标签：#an

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.

I’ve been working on a desktop utility called Ripper, a Python + CustomTkinter app that downloads video and audio from supported sites (starting with YouTube). The app itself has been a bit rough to build and maintain — but distributing it has been the annoying part. GitHub won’t host my repository, let alone the EXE, due to there size and I don’t want to rely on sketchy file hosts or temporary mirrors. So I finally figured out a temporary setup that’s stable and easy for users to follow. This post explains the distribution workflow and why I’m using it. Why I’m Using this Approach The EXE and source code are too large to push to GitHub, even when the ffmpeg EXE is zipped, and one of my main goals is that I don't want the user to have to hassle with getting ffmpeg. So, I set up a public Google Drive folder where users can get the zipped EXE file and use the app right away. But I want to emphasize that there’s nothing malicious. Google Drive Hosts the EXE Google Drive ended up being the simplest reliable host. It gives me: A clean public link No ads No expiration No weird redirects Instant updates when I replace the file Here’s the current download link: Download Ripper (Google Drive) https://drive.google.com/file/d/1w6rMgCAcSEteAssXIGJmYHrtyPHY99tC/view This is the only official download source. GitHub Pages Hosts Everything Else Since GitHub Pages can host static content, I built a simple project page that contains: https://codebunny20.github.io/ The official download link Feature list Tech stack Build instructions Planned features Version notes Development updates This page is now the “home base” for Ripper. Any time I push a new version, I update the Google Drive file and update the GitHub Pages site with the new version info. It keeps everything centralized without relying on GitHub Releases. Why This Setup Works Better It’s not fancy — but it’s reliable. I can update the EXE instantly I can update the GitHub Pages site just as fast Users always have one clean,

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

Alpharetta, Georgia, cops share data with thousands of Flock users, ranging from federal agencies to a fish and wildlife commission. The reasons why show how vast—and invasive—the network has become.

There are four patterns that bring PQC into a Spring Boot fleet: encrypting payloads between services, locking down database fields, signing documents that need to hold up for decades, and moving service tokens off RS256. Along the way, we discuss why Harvest Now, Decrypt Later is already happening, and why none of this is production-safe until KMS or Vault is in place. By Pankaj Sharma

MIT Technology Review’s How To series helps you get things done. Your thermostat may not look like a power plant. Neither does your electric vehicle, home battery, or HVAC system. But utility and energy companies increasingly want to treat them like one. A virtual power plant, or VPP, is a collection of household devices (such…

Il rischio reale dell'AI enterprise non sono gli agenti autonomi. È la complessità tra di loro. Executive Briefing — Settembre 2026 Quando le aziende deployano fleet di agenti AI invece di sistemi singoli, il pericolo vero non è un agente che si mette a fare il matto da solo. È la complessità emergente delle loro interazioni: una ragnatela di chiamate a cascata, permessi dimenticati e gap di accountability che nessuna checklist può chiudere. 1. Il problema che nessuno vede arrivare Le aziende non deployano un agente e lo guardano girare. Deployano fleet: bot di supporto, agenti di retrieval, layer di orchestrazione, ognuno che chiama API, delega ad altri agenti, si infila in sistemi che non erano stati progettati per decisioni automatiche. Lo scenario che dovrebbe farvi perdere il sonno non è un singolo agente che combina un guaio. È cento agenti che fanno esattamente quello per cui sono stati costruiti, tutti insieme, in combinazioni che nessuno ha disegnato. La complessità non cresce linearmente col numero di agenti. Aggiungi un secondo agente e aggiungi una connessione. Aggiungi il decimo e potenzialmente aggiungi decine di connessioni, perché ora qualsiasi agente può chiamarne un altro, e ogni chiamata può scatenarne una terza altrove. Un ticket di supporto che prima toccava un solo sistema oggi può passare attraverso quattro agenti prima che un essere umano lo veda. E ogni passaggio è un punto decisionale non approvato. La maggior parte dei programmi AI enterprise si blocca quando gli umani responsabili perdono il filo. Chiedete a un team security quali agenti possono raggiungere quali sistemi, e otterrete silenzio. Chiedete quale agente ha triggered quale downstream action tre salti fa. Ancora silenzio. 2. Perché le checklist non funzionano L'istinto è trattarlo come compliance: approva l'agente, registralo, passa oltre. Ma una checklist valuta un singolo punto nel tempo. La complessità corre lungo una catena, e non puoi governare una catena con una pila di ap

Enterprise AI's real risk isn't autonomous agents. It's the complexity between them. Executive Briefing — September 2026 When enterprises deploy fleets of AI agents instead of single systems, the real danger is not a rogue agent. It is the emergent complexity of their interactions — a web of cascading calls, forgotten permissions, and accountability gaps that no checklist can fix. 1. The problem nobody sees coming Enterprises do not deploy one agent and watch it run. They deploy fleets: support bots, retrieval agents, orchestration layers, each calling APIs, delegating to other agents, reaching into systems that were never designed for machine decision-makers. The failure mode that should keep you up at night is not a single agent doing something bad. It is a hundred agents doing exactly what they were built to do, all at once, in combinations nobody designed for. Complexity does not grow linearly with agent count. Add a second agent and you add one connection. Add a tenth and you potentially add dozens, because any agent might call any other, and each call can trigger another somewhere else. A support ticket that used to touch one system might now pass through four agents before a human ever sees it. Every handoff is an undocumented decision point. Most enterprise AI programs stall when the humans responsible lose the thread. Ask a security team which agents can reach which systems, and you get silence. Ask which agent triggered which downstream action three hops ago. More silence. 2. Why checklists fail The instinct is to treat this like a compliance checklist. Approve the agent. Log the agent. Move on. But a checklist checks a single point in time. Complexity runs across a chain, and you cannot govern a chain with a stack of one-time approvals any more than you can call a diet successful because you had a vegetable once. Two failure modes dominate. Permissions creep. Somebody builds an agent to summarize support tickets and grants it broad API access because scop

Our decision to wind down our contract providing OpenAI models to Cursor following its acquisition by SpaceX.

On Thursday, a judge ruled that the Pentagon's blacklisting of Anthropic earlier this year was unconstitutional, delivering the AI lab a win in a monthslong rollercoaster of a battle with the Trump administration. The lawsuit, filed in March in a California district court, accused the Trump administration of unlawfully retaliating against Anthropic for setting "red […]

Tuesday, 10:47 AM. Fourteen developers open their IDE extensions at once, and the shared AI server starts returning timeouts. Nobody planned for the morning spike. The free tier was announced on Monday, the team adopted it by Tuesday, and the first capacity incident happened before lunch. This article is a 30-minute load-test workflow for teams that just received access to a free hosted AI server. The goal is not to benchmark model quality. The goal is to find the concurrency ceiling before your team does — the hard way. The Free Server Is a Shared Resource Now MonkeyCode is an open-source AI coding project that offers free models and a free server. The offer is attractive for the same reason it is dangerous: it removes the two usual adoption barriers — API billing and self-hosting operations — and turns the server into a shared team resource overnight. Disclosure: This article was prepared as part of MonkeyCode's product outreach. A shared resource without a measured ceiling behaves like a shared database without connection pooling. It works in the demo, degrades under load, and fails at the worst possible moment: the morning standup, the release freeze, the day before the demo. The failure mode is not what most teams expect. It is not the token quota. It is latency collapse. Requests queue, timeouts cascade, and the IDE extension retries, which adds more load. The server does not die; it just becomes unusable. The Math: Little's Law for AI Requests Before writing any test code, define the model. Little's Law states that the average number of requests in a system equals the arrival rate multiplied by the average service time: L = λ × W L — average requests in the system (concurrency) λ — arrival rate, requests per second W — average service time per request, in seconds For an AI server, W is dominated by model inference time. A single code-generation request can take 10 to 40 seconds on a shared free server, depending on the model and the prompt length. That change

A federal judge has called the Department of Defense’s designation of Anthropic as a national security supply-chain risk “illegal and baseless.”

OpenAI and Thailand’s MHESI launch an eight-week accelerator helping 10 health, wellness, and education startups turn AI prototypes into trusted products.

Your filesystem is already a database. Most tools just don't treat it that way. That's the core idea behind the Markdown Database Pattern — written up properly on The Way of Markdown , a site we've been contributing to that makes the case for building things on plain markdown instead of locked-in platforms. We think it's a pattern worth more attention, so here's the short version. Treat a folder of markdown files as a database. Each file is a record. Frontmatter fields are columns. Directories are tables. Tags, wikilinks, and tasks in the body become queryable relations. Filesystem Database ────────────────────────────────── markdown file → record frontmatter field → column directory → table #tag → tag relation [[wikilink]] → link relation - [ ] task → task relation You get portability, version control (git works perfectly on plain text), no framework lock-in, and full queryability. You give up scale and real relational joins — this isn't for millions of records. It's a lightweight database, honest about its limits. Once you name it, you start seeing it everywhere. Obsidian Bases and Dataview already do versions of this, half-consciously. A team wiki where every page has a status and owner field is one. A blog with date and tags in frontmatter is one — it just doesn't know it yet. Sweet spot: up to roughly 10k files. Past that, reach for a real database. Below it, this gets you almost everything a database gives you, at a fraction of the complexity, with none of the lock-in. The full writeup — the complete tradeoff analysis, a worked example with actual queries, how to implement it in a weekend, and the tool ( MarkdownDB ) that does it for you — is here: wayofmarkdown.com/markdown-database

Standardized driver interface aims to let devices talk to AI and each other.

Grand Theft Auto VI is nigh. Here’s what the developer revealed about its highly anticipated game.

This week on “Uncanny Valley,” senior writer Will Knight talks his recent visit to China and the future of AI collaboration.

What is ZeroDroid? ZeroDroid is an open-source Android toolkit that exposes the radios, sensors and connected-device capabilities already present in a phone. GitHub: https://github.com/theabhishekchandra/ZeroDroid What problems does it address? The app contains 29 tools across five areas: Wireless: Wi-Fi, BLE, NFC, Bluetooth Classic and peer-to-peer connections RF and signals: IR, UWB, SDR-device detection and ultrasonic analysis Sensors: GPS/GNSS, QR analysis, device sensors and magnetic anomalies Network: USB inspection, cell-tower information and wardriving Security: tracker scanning, hidden-camera indicators, rogue-AP detection, network scanning and deauthentication indicators Architecture ZeroDroid uses Kotlin, Jetpack Compose, Material 3, MVVM, StateFlow, Hilt and Room. Services are lazy-loaded, and scanning begins only when the user starts a tool. Important limitations A smartphone cannot guarantee that it has found every camera, tracker, bug or network threat. Several detections are heuristic and may produce false positives or miss threats. Hardware availability also differs between Android devices. The project is intended only for education, defensive security and testing devices or networks you own or are authorized to assess. Feedback wanted I am looking for: Compatibility reports from different Android phones Feedback about permission handling False-positive reports Contributions, tests and documentation improvements Repository: https://github.com/theabhishekchandra/ZeroDroid

Claire McDonough is stepping down on October 30 to pursue a new opportunity, the company said in a filing on Thursday.