标签：#art

We recently got the shape() function and corner-shape property. What else could we possibly need as far as making shapes in CSS? Let me tell you: the border-shape property! Get Ready For the Powerful CSS border-shape Property! originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

AI law startup Norm has raised a $120 million Series C round led by Khosla Ventures, valuing the startup at $1.2 billion.

Bidbus, which lets dealerships bid on used cars, has raised $15 million in a Series A round that was led by early-stage mobility fund Ibex Investors.

iRobot just announced its first-ever non-robotic floor cleaner. The $399 Roomba Electro Plus is a 5-in-1 hard-floor cleaner that combines vacuuming, mopping, and disinfecting, but you have to operate it yourself. The company also announced updates to its line of Roomba robot vacuums, launching five new models with higher suction power, smaller footprints, and lower […]

You can clip a cover over the cameras, which could be a double-edged sword.

I came into this as a graphic designer, not a software engineer. I didn't have a computer science background, and a lot of what BrandStack needed — authentication, databases, payments, deployment — was new territory for me when I started. What made it possible wasn't some shortcut. It was breaking the problem down into pieces I could actually learn: how user accounts work, how a database should be structured so one person's data never leaks into another's, how to move from test payments to real ones without breaking checkout for actual customers. I made real mistakes along the way. Early on, every user shared the same underlying brand data because I hadn't scoped the database correctly to each account — a serious bug that I only caught by testing with two separate accounts myself. Finding and fixing that taught me more about proper application architecture than any tutorial could have. I don't think being a designer first is a disadvantage for building product. If anything, it means the interface and the experience get real attention, not just the backend logic. But it does mean being honest about what you don't know yet, and being willing to slow down and actually understand a problem instead of copying a fix you don't understand. BrandStack is still a work in progress. But it's a real, working product — built by someone who had to learn most of this from scratch, in public, one bug at a time.

The company just raised $7 million in seed funding, and is launching its app for iPhone and Android on Tuesday.

Amazon Web Services (AWS) has announced a major expansion of its AWS DevOps Agent, introducing new release management capabilities designed to assess code changes and autonomously test software before it reaches production. By Craig Risi

With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future. Returning to the foundational elements of AI architecture—the…

Norwegian striker Erling Haaland isn’t just a footballer anymore. He’s become an internet character perpetuated by fans and AI.

Aaron Erickson explains how NVIDIA designs and tests purpose-built AI agent hierarchies. For senior developers and architects, he outlines why balancing deterministic tools with agentic discovery is crucial. Discover how to leverage rare context, implement LLM-as-a-judge test pyramids, and avoid the paradox of choice to build highly reliable, production-grade AI systems at scale. By Aaron Erickson

Decentralized Autonomous Organizations are often presented as simple governance systems: token holders create proposals, vote, and execute decisions on-chain. In practice, building a production-grade DAO is far more difficult. A DAO is not only a smart contract. It is a distributed coordination system that combines governance logic, treasury security, token economics, identity, off-chain infrastructure, and human decision-making. A failure in any one of these layers can compromise the entire organization. Below are some of the most important technical problems DAO developers must solve. 1. Governance Attacks Through Borrowed Voting Power Many DAOs calculate voting power based on the number of governance tokens held at a specific moment. This creates a serious attack surface when tokens can be borrowed through lending protocols or flash loans. An attacker may temporarily acquire a large amount of voting power, submit or approve a malicious proposal, and return the borrowed assets shortly afterward. The standard defense is snapshot-based voting power. Instead of checking a user’s current balance, the governance contract reads historical balances from a previous block. function getVotes( address account, uint256 blockNumber ) public view returns (uint256) { return token.getPastVotes(account, blockNumber); } However, snapshots alone do not solve every problem. Developers should also consider proposal delays, minimum token-holding periods, quorum requirements, and vote-delegation risks. 2. Dangerous Proposal Execution The most sensitive part of a DAO is usually the executor. A successful proposal may call arbitrary contracts, transfer treasury assets, upgrade protocols, or change governance parameters. If proposal calldata is incorrectly validated, a governance action can execute unintended operations. A DAO should clearly separate: Proposal creation Voting Proposal queuing Timelock execution Emergency cancellation Using a timelock gives token holders and security teams

This is part of my work with 01MVP on OpenNomos — a project that helps founders validate ideas before building. The $0 Launch I once spent three months building a product. It had everything: authentication, payments, a polished UI, dark mode. I was proud of it. Launch day: 27 visitors. Zero signups. I had spent 90 days building and precisely zero days asking anyone if they wanted what I was building. I was solving a problem that existed only in my head. The Hardest Lesson The product wasn't bad. The code was fine. The UI was clean. The problem was that I never validated the core assumption: does anyone actually have this problem, and would they pay to solve it? This is the most common failure mode in indie hacking. You build something you think is cool, polish it to perfection, and launch to silence. The code was never the bottleneck. The validation was. What I Do Differently Now Talk to 10 people before writing code. Not surveys. Not landing page analytics. Actual conversations. "Would you use this? Would you pay for it? Why or why not?" Build a mockup, not a product. A Figma prototype or even a Google Form that simulates the core workflow is enough to test willingness to engage. Charge from day one. Free users will tell you nice things. Paying users will tell you the truth. If nobody will pay, the idea isn't ready. Kill fast. Most ideas fail. The goal isn't to make every idea succeed — it's to fail the bad ones quickly so you can find the good ones. Why This Matters More in 2026 In 2016, building a product was hard. You needed to know how to code, set up servers, handle deployments. The barrier to building kept bad ideas from being built. In 2026, Cursor writes your code, v0 generates your UI, and Replit deploys it. The barrier to building has collapsed to near zero. But here's the problem: AI can help you build anything. It cannot help you figure out what's worth building. The result is a flood of well-built products that nobody wants. The bottleneck shifted from

Hi everyone, I'm Marc, the founder of ClaimMate AI. I've been building an AI software engineering platform that helps developers generate code, explain existing code, debug issues, create tests, review code, and build applications from simple prompts or voice. I'm still in the early stages and would really appreciate honest feedback from other developers. Why I Built It I wanted one workspace where developers could chat with AI, generate code, debug problems, and iterate on ideas without constantly switching between multiple tools. I'd Love Your Feedback If you have a few minutes, I'd appreciate any thoughts on: Is the interface easy to understand? Which feature would you use most? What would stop you from using it regularly? What feature is missing? You can try it here: https://ClaimMateAI.pro I'm not looking for praise—I genuinely want constructive feedback that will help improve the product. Thanks for your time!

Fortunately, it shouldn't take too much extra space.

The industry is described as a "dual-track" race. On one side are incumbents (Big Tech) with massive infrastructure and deep pockets. On the other is a wave of nimble startups specializing in specific engineering, error-correction, and simulation challenges. The sector is currently transitioning beyond the Noisy Intermediate-Scale Quantum (NISQ) era toward fault-tolerant systems and commercial quantum advantage—the point where quantum machines reliably outperform classical supercomputers for useful tasks. These companies are building the foundational cloud-accessible platforms and hardware: Amazon Braket (AWS) IBM Google Quantum AI Microsoft NVIDIA These players are driving innovation in specific qubit modalities or niches: Superconducting Qubits: Rigetti Computing, IQM, and Atlantic Quantum. Trapped Ion: IonQ, Quantinuum, and Alpine Quantum Technologies. Neutral Atom: QuEra, PASQAL, and Atom Computing. Photonic: Xanadu, PsiQuantum, and Quandela. Silicon/CMOS: Diraq and Silicon Quantum Computing. Error Correction: Riverlane and Q-CTRL are focused on the "noise" problem, helping make unstable qubits behave predictably. Software & Algorithms: Classiq (design automation) and Multiverse Computing (finance/optimization applications). Quantum-Safe Cybersecurity: PQShield and evolutionQ are developing cryptographic solutions to protect data against future quantum threats.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. OpenAI CEO Sam Altman’s oft-discussed promise that Americans will share in the wealth AI creates was in the news again last week. On Thursday, the Financial Times reported that Altman is in…

Anthropic accused of spying on users; engineer says “experiment” is over.

Netflix engineers introduced dynamic partition splitting for Cassandra to address wide partitions in time series workloads. The metadata-driven approach detects oversized partitions, splits them smaller units, and routes reads across child partitions. Netflix reported lower read latency from seconds to milliseconds, reduced timeouts, and improved cluster stability while maintaining transparency. By Leela Kumili

Station F, the Paris-based startup hub founded by French billionaire Xavier Niel in 2017, is gearing up for a new edition of its selective acceleration F/ai program that will cement its role as a stepping stone for AI startups.