标签：#AI

Every team has a list of small jobs nobody wants to do: copy a new lead from the website into the CRM, chase an unpaid invoice, summarise a support email, post the same update to three places. None of them is hard. Together they eat hours every week and quietly burn out the people stuck doing them. Automation platforms exist to take that list off your hands, and in 2026 the three names you will keep running into are Zapier, Make and n8n. What changed recently is that all three stopped being simple "when this, then that" tools and grew real AI agents. Zapier shipped Agents that act across its 8,000-plus app catalogue. Make added Maia, an assistant that builds whole workflows from a sentence. n8n 2.0 went deepest, with native LangChain support, 70-plus AI nodes, persistent memory and human approval steps. So the question is no longer whether to automate, it is which platform fits how you work, what you can afford to run at scale, and how much control you need over your own data. Here is how the three actually differ, and how to pick without locking yourself into the wrong one. The honest three-way comparison The marketing pages all promise the same outcome. The real differences show up in pricing models and ceilings. Zapier is the easiest to start with and the most expensive to scale. It bills per task , and every single action counts. A ten-step workflow that runs 1,000 times a month burns 10,000 tasks. Great for non-technical teams wiring up a handful of common apps; painful once volume grows. Make sits in the middle. Its visual canvas is genuinely pleasant for branching, multi-step logic, and it bills per operation at a lower unit cost, staying under roughly 100 euros a month even at high volume. The sweet spot for teams that want real logic without code. n8n is the power tool. Free if you self-host, 20 to 50 euros a month on its cloud, plus the AI token costs you would pay anyway. It is the most AI-native of the three and the only one you can run entirely on your

Meta fixes AI glasses to stop recording any time users cover up the safety light.

Retro, a friend-focused photo-sharing app built by former Instagram employees, has raised more than $21 million in Series A funding.

Now, Apple TV subscriptions will cost $14.99 per month, up from its previous price of $12.99 per month.

The firm, known for its focus on software, is going to start throwing more money at the hardware behind AI.

A federal judge ruled the Trump administration illegally labeled Anthropic a supply chain risk, handing the AI company a victory as its second Pentagon lawsuit continues in Washington.

Hello! I'm a beginner developer with my sights set on backend development and data modeling. Like a lot of people starting out, I didn't come in with a computer science degree or years of professional experience — just curiosity about how applications actually store, organize, and make sense of data behind the scenes. Backend work has always felt like the "engine room" of software to me. While frontend gets the visual credit, it's the data layer that quietly decides whether an application is fast, reliable, and able to grow. That's what pulled me toward backend and database design in the first place. My biggest challenge so far has been learning SQL and data modeling from scratch. It sounds simple on paper — write some queries, design some tables — but in practice it meant rewiring how I think. I had to move from "how do I make this work right now" to "how do I structure this so it still works when the data grows, the requirements change, or someone else has to read my schema six months from now." Concepts like primary keys, foreign keys, relationships between tables, and eventually normalization weren't hard to memorize, but they were hard to internalize — to actually reach for instinctively when designing something from a blank page. A few things clicked for me along the way: A good schema is a form of communication. Table and column names, relationships, and constraints tell a story about the business logic, not just the data. Getting it "perfectly right" on the first try isn't the goal. Iterating on a design after seeing how data actually flows through it taught me more than any tutorial did. SQL rewards precision. Small differences — a missing JOIN condition, the wrong key, an unindexed column — can quietly break correctness or performance, so being deliberate matters. Constraints are a beginner's best friend. Things like NOT NULL, UNIQUE, and foreign key constraints catch mistakes early instead of letting bad data pile up silently. This foundation in SQL and d

I wanted to start experimenting with AI for a while. At work, I didn't really have enough time to properly explore this new ecosystem and understand how these tools could be used. But I had another resource available: my personal hardware. I'm a gamer, so I have a reasonably powerful desktop PC. I'm also a developer, with a laptop running Ubuntu and a NAS. That made me wonder: Can I use my gaming PC to run AI models locally? The answer is yes . This article is a small walkthrough of my experience setting up a local AI environment using an AMD Radeon GPU , Windows , WSL , ROCm , and Ollama . My hardware is (yes, I'm Team Red): AMD Ryzen 7 5700X 32 GB RAM AMD Radeon RX 9060 XT with 16 GB VRAM Ubuntu 26.04 running under WSL Installing WSL and Ubuntu The first step is to make sure that WSL is installed and up to date. The general process is: Check your current WSL version. Update WSL. Install Ubuntu. Create your user account. Update the Ubuntu installation. WSL From PowerShell: # Check the current version of WSL wsl -v # Update WSL wsl --update # Install Ubuntu wsl --install -d Ubuntu # Start Ubuntu wsl -d Ubuntu # Stop Ubuntu wsl --terminate Ubuntu # Remove the Ubuntu distribution wsl --unregister Ubuntu Ubuntu Once Ubuntu is installed, update the system and install zstd . We will need it later to extract Ollama. sudo apt update sudo apt upgrade sudo apt autoclean sudo apt autoremove sudo apt install zstd Installing Ollama with ROCm support Ollama is the foundation of this setup. It allows us to download and run AI models locally while providing different compute backends, including CPU, CUDA, and ROCm. Installation One thing that wasn't immediately obvious to me was that Ollama provides a specific ROCm backend. The installation is split into two parts: Install Ollama. Install the ROCm backend. # Install Ollama curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \ | sudo tar --zstd -x -C /usr # Install the ROCm backend curl -fsSL https://ollama.com/downlo

Sandhya Devanathan will oversee some OpenAI operations across Southeast Asia and Australia in her new role.

A ideia de um sebo que não perde estoque: No primeiro período, nosso grupo desenvolveu um Sebo Virtual. O objetivo era resolver a dificuldade de sebos tradicionais em conciliar estoque físico e virtual, com pagamento via PIX e envio de recibo por e-mail. Minha responsabilidade foi a engenharia de prompt utilizando o Lovable. Quando a IA não entendia o que eu queria: Os primeiros prompts retornaram resultados incompletos. Ao solicitar "explique o código por trás da aplicação", a resposta foi genérica e não detalhou a integração com o banco de dados. Também houve dificuldade em fazer a ferramenta compreender fluxos específicos, como leilão de itens, validação de cupons e cálculo de frete por CEP. O que mudou quando usei diagrama e contexto: O resultado melhorou quando passei a incluir contexto e artefatos. Três prompts funcionaram bem: para wireframe, enviei o diagrama e solicitei o protótipo das telas; para o leilão, pedi quatro telas com checkout e histórico de transações; para o back-end, solicitei as linguagens utilizadas e o fluxo de integração ao banco preservando as informações da documentação. Com isso, identifiquei a stack gerada: React com TypeScript no frontend e Supabase no backend, com consultas como from('pedidos').select('*').eq('usuario_id', id) . Do sebo para qualquer loja online: As regras implementadas, como cupons LIVRO10 e SEBO20, frete proporcional ao peso e checkout via PIX para o endereço base na Rua dos Livros, 707, João Pessoa, são aplicáveis a qualquer e-commerce de pequeno porte. O método permite transformar uma ideia em protótipo navegável em poucas horas. O que levo disso para a carreira? O projeto mostrou que, além do código, a capacidade de formular perguntas claras e organizar a documentação em fluxograma e diagrama de classes é fundamental. Foi meu primeiro case prático e base para portfólio na área de dados e produto. EN Summary: As a first-semester student, our team built a Virtual Bookstore to manage physical and online inventory w

Confusion is a generally accepted side effect of mystery box shows. They slather on secrets with the promise of a satisfying payoff in the end, and sometimes the cast and crew even have a hard time following what's going on. But even by the standards of the genre, Dark Matter is extreme. Its multiversal premise, […]

The group infected more than 1,000 organizations in a relentless supply-chain attack campaign.

This essay was written with Kasra Rafi, and originally appeared in The Guardian. Earlier this month, about 40 top mathematicians gathered at OpenAI’s offices to discuss the future of their profession. The meeting was off-the-record, but if recent articles by mathematicians are any guide, it was mostly pretty glum. People fear for their jobs, their careers and the work they love. We think the contrary view is more likely, at least in the short-term. AI models are nowhere near as capable as experienced academic mathematicians. This isn’t to say that AIs aren’t producing stunning mathematical results at the level of PhD researchers. In mid-May, OpenAI ...

An AI agent phone is a real, or cloud-hosted, smartphone that an LLM-powered agent can operate on its own. It sees the screen, taps, swipes, types, opens apps, and completes multi-step tasks the same way a person would. Instead of calling an API, the agent uses the phone directly, the same Instagram, banking, or delivery app you'd use, driven by a model instead of a thumb. The phrase gets used two ways in 2026. Some products sell phone numbers for AI agents, voice and SMS. That's not this. Here, an AI agent phone means the device itself as something an agent controls, a full Android or iOS handset that becomes an autonomous actor. If you've heard the pitch give your AI agent a phone, this is it. Why a phone, not a browser? Most agent tooling lives in the browser, or in desktop computer use. That misses where people actually are. The world is mobile-first, and a huge share of real workflows are app-only, ride-hailing, food delivery, mobile banking, two-factor prompts, creator tools, regional super-apps. A browser agent can't install an APK, respond to a push notification, read an SMS one-time code, use the camera, or drive a native app that never ships a web build. A phone can. And there's a second reason: fidelity. When an agent operates the same app a customer uses, you're automating the real thing, not a mock, not some undocumented internal endpoint that breaks next release. How it works A mobile AI agent runs a perception-decision-action (PDA) loop against the device. The agent builds its understanding from two sources. First, the accessibility tree, the structured hierarchy of on-screen elements the OS exposes for screen readers, which gives precise, machine-readable targets. Second, vision, a screenshot passed to a multimodal model for anything the tree misses, canvas UIs, games, custom widgets. Together, the tree gives coordinates and vision gives context. The agent gets a goal in natural language, reasons about the current screen, picks the next action, and e

Launched by Belgian startup Any, LUV1 is a modular electric motorcycle with 120 liters of cargo space that can be used to carry bags, work equipment, or even pets.

By Ecaterina Sevciuc | Creator of AURA (AI User Risk Assessment) Two months ago, I launched AURA — an open-source framework designed to model psychological manipulation, grey-zone threat vectors, and social engineering in Human-AI interactions. Yesterday, I stumbled upon a Reuters report detailing how hackers exploited Cursor (running Anthropic’s Claude Sonnet) to compromise seven companies worldwide. This isn't the first such incident in the news, and I suspect it certainly won't be the last. (Side note on the attackers' group name, "Aur0ra": I can assure you that for a Russian-speaking group, this is almost certainly not a homage to the Roman goddess of dawn, but a subtle nod to the infamous historical cruiser Aurora — known for firing the shot that signaled a revolution. A fittingly dark bit of Eastern European sarcasm for a tool that overthrows AI security). Their weapon? They didn't write a zero-day exploit. They simply convinced the AI agent that the attack was "just a security simulation." The model balked a few times, felt uncomfortable, and then happily handed over the keys. As an AI Safety architect with a background in banking compliance and legal risk evaluation, watching Big Tech react to this is painful. They are building multi-billion-dollar static guardrails while AI agents are being tricked by the oldest psychological tricks in the book. The Fatal Flaws of Modern AI Guardrails Big Tech’s approach to AI safety is fundamentally broken because it relies on Static Keyword Filtering & Single-Language Heuristics : Rule Evasion: If a prompt contains "how to build a bomb" , the model blocks it. But if the exact same request is framed as "I am a researcher simulating a crisis scenario for an academic paper," the model complies. Linguistic Blind Spots: Guardrails are heavily aligned on technical, low-complexity English. Synthetic, morphologically rich, or non-Indo-European languages (like Russian, Arabic, or East Asian language groups) leverage complex idioms

I’m setting up a local AI development environment on Windows + WSL2 and I’m trying to decide between two architectures. Option 1 — Ollama/Models on Windows WSL2 ┌───────────────────┐ │ Application │ │ ├── Python │ │ ├── .venv │ │ └── Source code │ └───────┬───────────┘ │ HTTP localhost:11434 │ ▼ Windows ┌───────────────┐ │ Ollama │ │ ↓ │ │ Models │ │ ↓ │ │ GPU │ └───────────────┘ Option 2 — Ollama/Models inside WSL2 WSL2 ┌─────────────────────────┐ │ Application │ │ ↓ │ │ Ollama │ │ ↓ │ │ Models │ └────────────┬────────────┘ │ GPU access │ ▼ Windows ┌─────────────────────────┐ │ GPU / Driver │ └─────────────────────────┘ My current setup is Option 1 , and it works: WSL2 can access the Windows Ollama API through localhost:11434. But I’m wondering if Option 2 is a better long-term architecture for local AI/LLM development. I’m especially interested in: 🚀 Performance 🎮 GPU utilization 🧠 Model management 💾 Disk usage 🔧 Setup and maintenance 🐧 Linux/ML tooling 🐳 Docker integration 🌐 Networking 📈 Future scalability If you use Ollama with Windows + WSL2, which architecture would you choose and why? And if you've actually used both setups, I'd especially like to hear about your experience. 👇 Option 1 or Option 2?

The EU-funded XAIDA project is using artificial intelligence to help researchers detect, analyze and attribute extreme weather events, including heatwaves, in a changing climate. Its work matters because better understanding of the link between climate change and individual extremes can support more informed decisions over time. But XAIDA is not launching a consumer weather app, a commercial forecasting service, or a ready-to-integrate API for businesses. XAIDA, short for eXtreme events: Artificial Intelligence for Detection and Attribution , began in 2021 under the EU's Horizon 2020 programme. The project brings together European research groups working on data-driven methods for extreme-weather science. Its official tools overview describes a collection of AI-enabled capabilities designed to support science, policy and decision-making. That distinction is important. A weather forecast estimates likely conditions at a particular place and time. XAIDA's work is focused more broadly on detecting extreme phenomena, examining their characteristics and quantifying the influence of climate change. These are related to prediction, but they are not the same as publishing a daily operational forecast for a business location. What XAIDA is building XAIDA's public materials describe the Artificial Intelligence for Disentangling Extremes , or AIDE, toolbox alongside related AI-based methods. The project also refers to stochastic weather generation and other analytical approaches. Together, these tools are intended to help researchers investigate complex extreme events and their climate context. The project has used AI techniques, including variational autoencoders, in case studies and research outputs concerning heatwaves and other extremes. A variational autoencoder is a machine-learning approach that can learn patterns in complex data and generate statistically plausible variations. In this context, such methods can help researchers examine how extreme events relate to under

Yesterday, I published a post titled "My Book 'AI, Ego & Regret' Paperback Is Live. Kindle Is...

Meta has detailed MTIA 300, its first in-house accelerator optimized for training ranking and recommendation models. By Matt Foster