标签：#iot

I knew I’d officially become a ‘longevity influencer’ this month when a company called Generation Lab reached out to offer me the chance to write about—and even receive—their new rejuvenation treatment, an injectable combination of two existing drugs which they call 1 Generation. This wasn’t just any antiaging treatment, either. A company fact sheet says that…

Hoomanely has developed a smart bowl to measure and record dogs' feeding data, then tells owners if behaviors change.

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

A fixed-schedule solar baseline drifts out of sync with the sun throughout the year. In Phoenix the sun is up for 13 hours 10 minutes in late August and 10 hours 2 minutes at the December solstice. A flat daily kWh target flags that entire winter as a fault, then stays quiet on the July afternoon when one string dies at 2pm under full sun. The fix is to anchor the baseline to the actual sun instead of the clock, and most of what you need for that does not require an irradiance forecast. One thing before any code: sun geometry tells you when a system should be producing and when it should peak. It does not tell you how much light actually reached the panels. That is irradiance, and cloud cover swamps it. If you want modeled output in kWh, reach for Forecast.Solar or Solcast, which fold in weather and your array's tilt and azimuth. What follows is the free, dependency-light layer underneath that: the daylight window, the solar-noon peak, and the day-length trend. TL;DR Sun geometry (sunrise, sunset, solar noon, day length) catches a specific class of solar underperformance with no irradiance data. Gate alerts to the real daylight window so your monitor stops crying "underperformance" before sunrise. Track the daily production peak relative to solar noon. A persistent shift across comparable days can reveal shading, orientation, or system changes that a total-kWh check misses. Normalize a flat kWh target by day length so winter stops tripping false alarms. First-order fix, not a physics model. One call to an astronomy endpoint returns all of it. Code below in curl, Python, and Node. For real production forecasting, use an irradiance API. Sun times are the sanity layer, not the forecaster. Sun times will not predict your kWh, but they eliminate common timing-based false alarms and can surface useful production-shape anomalies early. Pull sunrise, sunset, solar noon, and day length once a day, gate your alerts to daylight, watch the peak, and scale the target for season.

The company won't say if medical devices are affected or if any customer data was exfiltrated.

Counting the creatures in the world around us is critical for a raft of conservation efforts. It helps scientists gauge biodiversity, track migration, and spot invasive species. That census-taking, though, often requires humans to tabulate what they see, trap, or otherwise sense—a potentially laborious, costly process that can still leave gaps. But developments over the…

ESP32 + Python: From Microcontroller to IoT Artcal 0: Introduction When it comes to transferring data from one place to another, things can sometimes become tricky, especially when communication happens between the hardware and software levels. In this article series, I would love to share the experience and knowledge I’ve gathered while working with ESP32 and Python. We’ll explore how these two technologies can work together, starting from the basics and gradually moving towards more interesting and practical projects. If you have any questions, suggestions, or ideas along the way, feel free to share them in the comments section below. I’d love to hear from you and discuss them with the community. So, without further ado, let’s begin! 🚀 What is ESP32? Think about Esp32 as a microcontroller with Internet facilities, consisting WiFi, Bluetooth and a own wireless data transfer protocol called ESP-NOW between ESP32 chips. Nowdays, the developers have made development boards integrading these chips for the easy use. ESP32 is a family of microcontrollers developed by Espresiff. This can read sensor inputs, process data, contol devices and specially connect to the internet. This is like Arduino but better, faster and smaller. With these information that we have, we can speak about this board as, "A powerful microcontroller that can interact with electronic components and communicate with other devices through Wi-Fi, Bluetooth, and other communication methods." Python??? We use different languages to tell the same thing but in different ways. We use programming languages to tell the computer the same thing but in different approches. Some languages can be hard to learn and some are easy. Some are well developed and some are not. Python programming language was created back in 1980s by Guido Van Rossum, with the development begining around 1989. It was publicly released in Feb, 1991. 🐍 1989 — Guido van Rossum developing Python. 🐍 1991 — The first public release. 🐍 2000 — Py

Michael Polansky — better known publicly as Lady Gaga's partner and a former top deputy to Sean Parker — has quietly spent years building an AI-driven startup that keeps living human skin tissue alive for weeks outside the body to discover new skincare compounds, and is only now going public about it.

Architecture is not a fixed choice made once; fitness is a moving target driven by changing regulations, tech, and markets. Even a sound design can silently stop fitting over time without bad calls. Spanning seven articles on context stores, gateways, and topologies, this collection treats architecture as an evolving sociotechnical craft where teams deliberately shape friction, fitness, and flow. By InfoQ

When the biotech company Insilico Medicine used its computer models to propose a promising drug for pulmonary fibrosis, it enthusiastically claimed in a press release that the molecule had been “discovered by” its generative AI platform. Insilico leads a pack of companies using AI to rapidly come up with drug ideas humans might never think…

An effort to transform the world of sports through steroid use doesn't exactly seem to be bearing financial fruit.

It's the data, stupid.

The scanning and bloodwork health startup founded by Spotify's founder will officially launch in New York in about a month.

Controller Area Network (CAN) is one of those technologies that quietly powers a huge number of embedded systems. It is commonly found in cars, EVs, industrial controllers, robotics, and other distributed systems where multiple devices need to exchange data reliably over a shared bus. For development and debugging, it is useful to connect that CAN network to a computer. The problem is that a standard computer communicates through USB, while CAN uses a dedicated differential bus. A USB-to-CAN interface solves this problem by translating between the two. In this project, we will explore the DSD TECH SH-C30L USB-to-CAN adapter , learn how its different firmware modes work, connect it to a PC, and then use an Arduino UNO with an MCP2515 CAN module to create a simple bidirectional CAN communication setup. The goal is not just to make the hardware work, but also to understand what happens between the Arduino, CAN bus, USB adapter, and computer. SH-C30L USB-to-CAN Adapter Overview The SH-C30L is a compact USB-to-CAN interface designed to connect a computer directly to a CAN network. It is based on an STM32F072C8T6 microcontroller, which contains an integrated CAN controller. This allows the adapter to handle CAN protocol processing without requiring a separate external CAN controller. The microcontroller communicates with the computer through USB, while a dedicated CAN transceiver handles the physical CAN interface. The transceiver converts the controller's logic-level signals into the differential CAN_H and CAN_L signals used on a CAN network. One of the interesting aspects of the SH-C30L is its firmware flexibility. The adapter can work with Candlelight firmware , which allows it to operate with Linux SocketCAN and compatible CAN applications, or with SLCAN firmware , where it behaves more like a serial CAN interface. This makes the same hardware useful with different operating systems and software environments. The adapter supports both CAN 2.0A and CAN 2.0B frames, wit

Most of the industrial AI you have seen is a retrieval pipeline with a chat box on it. Chunk the manuals, embed them, stuff the top matches into a context window, let the model talk. It demos well. It falls apart the first time somebody asks a question where the answer depends on what a machine is doing right now. We are an applied research lab called Nodeblue, and the system we build is called Nexus. This is a writeup of the architectural decision at the center of it, which is that the language model is the smallest and least interesting part. The failure that set the design Here is the test that made the decision for us. Industrial control programs live in two places. There is the project archive, which is the file in source control, and there is the program actually running in the processor. Those drift, constantly, because engineers go online to fix a timer during a downtime event and do not always upload the change back. Anyone who has worked on a plant floor knows this. It is Tuesday. We took a real production controller with exactly that situation, a live edit present in the processor and absent from the archive, and asked eleven frontier models which version of the routine was executing. We gave them the export, the docs, the context, everything a careful human would get. All eleven answered confidently. All eleven were wrong. Not garbled, not obviously broken. They read the export correctly, described the rung correctly, and then told us the archived version was running, because the archived version was the only version they had ever seen. Then we put the same eleven models on top of our engine and asked again. All eleven got it right, cited to the rung. The models did not improve. They got access to a fact that lives in a processor rather than in a corpus. That is the entire lesson, and it generalizes past our domain: when a model is asked something it structurally cannot know, it does not abstain, it produces the most probable sentence. In a domain where

A couple of weeks ago I dropped the CRA text (the EU's cybersecurity regulation for IoT devices) into ChatGPT and asked when the main requirements actually kick in. The answer was confident and wrong - it mixed up the date the regulation entered into force (2024) with the date the requirements actually apply (2027). Three years off, stated like an obvious fact. My team (Platanor, embedded security for IoT) has been building an internal reference on CRA/RED/NIS2/CSA for a few months now, and this is exactly the kind of mix-up we kept running into whenever we just threw the regulation PDF at a model. The problem isn't the model. It's how the source is laid out: dates are scattered across different articles with no explicit link between them, token-based chunking cuts sentences off mid-article, and the model has no way to tell how fresh the text is. When we rebuilt the base as a public repository, we fixed this with file structure, not prompting. Cut by article headings, not by tokens: ### Article 13 Obligations of manufacturers 1. When placing a product... ### Article 14 Reporting obligations... ### Article N is a natural boundary. Each chunk stays whole - the article never gets split mid-sentence. Source priority, written into the file itself, not the prompt: primary source > official related documents > third-party summaries > our own analysis. The model sees this right next to the content, not as an instruction that's easy to lose in a long chat. A verification date on every file: > Last verified: 2026-08-10. > Annex I application deadline: 11 December 2027 (not to be confused with the entry-into-force date - 10 December 2024). That one line is what removed the exact error I opened with. llms.txt at the repo root - an index of every file, so an agent can pick what to load instead of reading the whole repository. The same questions now get answered correctly - not because the model got smarter, but because the source stopped being one continuous wall of text. We pac

This week I spoke to scientists who have found a way to turn male mouse embryos female. They’ve developed a CRISPR-based approach to essentially cut out the Y chromosome. It allowed them to create female clones of male mice. That’s right: female animals that are genetically identical to males, except for the missing Y chromosome.…

In 2017, Deanne Taylor attended a presentation at the University of Pennsylvania, just a short walk from her office. A researcher was there to unveil the Human Cell Atlas, an ambitious project that aimed to map every cell in the human body. Taylor was floored, and then concerned. As details emerged, she discovered that the…

The plaintiffs say they invested nearly $1.2 million in the company, and are accusing Gomez of failing to build and market the startup.

Scientists have deliberately turned male mouse embryos into females for the first time. A team based in Japan used a CRISPR-based approach to remove the Y chromosome from male cells and create female clones of male mice. “No one has done this before,” says Monika Ward, a reproductive biologist at the University of Hawaii, who…