标签：#CLI

An old Oscar Mayer factory in Wisconsin will become America's latest fusion power research and development hub.

Build Firebase AI Logic with Antigravity CLI Note: Google Cloud credits are provided for this project. In this blog post, I demonstrate how to use the Antigravity CLI (an agentic AI assistant integrating directly with development workflows via skills and servers) to build an image analysis demo using Angular, the Firebase Hybrid & On-device Inference Web SDK, and Gemini models. Users upload an image and use a Gemini model to analyze it to generate a few alternative texts, tags, recommendations, and CSS tips to enhance the image quality. When the demo is running in Chrome 148+, the Hybrid & On-device SDK leverages the Prompt API of the on-device Gemini Nano model to perform the image-to-text tasks, and the token usage is 0. When other browsers, such as Safari or Firefox, execute the same tasks, the SDK falls back to Cloud AI (Gemini 3.5 Flash model), which consumes tokens. Next, I describe how to install the skills in my Angular project and register the Angular and Stitch MCP servers in the Antigravity CLI to develop the infrastructure, services, and UI design of my demo. 1. Workflow This is my entire workflow from implementing features, generating UI screens, and mapping the screens to Angular components. 2. Skills I installed the grill-with-docs , angular , and firebase skills in my project for the following reasons: grill-with-docs: Conduct a rigid Q&A session to generate a specification for a feature, refactor, or critical fix. AI is responsible for performing thorough analysis, and putting in more efforts to generate code to achieve the task. domain-modeling: The skill is referenced in the SKILL.md of the grill-with-docs skill, so a copy of it is required. code-review: Spawn two sub-agents to review changes to detect code smells and verify that the changes align with the specification. angular: Provide the best practices of modern Angular architecture, such as using signals and signal forms. firebase: Provide the skills for Firebase AI Logic, Firebase Remote, et

Cet article a été publié à l'origine sur WP Admin Lab , le journal du web technique en français. WP-CLI est l'interface en ligne de commande officielle de WordPress, un outil indispensable pour tout développeur ou administrateur gérant des sites WordPress en production. Contrairement à l'interface graphique du tableau de bord, WP-CLI permet d'exécuter des opérations en masse, d'automatiser des tâches répétitives et d'intervenir sur des sites inaccessibles via le navigateur. En 2026, maîtriser WP-CLI est devenu une compétence fondamentale pour la gestion professionnelle de parcs WordPress, que ce soit pour des agences gérant des dizaines de sites ou pour des développeurs travaillant sur des environnements de staging et de production. Installation et configuration de WP-CLI en 2026 L'installation de WP-CLI s'effectue en téléchargeant le fichier Phar officiel depuis le dépôt GitHub du projet et en le rendant exécutable à l'échelle du système. Sur les serveurs Linux mutualisés ou dédiés, la commande curl -O permet de récupérer le binaire, que l'on déplace ensuite vers /usr/local/bin/wp avec les droits d'exécution appropriés. Les hébergeurs comme Kinsta ou WP Engine proposent WP-CLI préinstallé dans leurs environnements SSH, facilitant la prise en main immédiate. La vérification de l'installation avec wp -info fournit les détails de version, l'interpréteur PHP utilisé et le chemin vers le fichier de configuration wp-config.php. La configuration avancée de WP-CLI passe par le fichier wp-cli.yml placé à la racine du projet WordPress. Ce fichier YAML permet de définir l'URL du site, le chemin d'installation, les alias de serveurs distants pour les déploiements SSH, et des paramètres par défaut pour certaines commandes. Les alias SSH dans wp-cli.yml sont particulièrement puissants : ils permettent d'exécuter des commandes WP-CLI sur un serveur distant exactement comme en local, avec une syntaxe du type wp @production plugin list. Cette fonctionnalité simplifie considérableme

Smalltalk has excellent live environments, but managing the different VMs, images, and installers for Pharo, Cuis, Squeak, Glamorous Toolkit, GNU Smalltalk, etc. is painful. I made st — a lightweight shell-based CLI that gives one consistent interface: st pharo install && st pharo run st gt install , st cuis install , etc. Basic package search/install and eval support where available Works on Linux/macOS/Windows (WSL) Repo: https://github.com/hernanmd/st Happy to answer questions, take feedback, or hear what other Smalltalk pain points you'd like addressed. Contributions welcome (especially adding new dialects)!

New York has become the first state to temporarily halt approval of large data centers, as Gov. Kathy Hochul argues the AI-driven building boom shouldn’t come at the expense of higher electricity costs, water supplies, or local control.

The error looks like a typo in the remote URL. Usually it isn't. On a machine with more than one GitHub account signed in, this message is GitHub's way of saying wrong identity, not wrong address. The symptom A repo clone that has worked for months suddenly can't fetch or pull. The remote URL hasn't changed. The repo hasn't been renamed or deleted; you can open it in the browser just fine. Yet the command line insists otherwise: $ git pull remote: Repository not found. fatal: repository 'https://github.com/<org> /<repo>.git/ ' not found Why GitHub's error is misleading For a private repository, GitHub won't confirm or deny that the repo exists to a caller who isn't authorized to see it. Confirming would leak information about private repos to anyone probing URLs. So instead of a clear 403 Forbidden , an unauthorized request gets treated the same as a repo that truly doesn't exist: a 404 , which git renders as Repository not found . "Repository not found" on a private repo almost always means the credential attached to this request can't see it. It's rarely a wrong URL. The usual cause: two accounts, one keychain This shows up most on machines used for both personal and organization-owned work: a personal GitHub account for side projects, and a separate account (or SSO identity) that actually holds access to the org's private repos. Credential helpers cache one token per host. If the cached token belongs to the personal account, every git operation silently authenticates as that account, including ones against the org repo it has no rights to. personal-account --(switch)--> org-account Active, no repo access Has repo access Diagnose it First, confirm the remote itself is fine. $ git remote -v If the URL opens in a browser while logged into the right account, the remote isn't the problem. Next, check which credential is actually cached. On macOS with the default helper: $ git credential-osxkeychain get <<< $'protocol=https \n host=github.com' username=personal-account

I Built an AI-Powered CLI That Migrates Legacy Java Code to Java 17/21/25 If you've spent any time in enterprise Java, you know the feeling. You open a service that's been running since 2014 and you're greeted by walls of anonymous inner classes, verbose null checks, Collections.unmodifiableList wrapping a new ArrayList , and switch statements with more break keywords than actual logic. Individually each pattern takes 30 seconds to fix. But across a codebase with 300 files, it's a week of mechanical work — and that's before you factor in the code review. So I built java-migrate : a CLI tool that scans your Java files, detects legacy patterns, and sends them to Claude with a precise system prompt to get them modernised. One command, instant diff, no surprises. What it looks like in practice Here's a typical legacy file before migration: public class LegacyService { // POJO with getters/setters public static class User { private String name ; private int age ; public String getName () { return name ; } public void setName ( String name ) { this . name = name ; } public int getAge () { return age ; } public void setAge ( int age ) { this . age = age ; } } public List < User > sortUsers ( List < User > users ) { // Anonymous Comparator users . sort ( new Comparator < User >() { @Override public int compare ( User a , User b ) { return a . getName (). compareTo ( b . getName ()); } }); return Collections . unmodifiableList ( users ); } public String describeObject ( Object obj ) { // instanceof + cast if ( obj instanceof String ) { String s = ( String ) obj ; return "String of length " + s . length (); } return "Unknown" ; } public String classify ( int value ) { // switch statement String result ; switch ( value ) { case 1 : result = "one" ; break ; case 2 : result = "two" ; break ; default : result = "other" ; } return result ; } } Run java-migrate LegacyService.java --dry-run --verbose and you get this diff: - public static class User { - private String name; - privat

A team lead announces that the team will start using AI-assisted development. Everyone nods. Nobody asks what that actually means on Monday morning. Some times ago I was in that position. A project I was working on needed to start using AI-assisted development, and the team was new to it. Nobody had rules written down for an agent to follow. Nobody had skills defined for it to load. There was no shared idea of how this should work inside our specific repo. Someone had to go first. That someone was me. The rules worked because I built them for one repo I spent time curating a set of rules and skills for that project. Not generic ones. I shaped them tightly around how that repo was actually structured, its conventions, its layout, the things a new engineer usually has to learn by asking around. I wanted an agent working inside that codebase to already know what a human teammate would have picked up in the first two weeks. I gave a demo. It landed well. Well enough that it got shared further across team, as something other teams could learn from. I gave the demo again. Same reaction. Then a few developers reached out for the actual rules and skills files. I said sure, and then I actually looked at what I would be handing them. The problem showed up the moment other people wanted in It was not copy-paste-able. The rules referenced folder names, module boundaries, and patterns specific to one repo. Handing them over as-is would have meant handing over advice that was wrong for their project, dressed up as a shortcut. So I told them to use it as a reference. Look at the structure, understand the reasoning, adapt it to your own repo. That is correct advice. I watched people nod at it and then quietly missing it. I was solving the wrong problem the whole time I had been thinking about this as a documentation problem. Write good rules, explain them well, let people copy the idea. What I actually had was a generation problem. The rules that worked were the ones rendered speci

General Fusion started trading on the Nasdaq following a reverse merger that saw high redemptions.

TL;DR — This article is going to be jam-packed with useful information, tips, tricks and hacks for setting up an agentic development in the Google ecosystem. This one isn’t really about the FinOps! Welcome to Part 2 Welcome back, friends! In the first part , I described the purpose of the FinSavant FinOps solution, the motivation for creating it, its overall architecture and tech stack, and how it works. In this part, we’ll use FinSavant as a case study in how to set up a development environment for the purposes of building such an ADK-based agentic solution. Even if you’re not particularly interested in FinSavant itself, I hope you’ll find a bunch of useful information and tips here that will help you build your own agentic solutions more effectively and quickly. We’ll cover: Using Antigravity IDE Overall project workspace structure Setting up agent skills for your coding agent My project’s GEMINI.md (or if you prefer, AGENTS.md ) My documentation approach Setting up MCP servers for your coding agent, such as BigQuery MCP Scaffolding the initial ADK agent using Google Agents CLI and its supporting skill Getting started with a Makefile Sound good? Let’s get cracking! Series Orientation Let’s see where we are in this series. Goals, Architecture, and Tech Stack: Capabilities, project goals, target architecture, technology stack, and design decisions. Development Environment Setup, Google Antigravity, MCPs and Skills, and ADK Bootstrapping with Agents CLI 📍 You are here. Building the ADK Agent and API Designing and Building the UI with Google Stitch and A2UI Deployment with Gemini Enterprise Agent Platform, Agent Runtime, Cloud Run and IAP Automating Deployment with CI/CD and Terraform Agent Observability, Evaluation, and Tuning with Gemini Enterprise Agent Platform Getting Started with Antigravity IDE These days, my favourite coding environment for any significant project is Antigravity IDE. This is Google’s agent-first integrated development environment. You get a lo

Responsibly dispose of your food scraps with one of these indoor, (mostly) odor-free, WIRED-tested devices.

As the climate phenomenon sends warm water surging across the eastern Pacific, some parts of the fishing industry are suffering—but other regions are seeing a windfall.

Data centers are driving up the company’s use of electricity—and carbon pollution.

I run ezyZip , a browser-based archive tool, so "which format should I use?" is a question I field constantly. The honest answer is usually "it depends," which satisfies nobody. So I stopped hand-waving and measured it. We benchmarked 42 archive and compression formats, spanning four decades, from 1984's Unix compress through today's Zstandard, Brotli, and context-mixing paq8px. Everything ran against the same realistic 55 MB corpus, every archive was round-trip verified byte for byte, and the whole thing reproduces from a single command. Here's what came out of it, and what I'd actually reach for. The setup Most compression benchmarks measure raw codecs on standardized corpora like Silesia. That's the right call for algorithm research and the wrong call for answering "what should I zip my folder with?" I wanted end-user formats, real CLI tools, container overhead and all, on data that looks like an actual folder. So the corpus is deliberately mixed: about 11 MB of text, 15 MB of office documents, 16 MB of images, and 13 MB of video, all public domain so it can be committed and redistributed. That mix matters. Office documents ( .docx , .xlsx , .pptx ) are themselves ZIP containers, so they stress how a tool handles already-compressed data. The JPEG and H.264 media is near-incompressible and sets an honest lower bound. The plain text and uncompressed images are where formats actually separate. Two rules kept it fair and practical: Only two levels per tool: its default, and its one "maximum compression" dial. No method tuning, no dictionary sizes, no thread-count games. That's what a normal person can reach. Everything is round-trip verified. Each archive gets extracted, and every file is hashed with SHA-256 against the original manifest. Exit codes are not trusted. That last rule earned its keep immediately. The verification gotcha On the image category, a 1985-era ARC build produced an archive that its own extractor happily unpacked, while printing a CRC warning an

When a CLI becomes useful, someone eventually tries to automate it. That is where a progress bar can quietly become a problem. For a person, this kind of output is helpful: Translating markdown files 12/40 30% docs/intro.md It tells me that the command is alive, how far it has moved, and which file it is working on. But when another system starts reading that same output, the progress bar stops being only a user interface. It becomes an accidental contract. That was the real problem behind one of the changes in Co-op Translator v0.20.0. The release added a Rich-powered CLI progress UI, but it also added structured translation events. At first, those may look like two separate improvements. They are really two surfaces for the same state: Rich output gives a person something readable, while structured events give integrations something stable. This article is about three things: First, why console output is tempting to parse. Second, how Co-op Translator separated the Rich UI from the event stream. Third, why that separation matters for CLI, Python API, MCP, and product integrations such as Localizeflow. The problem appears when logs become state Console output is written for people. When I run a translation command, I want a quick answer to a few practical questions: Is the command still running? Which stage is active? Which file is being processed? How much work is left? Did anything fail? A progress bar is good for that. It compresses the state of the run into something I can scan quickly. But a product integration needs a different kind of information. Imagine Localizeflow running Co-op Translator as part of a larger workflow. It does not only need to know that text was printed. It needs durable state: Which translation job started Which target language is active Which stage is running Which file completed Which file failed How many items are done Whether the run succeeded If all of that only exists inside console text, the integration has to parse human language

It's too hot. There, we said it. Protect your health and keep your home cool with one of these top-rated air conditioners.

As extreme heat becomes the norm on the continent, the AC culture wars may be solved by advances in environmentally friendly technology.

I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power. Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can…

As worries about a growing super El Niño mount, new findings suggest a radical solution to mitigating its impacts.

Go from an empty repository to a live custom domain with HTTPS in about 14 minutes, without manually editing a single DNS record. The post How GitHub Copilot enables zero DNS configuration for GitHub Pages appeared first on The GitHub Blog .