标签：#art

When Xander first met Moxie, she taught him that when he was anxious, he could calm down by exhaling through his lips so that he buzzed like a bee. They practiced breathing like dragons to manage feeling mad and sniffing like bunnies to boost his energy. But in the six years they’ve known each other,…

Google has added some smart theft-detection features to Android in recent years.

OpenRouter's CEO recently described the startup as Stripe for AI.

If you really want a OnePlus phone, you can still make it work.

You probably don't need antivirus software on your Android phone, but there are some exceptions.

Take time to enable these features today before you need them; they could help save your life in an emergency.

Observatories have been built in mines, deep under the Antarctic ice sheet, and elsewhere in an effort to detect the fleeting particles.

When many people are first learning how to code, they find it difficult and when they ask, "How can I get better at coding?" they are usually told, "With time and practise it will get easier." . But instead of using so much time and effort to get better at coding using hard programming languages, what if coding could get better for you instead of you getting better at coding ? Well, this is the reason that inspired me to make a new programming language called Sofya . Sofya is designed to be so simple (even simpler than Python ) so that anyone can find programming easy and fun. But to prove my point, let us use an example. Let us say that we want to make a program that will show us all the numbers from 1 to 20 . Let us compare how this program will look like in Python and Sofya . The Python Program for number in range ( 1 , 21 ): print ( number ) The Sofya Program Variable Number is 0 Do this { Increase Variable[Number] by 1 Write Variable[Number] on the screen } Until Variable[Number] = 20 From this example, we can see that the Sofya program is easier than the Python program, for a beginner in programming, for the following reasons: Sofya uses simpler commands than Python: It is easier for a beginner in programming to remember the command Do this...Until Variable[Number] = 20 , which is used for making a loop, as compared to the command for number in range(1, 21): . Sofya's syntax is closer to English as compared to Python's syntax: When we are making a loop variable in Sofya, we simply say Variable Number is 0 rather than saying number in range(1, 21) in Python. The Sofya program can easily be understood by anyone even if it is the first time that they are seeing it as compared to Python: A beginner in programming can easily tell that in the line where we say Increase Variable[Number] by 1 , that we are increasing the value of the variable called 'Number' by 1 as compared to the line number in range(1, 21) in Python. If you would like to try out Sofya for yourself

A few days ago I posted here about DevStacker, my app for helping self-taught developers escape tutorial hell and build real projects. We were getting ready to launch it, but then we found some bugs in the login screen. The app itself was working, but the login flow had some issues, and obviously we can't really launch while the first thing users see is broken lol. So we delayed the launch for a bit and we're fixing it now. I'm pretty close to getting everything ready, so hopefully DevStacker will be live very soon. This is also my first app, and honestly I didn't realize how many random things can go wrong until I actually tried to launch one 😭 Anyway, back to fixing login. I'll post again when DevStacker is finally live 🚀

Dario Amodei Is Right. But He Is Missing the Hard Part. Dario Amodei said the first billion-dollar company with one employee would appear in 2026. He put 70-80% probability on it. I am not building a billion-dollar company. But I am running something that does the work of several teams: 86 containers, 24 databases, 240 cron jobs, two servers, one person. Amodei is right that this is now possible. The tools exist. The costs dropped. A full AI stack costs me between $3,000 and $12,000 per year. The equivalent in human headcount would run $80,000 to $120,000 per month. But the headline version of the "one-person company" story skips the hard part. It sounds like you hire an AI, fire your team, and go make money. That is not what happened for me. What actually happened was eighteen months of building a system that makes "one person" sustainable at 3 AM when something breaks and nobody is awake to fix it. Here is what that system looks like in practice. The Stack Is Not the System Most people stop at the stack. They pick Claude or GPT, wire up a few automations, and call it an AI-powered business. That works until the first thing breaks in a way the model did not anticipate. The stack I run includes SaaS apps for golf clubs, a school management platform, an auth provider, a CRM, a community platform, and several tools for my own operations. Each of these runs in Docker containers managed by Coolify, spread across two Hetzner servers in Germany. That part is table stakes. Any competent developer can set up containers. The system is what sits on top. It is what makes the difference between "one person with a lot of tools" and "one person running a business that actually works." Guard Rules: The Thing That Catches What You Miss I wrote about this in detail in Runs Without Me : the biggest risk in a one-person setup is not that the AI does something wrong. It is that you do not notice until hours or days later. My setup uses 177 guard files that intercept operations before t

The story behind AI Clip Cutter — and why we’re building AI editing around one simple idea: the creator should stay in control. Press enter or click to view image in full size There is an uncomfortable truth about short-form content: Most creators don’t have a content problem. They have a time problem. You can spend an hour recording a podcast. Two hours researching. Three hours having a conversation worth sharing. And then discover that turning that one long video into five genuinely good Shorts is going to take another afternoon. Finding the moments. Cutting them. Reframing them. Writing captions. Making sure the captions don’t start halfway through a sentence. Checking whether the clip actually makes sense without the 30 seconds of conversation before it. Then doing it again. And again. And again. That was the problem that led us to build AI Clip Cutter. AI Clip Cutter But there was another question behind it: What if AI didn’t need to replace the editor? What if it could simply do the boring part incredibly well? The idea was simple Take a long-form video. Find the moments worth sharing. Turn them into short vertical clips. Add captions. Let the creator decide what gets published. Sounds obvious. But once we started building it, we realized that “find the best clips” is not actually a simple problem. A 60-minute podcast can contain dozens of technically valid 30-second sections. But most of them aren’t good Shorts. Some start in the middle of an argument. Some need 45 seconds of context. Some contain interesting information but have no hook. Some are emotional but say nothing. And some sound incredible when you’re sitting inside the full conversation — but completely confusing when they’re watched alone. So we needed the AI to understand something more important than: “What was said?” It needed to understand: “Would someone want to watch this?” We don’t ask AI to pick “interesting” moments This was one of our biggest product decisions. Instead of asking the mode

Spend an afternoon browsing new AI startups and a strange déjà vu sets in. The landing pages rhyme. There is a dark hero section, a gradient somewhere between indigo and violet, a little sparkle or star icon denoting Intelligence, a headline promising to let you “chat with” your documents or data or customers, and a demo video with the same upbeat, slightly anonymous soundtrack. You could swap the logos between fifty of these sites and almost nobody would notice. Sameness on the surface Some of this is just design fashion, and design fashions always converge. But the AI cohort has converged harder and faster than most, and the reason is worth naming: when everyone is building on top of the same handful of foundation models , the differentiation has to come from somewhere else , and branding is the cheapest lever to pull. If your product is a thin layer over a model anyone can call, you cannot differentiate on the model, so you differentiate on the gradient. When the engine is a commodity everyone rents, the paint job is the only thing left to argue about. Hence a thousand identical paint jobs. Funded by the same money, chasing the same story The uniformity runs deeper than design and architecture; it reaches into the incentives. A great many of these companies are funded by the same pools of venture capital, pitched against the same market maps, and steered toward the same narrative arc — explosive growth now, monetisation later, an acquisition or an IPO at the end. When the funding, the advice and the definition of success are shared, the strategies converge. Everyone chases the same enterprise customers, adopts the same land-grab pricing, and races the same clock, because that is the shape of company the money was betting on. This produces a cohort that is not only visually and technically alike but strategically alike, which makes the whole field unusually fragile to the same shocks. A shift in model pricing, a change in what the platform providers offer natively

Every Flutter developer has run into the Sticky State Dilemma . You build a login screen. When authentication fails, your state container emits an error. You catch it in your UI and show a SnackBar . Everything works—until the user rotates their phone, pulls down the notification shade, or types on the virtual keyboard. Suddenly, the widget tree rebuilds. The state container is still holding AuthErrorState("Invalid password") . The UI listener fires again. And a duplicate snackbar appears out of nowhere. In this article, we’ll explore why domain state machines struggle with transient UI events, how the classic BLoC community worked around this with package:bloc_presentation , and how BlocSignal lets you handle one-shot side effects cleanly with zero additional package dependencies . 1. The Root Problem: Persistent State vs. Ephemeral Actions State management in Flutter is designed to model persistent truth over time: Is the user logged in? AuthState.authenticated(user) Is data loading? TodoState.loading What is the cart total? $49.99 Persistent state answers: "What is the system's current condition?" In contrast, UI presentation actions are ephemeral pulses : Show a brief SnackBar toast. Pop up an alert confirmation dialog. Push a new route on the Navigator stack. Vibrate the haptic motor. These actions answer: "What just happened that requires a one-time reaction?" ┌────────────────────────────────────────────────────────┐ │ State vs. Effects │ ├────────────────────────────┬───────────────────────────┤ │ Persistent State │ Ephemeral Side-Effect │ ├────────────────────────────┼───────────────────────────┤ │ • Survived by UI rebuilds │ • Consumed once & gone │ │ • Represented in signals │ • Triggered by an event │ │ • Backed by equality diffs │ • Zero domain state footprint │ └────────────────────────────┴───────────────────────────┘ 2. The Legacy Workarounds (And Their Hidden Costs) Historically in package:bloc and package:flutter_bloc , developers used one of three

AI coding startup Cursor is now officially a part of SpaceX.

Fusion startups have raised $7.1 billion to date, with the majority of it going to a handful of companies.

Auk’s Mini 2 improves on the original model with better lights, a sleeker design, and fun new colors.

When Twitch announced that streamers could opt out, thousands of users questioned why their content was being used to train AI models in the first place.

Building Pure Dart Web Apps Without Compromise When developers evaluate Dart for the web, they typically face a stark tradeoff: Flutter Web : Exceptional for canvas-driven applications, design systems, and cross-platform desktop/mobile parity—but heavy for content-first landing pages, docs, and fast-loading SEO sites. Jaspr Web : A lightweight, component-driven framework that compiles pure Dart to HTML and CSS with instant first paint and full search engine indexing. When we built the official documentation and showcase site for BlocSignal , we knew Jaspr was the perfect foundation. But like many engineers diving into a new UI paradigm, our initial implementation took a shortcut: we used raw StatefulComponent lifecycles and manual .subscribe() callbacks to wire up our state machines. It worked—but it wasn't idiomatic. In this behind-the-scenes case study, we walk through the process of dogfooding bloc_signals_jaspr across blocsignal.dev , replacing manual subscription glue with declarative consumer components, achieving 100,000 operations/sec in compiled JavaScript , and exploring the sheer developer ergonomics of Dart 3.13 primary constructors . The "Manual Subscription Trap": Why Raw .subscribe() Fails at Scale In classic Flutter or Jaspr development, when you create a state machine without framework-level consumer widgets, you might be tempted to subscribe inside initState() : // ❌ THE ANTI-PATTERN: Manual subscription glue in StatefulComponent class LiveVisualizerState extends State < LiveVisualizer > { late final LiveCounterBloc _bloc ; @override void initState () { super . initState (); _bloc = LiveCounterBloc (); // ⚠️ Flaw 1: Every state change triggers a full component setState _bloc . state . subscribe (( _ ) { if ( mounted ) setState (() {}); }); } @override void dispose () { // ⚠️ Flaw 2: Manual dispose tracking _bloc . close (); super . dispose (); } } While this appears harmless in a simple counter demo, it introduces three severe architectural flaws:

The Unitree G1 has found online fame as a relatively affordable robot that can charm a crowd. But can it ever hold down a real job?

Chris Carter chats with Ars about the film he originally wanted to make—and why it's more relevant than ever.