poster= "/preview.jpg" > <source src= "/video.mp4" type= "vide

If you’ve ever struggled with CALCULATE() or wondered why SUMX() behaves differently from SUM() , this guide is for you. DAX (Data Analysis Expressions) is the language that powers Power BI , Analysis Services , and Power Pivot — enabling dynamic calculations, filtering, and time intelligence. Below is a categorized cheat sheet of essential DAX functions , plus examples showing how to use each in real-world Power BI scenarios. Filtering & Context These functions control how filters are applied and evaluated in your calculations. Function Example Description CALCULATE() CALCULATE(SUM(Sales[Amount]), Region[Name] = "Nairobi") Changes filter context to calculate total sales for Nairobi. FILTER() FILTER(Sales, Sales[Amount] > 10000) Returns a table filtered by condition. ALL() CALCULATE(SUM(Sales[Amount]), ALL(Region)) Ignores filters on Region. REMOVEFILTERS() CALCULATE(SUM(Sales[Amount]), REMOVEFILTERS(Region)) Removes filters from Region. VALUES() VALUES(Customer[City]) Returns unique list of cities. SELECTEDVALUE() SELECTEDVALUE(Product[Category], "All") Returns selected category or “All” if none. TREATAS() TREATAS(VALUES(Temp[City]), Customer[City]) Applies one table’s values as filters on another. KEEPFILTERS() CALCULATE(SUM(Sales[Amount]), KEEPFILTERS(Product[Category] = "Electronics")) Keeps existing filters and adds new ones. ALLSELECTED() CALCULATE(SUM(Sales[Amount]), ALLSELECTED(Region)) Respects user selections in visuals. ALLEXCEPT() CALCULATE(SUM(Sales[Amount]), ALLEXCEPT(Sales, Sales[Year])) Removes all filters except Year. Aggregation Summarize or aggregate data across rows or columns. Function Example Description SUM() SUM(Sales[Amount]) Adds all sales amounts. AVERAGE() AVERAGE(Sales[Amount]) Calculates mean value. COUNT() COUNT(Customer[ID]) Counts non-blank entries. COUNTROWS() COUNTROWS(Sales) Counts rows in a table. DISTINCTCOUNT() DISTINCTCOUNT(Customer[ID]) Counts unique customers. MIN() MIN(Sales[Amount]) Finds smallest sale. MAX() MAX(Sales[Amo

Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, how AI is playing a part.

The first time a client said yes to a quote without hesitating, I felt sick. This was early on. I'd sent over a rate for a batch of articles, my palms were actually sweaty over the email, and the reply came back in under an hour. "Sounds great, when can you start?" No pushback, no negotiation, nothing. I should have been thrilled. Instead, I sat there doing the math on how much more I could have charged, and I knew, the way you just know sometimes, that I'd priced it too low. His enthusiasm was the tell. That queasy feeling taught me more than any pricing guide ever did. If a client says yes instantly and happily, you were cheap. I've been freelancing for about eight years now, all of it writing and content work, most of it solo from a spare room in my house. I've priced my work a dozen different ways over that stretch, and I've gotten most of them wrong at some point. So here's what I actually believe about pricing, after enough scars to have earned an opinion. Per-word pricing quietly punishes you for getting better I started out charging by the word, like a lot of writers do. Five cents a word, sometimes six if I was feeling brave. It felt safe because it was easy to explain and easy for a client to say yes to. A 1,500-word article costs this much. Clean. Predictable. The problem showed up slowly. The better I got, the worse that model treated me. Early on I'd pad a piece to hit a word count because more words meant more money, which is a genuinely insane thing to be incentivized toward as a writer. Then I spent years learning to cut. Learning that the sharpest version of an article is usually the shortest one that still does the job. And every ounce of that hard-won skill made me poorer, because a tight 900-word piece that took real judgment to shape paid less than a bloated 1,400-word one I could have written half-asleep. Think about how backwards that is. I was being paid the least for the writing I was proudest of. The stuff that took a decade to be able to d

New climate evidence adds context to these long voyages.

Apple has announced several new Child Safety features coming soon to iPhones and other devices. Here’s what’s changing.

This is a submission for Weekend Challenge: Passion Edition Target categories: Best use of Snowflake...

@kungfufk Since the dawn of computing, we have built Artificial Intelligence on a flawed premise: perfect rationality. We brute-force algorithms to find the optimal solution, assuming infinite time and infinite capacity. But humans don't work like that. As Herbert Simon famously coined, we operate on Bounded Rationality. We make decisions based on limited time, limited cognitive capacity, and limited information. What if, instead of forcing AI to be perfectly rational, we created a mathematical equivalent for human processing? What if we modeled human cognition using the laws of physics — wave theory, thermodynamics, and mechanical energy equations — to build a heavy, complex, but highly probabilistic AI engine? Here is a blueprint for a new field of research: Computational Cognitive Mechanics . 1. The Core Equations of Cognitive Processing To model bounded rationality mathematically, we first need to define the relationship between Knowledge ($K$), Cognitive Capacity ($C$), and Processing Time ($T$). Based on human observation, we can establish these foundational proportions: Knowledge vs. Time — The more knowledge you possess, the faster you can generate a decision. $$T \propto \frac{1}{K}$$ Capacity vs. Time — High cognitive capacity (skills, processing power) inversely relates to the time required to solve a problem. $$T \propto \frac{1}{C}$$ Knowledge vs. Capacity — This is the most fascinating limit. Knowledge does not scale linearly with capacity. Gaining true knowledge requires exponential capacity (effort/skill). Therefore, knowledge is roughly proportional to the square root of capacity. $$K \propto \sqrt{C}$$ By integrating these, we can build a baseline processing algorithm for an AI. Instead of giving an AI unlimited time to compute, we cap its computing time based on a synthetic "Knowledge and Capacity" matrix, forcing it to use heuristics — just like a human. 2. Cognitive Wave Theory & FFT: Information as Interference In physics, waves interact throug