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Garden Irrigation System Summary MY project to make a bespoke irrigation system for my home garden, which comes in at under £10 per zone including the actual water delivery method, and is made with relatively easily sourced components. I am a mechanical engineer by training, but not an electrician so interested in hearing pointers on how to make it better. Some of the component and tool links below are AliExpress affiliate links. If you buy through them I earn a small commission at no extra cost to you. Everything listed is what I actually bought and used, or the closest equivalent I could find. This helps me fund some more ambitious but hopefully useful builds in the future. Intro So I have a vegetable patch and some flowers in the garden; it became a bit of a job during the hot days of summer to water the plants in the evening. I didn’t especially mind it but given my love of AI and tech, alongside recent experiments with Home Assistant, I thought there must be a 2026 version of this job. I tried a Wi-Fi-controlled tap, but quickly realised the flow rate was low - due to a small aperture size, and also scaling up with this type of solution to 6 + zones would quickly get expensive and leave me dependent on battery-powered solutions - also not a big win. So as I had begun experimenting with creating my own devices with dev boards etc, I figured, “how hard can it be” and in honesty it wasn’t, just took a bit of trial and error. This guide will be focused on how i would build it today, not all the steps that got me to here. My philosophy Standardised equipment/ components as much as possible Speed of delivery = speed of experimentation Modular where possible Anything can be achieved at any cost, but some of the fun is building something from very little Components Note all water pipes for this project are ½ inch and so connector etc are for that, this corresponds to a ¾ in threaded connector for attaching to pipes Standard UK Hose (½ inch) ¾ inch Threaded Tap Push Fit

The longtime far-right operator and troll, a UK citizen, is being held in ICE custody pending his removal from the United States.

I combed through the Labor Day sales to find the power tools and other essentials that cover nearly every household repair.

ESP32 Energy Metering with HLW8032, BL0942, and ESPHome ESP32 energy metering with HLW8032, BL0942, and ESPHome is not just about reading voltage, current, power, and energy. This article explains how to design the UART boundary, reporting cadence, calibration, entity model, and diagnostics as one stable data path. Many ESP32 energy metering projects start well. You connect an HLW8032 or BL0942 module, enable the matching ESPHome component, and Home Assistant quickly shows voltage, current, power, and energy. But reading values is not the same as building an energy metering node that can run reliably over time. The core conclusion is this: the hard part of ESP32 energy metering is not whether HLW8032 or BL0942 can be read. The hard part is designing the metering chip, UART, Wi-Fi behavior, ESPHome entities, calibration, and diagnostics as one stable data path. If the project focuses only on sensor YAML, it can later fail on transient loads, serial conflicts, unstable sampling, Wi-Fi reconnects, Home Assistant database growth, and calibration drift. In this article, an ESP32 energy metering node means an edge device where ESP32 reads voltage, current, power, and energy from a metering chip such as HLW8032 or BL0942, then exposes those values through ESPHome to Home Assistant or another upper-layer platform. It is suitable for device energy monitoring, trend observation, and low-risk operational diagnostics. It should not be treated as a billing-grade meter or an electrical protection device. If the goal is to monitor the energy behavior of one appliance, one small circuit, or one commercial device inside Home Assistant, ESP32 + ESPHome + HLW8032/BL0942 is a fast and low-cost path. If the goal is billing, electrical protection, high-accuracy compliance measurement, or safety interlocking, use certified meters, protection devices, or industrial acquisition hardware instead of stretching an ESPHome node beyond its boundary. 1. Why energy metering is more fragile than or

HelloFresh’s organic meal kit Green Chef offers transparent sourcing, layered cooking, and trustworthy gluten-free dishes.

With school and sports starting again, these discounts on Instant Pots, cookware, and coffee gear arrive right on time.

Wyze announced a new pan-and-tilt security camera today designed for indoor use. The entire Indoor Cam Pan can rotate 360 degrees, while the camera and lens on its front can tilt up and down 103 degrees. When you want privacy, the lens can tilt down until it completely disappears inside the Indoor Cam Pan's body […]

The best car phone holders keep your phone firmly in place while never getting in your line of sight. I took a dozen on road trips this summer to find the best.

Salmonella outbreaks are seemingly everywhere right now. A good temperature probe is the last and best line of defense.

With two cameras and built-in climate sensors, the Birdfy Nest Duo let me watch an entire nesting season unfold.

These WIRED-tested dual-purpose air purifiers also function as heaters, fans, art pieces, and more, offering the best of both worlds.

Voice and chat assistants for the home share a deceptively hard job: turning messy natural language into precise, structured commands. “Make it cozy in here” has to become a concrete intent plus the right slots — which device, which room, which value. Domux is an open model from iFlytek that focuses on exactly this problem: command understanding for smart-home assistants, framed as intent parsing and slot filling. What it is Task: smart-home command understanding — intent parsing + slot filling Base model: fine-tuned on google/gemma-4-E2B-it Modality: multimodal (image + text input) Target: edge / on-device deployment rather than large cloud models License: Gemma Why the compact base matters Building on the small Gemma-4-E2B base keeps Domux in a size class meant to run close to the device. For home assistants, that direction is attractive: keeping command understanding on-device can reduce round-trips and keep more interaction local, instead of routing every utterance to a large hosted model. Try it The model card is on Hugging Face (access is gated — you may need to log in and request access): 👉 https://huggingface.co/iFlytekOpenSource/Domux We're sharing open work like this because on-device, task-focused models are a practical piece of the foundation-model and serving story — not everything needs to be a giant cloud model.

Your desk setup needs a monitor arm, and I’ve been testing every one I can get my hands on to find the best.

The Nyrius Phoenix Home True 4K60 can wirelessly transmit games, TV shows, movies, and more across your home (even through walls).

After two years of testing, these are the electric toothbrushes that impressed WIRED staffers the most.

Comcast announced a new platform this week called Xfinity Shield that includes the option to let customers turn their routers into motion sensors. Many people reacted with fear and anger. "I am not enabling this. I am also seriously considering replacing my leased gateway with my own equipment, because a device I do not control […]

My two cats live a refreshingly low-tech lifestyle. They've never experienced a robot litter box, an automated feeder, or a GPS tracker. Noodle and Loaf live blissfully unaware of the technological trappings that surround the rest of my life. Except, that is, for the glowing neon sign that turns on whenever they go to the […]

The Poolease X1 can collect leaves from the pool floor, but dirt, silt, walls, and steps are all beyond its pay grade.

I put top-rated USB car chargers to the test for fast charging, value, heat, and safety. These are the best I found.

I Deliberately Destroyed My Kubernetes Cluster at 2 AM. Here's What Died First. Chaos engineering is not about breaking things. It's about discovering that your "production-grade" homelab is held together by hope and a single etcd snapshot before someone else finds out for you. The Setup I was lying in bed at 1:47 AM, staring at the ceiling, unable to sleep. Not because of caffeine. Because of a thought that had been gnawing at me for weeks: If one of my nodes died right now, would my cluster actually survive? I run a 4-node bare-metal Kubernetes cluster on Talos Linux. Dell OptiPlex control plane. Three Raspberry Pi workers. Cilium eBPF. ArgoCD. Longhorn distributed storage. Prometheus. Grafana. The whole cloud-native stack, shoehorned into $220 of scrap hardware and stubbornness. From the outside, it looks solid. ArgoCD syncs green. Cilium status shows healthy. Longhorn volumes are replicated across three nodes. I have etcd snapshots every 6 hours to S3. On paper, I'm resilient. But I had never actually tested it. Not a controlled test. Not a graceful node drain. I mean chaos . Sudden death. The kind of failure that happens at 3 AM when a power supply dies, or a kernel panics, or a neighbor's construction crew hits the wrong breaker. So I got out of bed, walked to my desk, and installed Chaos Mesh. Why Chaos Engineering on a Homelab? Professionally, I design AWS infrastructure with multi-AZ failover, auto-scaling groups, and managed services that abstract failure away. At Siemens, if an EKS node dies, the managed node group replaces it before I finish reading the alert. But my homelab has no managed control plane. No AWS SLA. No auto-repair. If a Pi's USB boot drive corrupts, that node is gone until I physically fix it. I needed to know: What dies first when a worker vanishes? Not "what should die" — what actually dies. Does Longhorn really failover? Three replicas sound great until you realize two of them were on the same node. Does Cilium handle network partitio