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There are three times of year that are best for buying a new TV: leading up to the Super Bowl, Black Friday, and of course now, during Amazon Prime Day. Many of the new 2026 models have been released, and while some will be seeing discounts, the majority of the best deals are going to […]

The app would be independent of Meta's other social media offerings, although sources told the NYT that those social sites could direct users to engagement with the app.

While there are several thousand products discounted for Prime Day 2026, most of them aren’t what we’d call winners. We’ve already gathered the best Prime Day deals in our main roundup, but we’re dedicating this page to discounts on Verge-favorite items to help you find useful gear, whether it’s tech-adjacent or not. We’re covering Prime […]

If gaming hardware prices have you looking for less expensive alternatives, Amazon has the Windows version of the Lenovo Legion Go S on sale for $549.99 (typically around $700) for Prime Day, matching a previous low we spotted at Woot a month or so ago. What it lacks in raw performance, it makes up for […]

We at The Verge love a good gadget or tchotchke, especially when it’s not too expensive. But with the rising cost of just about everything these days, many once-cheap gadgets aren’t so affordable anymore. Amazon and other retailers sell all sorts of products from word salad brand names, but only some of it is handy […]

If you’re in the market for a robot vacuum, Amazon Prime Day has brought a ton of discounts that we’re tracking, but the Eufy Omni C28 is one of the best deals we’ve spotted so far. It’s a great robot vacuum and mop hybrid that offers a number of flagship features without the flagship price. […]

Algae blooms, peeling paint, and a host of fixes from hydrogen peroxide to nanobubblers have made it hard to diagnose what's wrong with the Reflecting Pool, let alone how to clean up the mess.

A newly identified species of fungus attacks the famous “zombie mushrooms” that control ants.

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Zabbix is an open-source monitoring platform that tracks the health and performance of servers, networks, applications, and services, with built-in alerting and visualisation. This guide deploys the Zabbix server, web UI, agent, and MySQL database using Docker Compose, with Traefik handling automatic HTTPS for the dashboard. By the end, you'll have Zabbix monitoring its own host with a secured dashboard at your domain. Set Up the Project Directory 1. Create the project directory: $ mkdir ~/zabbix-docker $ cd ~/zabbix-docker 2. Create the environment file: $ nano .env DOMAIN = zabbix.example.com LETSENCRYPT_EMAIL = admin@example.com MYSQL_PASSWORD = YOUR_DB_PASSWORD MYSQL_ROOT_PASSWORD = YOUR_ROOT_PASSWORD Deploy with Docker Compose 1. Add your user to the Docker group: $ sudo usermod -aG docker $USER $ newgrp docker 2. Create the Compose manifest: $ nano docker-compose.yaml services : traefik : image : traefik:v3.6 container_name : traefik restart : unless-stopped command : - " --providers.docker=true" - " --providers.docker.exposedbydefault=false" - " --entrypoints.web.address=:80" - " --entrypoints.websecure.address=:443" - " --entrypoints.web.http.redirections.entrypoint.to=websecure" - " --entrypoints.web.http.redirections.entrypoint.scheme=https" - " --certificatesresolvers.le.acme.httpchallenge=true" - " --certificatesresolvers.le.acme.httpchallenge.entrypoint=web" - " --certificatesresolvers.le.acme.email=${LETSENCRYPT_EMAIL}" - " --certificatesresolvers.le.acme.storage=/letsencrypt/acme.json" ports : - " 80:80" - " 443:443" volumes : - /var/run/docker.sock:/var/run/docker.sock:ro - ./letsencrypt:/letsencrypt networks : - zabbix-net mysql-server : image : mysql:8.4.8 container_name : zabbix-mysql environment : MYSQL_DATABASE : zabbix MYSQL_USER : zabbix MYSQL_PASSWORD : ${MYSQL_PASSWORD} MYSQL_ROOT_PASSWORD : ${MYSQL_ROOT_PASSWORD} volumes : - ./mysql-data:/var/lib/mysql networks : - zabbix-net restart : unless-stopped zabbix-server : image : zabbix/zabbix-se

MLflow is an open-source platform for managing the machine learning lifecycle — experiment tracking, model registry, and reproducible runs. This guide deploys MLflow using Docker Compose with a PostgreSQL backend, S3-compatible artifact storage, basic-auth, and Traefik handling automatic HTTPS, then logs a sample scikit-learn run. By the end, you'll have MLflow recording experiments at your domain over HTTPS. Prerequisite: An S3-compatible bucket (e.g. Vultr Object Storage) with access key, secret key, region, and endpoint URL. Set Up the Directory Structure 1. Create the project directory: $ mkdir -p ~/mlflow $ cd ~/mlflow 2. Create the environment file: $ nano .env DOMAIN = mlflow.example.com LETSENCRYPT_EMAIL = admin@example.com POSTGRES_USER = mlflow POSTGRES_PASSWORD = StrongDatabasePassword123 MLFLOW_AUTH_CONFIG_PATH = /app/basic_auth.ini MLFLOW_FLASK_SERVER_SECRET_KEY = GENERATED_SECRET_KEY S3_BUCKET = mlflow-artifacts S3_ACCESS_KEY = YOUR_ACCESS_KEY S3_SECRET_KEY = YOUR_SECRET_KEY S3_REGION = YOUR_REGION S3_ENDPOINT = https://YOUR_OBJECT_STORAGE_ENDPOINT 3. Create the basic-auth configuration: $ nano basic_auth.ini [mlflow] default_permission = READ database_uri = sqlite:///basic_auth.db admin_username = admin admin_password = ADMIN_PASSWORD authorization_function = mlflow.server.auth:authenticate_request_basic_auth 4. Create a Dockerfile that adds the auth-server extras and Postgres/S3 clients to the official image: $ nano Dockerfile FROM ghcr.io/mlflow/mlflow:v3.10.1 RUN pip install --no-cache-dir psycopg2-binary boto3 'mlflow[auth]' Deploy with Docker Compose 1. Create the Compose manifest: $ nano docker-compose.yml services : traefik : image : traefik:v3.6 container_name : traefik command : - " --providers.docker=true" - " --providers.docker.exposedbydefault=false" - " --entrypoints.web.address=:80" - " --entrypoints.websecure.address=:443" - " --entrypoints.web.http.redirections.entrypoint.to=websecure" - " --entrypoints.web.http.redirections.entrypoint

There's a moment in every ambitious AI engineering project where you convince yourself that more agents means more power. I hit that moment early in building my Python orchestration framework — and I spent several painful weeks learning exactly why that intuition is wrong. The seductive pitch: decompose complex tasks into specialized sub-agents, run them in parallel, let them coordinate. What actually happened was a reliability nightmare that taught me more about agentic architecture than any framework documentation ever could. The Problem I Actually Built My Python orchestration system was designed to automate complex, multi-step workflows — the kind that require planning, research, code generation, and validation to happen in a coherent sequence. Early on, I structured it as a web of parallel agents: a planner, several workers, a validator, and a synthesizer, all exchanging structured messages. On paper it was elegant. In practice, it had three failure modes I couldn't engineer away: Context drift. Each agent only saw the slice of information it was handed. The worker writing one module couldn't see what the worker writing another module had decided. By the time the synthesizer tried to combine outputs, I had conflicting assumptions baked into the results — variable names that clashed, patterns that contradicted each other, interfaces that didn't align. Cascading partial failures. When one agent produced ambiguous output, every downstream agent amplified the ambiguity. A planner that returned a slightly underspecified task description produced workers that each interpreted it differently. Nothing failed loudly. Everything just drifted, quietly, until the final output was incoherent. Debugging opacity. When something went wrong in a parallel multi-agent system, tracing the failure was miserable. Was it the planner? One of the workers? The message-passing layer? I'd rebuilt the worst parts of distributed systems debugging inside a single Python process. The Architec

The Fellow Series 1 espresso machine has its first sale, and it's a whopper. Other Fellow devices are 20 percent off, too.

Meta CEO Mark Zuckerberg has tasked a small team at the company with making an app that works like the prediction markets Polymarket and Kalshi, according to The New York Times. The app, internally called "Arena" won't let users wager real money, but instead will "probably rely" on allowing users to bet with points, the […]

I can’t think of a tech accessory I actively use more than headphones. Whether it’s for music, calls, or just to block out ambient noise so I can focus, I always have a pair close at hand. One of the best times to get some of your own with some big savings attached is during […]

Current amphibian development may not have been typical of early land vertebrates.

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Mark Zuckerberg has reportedly directed Meta to build its own prediction market.

Netflix's new horror game uses your smartphone to control stars Zoë Kravitz's hands.