标签：#iot

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…

Startups and Big Ag are using Crispr gene editing to create crops that taste better and grow on a hotter planet. But will they find a market?

Mini human brains are being grown in labs all over the world. Soon, they could outthink neural networks.

How We Built an IoT Platform That Handles 30 Million Concurrent Connections — With a Team of 10 DGIOT is an open-source industrial IoT platform. We run 928 gateways across 16 oil fields, process 652 million data points, and maintain 99.9999% uptime. Here's the architecture that makes it possible. The Problem In 2021, we got a call from Daqing Oil Field — China's largest oil producer. They had a problem: 928 industrial gateways from different vendors 114,809 sensor points speaking 15 different protocols Data collection every 10 minutes (they needed seconds) 15-30 minute end-to-end latency (they needed <3 seconds) False alarm rate above 20% The existing system was a patchwork of vendor-specific tools, each with its own database, UI, and authentication. Operators had to log into 8 different systems just to check if a pump was overheating. They asked: "Can you unify this?" What We Built DGIOT is an Erlang/OTP-based platform that acts as a universal translator for industrial protocols. Think of it as a Rosetta Stone for machines. Modbus ─┐ OPC UA ─┤ MQTT ──┼──→ Unified Pipeline ──→ TDengine ──→ Dashboard IEC104 ─┤ A11 ──┘ The key insight: industrial protocols are just state machines . Once you model each protocol as a gen_statem FSM in Erlang, you can handle hundreds of them concurrently with almost zero overhead. The Architecture: DLAS We designed a four-layer architecture that separates concerns cleanly: Layer 1: DATA — Ingestion Parse Server (23 classes) handles device metadata, user auth, tenant isolation TDengine stores 652M time-series data points with 10:1 compression EMQX handles MQTT message routing at 1M+ msg/sec Mnesia/ETS provides in-memory caching for hot data Layer 2: LOGIC — Ontology Engine This is our secret weapon. We built a 252-entity OWL ontology that models industrial equipment: Pump ⊑ Equipment ⊓ ∃ hasPart.Bearing ⊓ ∃ measures.Pressure Bearing ⊑ Component ⊓ ∃ hasFailureMode.Overheat Overheat → triggers ( Alert ) ∧ reduces ( RemainingLife , 0.8 ) The

The use of AI systems to create viruses opens up new possibilities for combating bacterial resistance. It also raises concerns about the pace at which technology is outstripping regulation.

Consumindo REST APIs no ESP32 sem Estourar a Memória: Conheça o ESP32-HTTP-Client Se você já desenvolveu projetos IoT no ESP32 que se comunicam com APIs REST (seja para enviar dados de sensores para a nuvem, consultar status de serviços ou integrar com Firebase e AWS), provavelmente já enfrentou um destes problemas clássicos: Fragmentação e estouro de heap: O combo padrão HTTPClient + ArduinoJson precisa carregar todo o payload HTTP na RAM como String antes de desserializar o JSON. Em payloads médios ou grandes, isso gera Out of Memory ou travamentos intermitentes. Lentidão em requisições consecutivas: O HTTPClient padrão refaz o handshake TLS/TCP repetidamente, adicionando centenas de milissegundos a cada chamada. Código verboso e boilerplate excessivo: Mais de 15 a 20 linhas de código para instanciar clientes, extrair buffers, checar erros e navegar em nós JSON. Para resolver esses gargalos de forma elegante e moderna, foi criada a biblioteca ESP32-HTTP-Client . O que é o ESP32-HTTP-Client? O ESP32-HTTP-Client é um cliente HTTP/REST moderno, fluente e orientado a objetos para ESP32, projetado especificamente para sistemas embarcados de alta eficiência. Em vez de "fazer download da resposta, guardar na memória e depois processar", ele utiliza Direct Memory Binding (injeção direta) e Stream Parsing : os dados do JSON são lidos diretamente do stream da rede e injetados direto nas suas variáveis ou struct s em C++, sem armazenar o payload inteiro na RAM . // Uma linha. Zero strings intermediárias. Injeção direta em memória. client . get ( "/sensor" ). getBody ( "temperature" , & myFloatVariable ); Benchmark: ESP32-HTTP-Client vs Abordagem Tradicional Em testes controlados com 100 requisições HTTP consecutivas contendo payloads JSON (usando o endpoint /users do JSONPlaceholder), os resultados comprovam a economia de recursos: Métrica / Recurso HTTPClient + ArduinoJson (Padrão) ESP32-HTTP-Client Diferencial Heap alocado por requisição ~58.2 KB ~0.0 KB (15 bytes) ~99.9%

What started as a vision for mapping urban gardens has evolved into something much bigger. Over the past weeks, we've built a complete decentralized ecosystem that connects IoT sensors, robots, and communities using Monero (XMR) and MYZ tokens. The Journey: From Gardens to Streets It all began with a simple idea: create a map for urban gardens. But we quickly realized that a map alone wasn't enough. We needed a full system that could: Monitor soil health in real-time Automate irrigation and analysis Enable private, decentralized payments Connect communities and institutions Here's what we built. 🗺️ The Urban Garden Map Using Leaflet.js and a REST API, we created an interactive map where anyone can register their urban garden. The map supports: Geolocation with /nearby endpoint Full CRUD operations for gardens Search by name and city Check it out: Live Demo 📡 Arduino Sensors for Smart Agriculture We integrated Arduino sensors to monitor soil conditions in real-time: pH (0-14 scale) EC (Electrical Conductivity) Temperature and Humidity The data flows through Node.js APIs and is stored in MongoDB, making it accessible for analysis and reporting. 🦾 The Robot Ecosystem We built a family of software robots that can receive payments automatically in MYZ and XMR: 1. AgricoloBot - The Garden Assistant Monitors soil health Generates automatic reports Provides recommendations for farmers 2. Robot Arm - The Physical Gardener 4 DOF (Degrees of Freedom) Controlled via WebSocket Can water, plant, analyze, and harvest 3. CleanStreetBot - Street Cleaning Robot Reports waste with geolocation Automates zone cleaning Generates reports for municipalities 4. RecicloBot, PuliziaBot, CompostBot - Recycling and Waste Management Monitor containers and optimize collection routes Track composting and organic waste management 💰 Decentralized Payments with Monero and MYZ All robots receive automatic payments through an escrow system: 85% → Robot owner 2% → MyZubster platform 8% → Bosco Community

* *The purpose of this light version threat model is to demonstrate how STRIDE can be applied to an ECG device. It is intended for readers learning system decomposition and threat modelling techniques. The example includes a simplified set of components, threats, and mitigations for educational purposes and is not intended to represent a comprehensive medical device cybersecurity assessment or any regulatory submission. **Assumption: This example models a typical ECG device, which may include network connectivity in a clinical environment. Trust Boundaries: Trust boundaries exist between the ECG device, hospital network, and external clinical systems. System Definition: ECG is the abbreviation for an Electrocardiogram. It is used to detect electrical activity of the heartbeat in the form of P wave, QRS complex and T wave to identify and diagnose irregularities in heartbeat. Electrodes are placed on patient’s limbs and chest to measure the electrical potentials. It translates tiny electrical signals into digital wave patterns. These waveforms are used by the doctors to evaluate the heart rhythm and check for cardiac damage. Components • Electrodes • Lead wires • Amplifier and filters • Analogue-to-Digital Converter (ADC) • Main processing unit • Display/printer • Local storage • Network interface (Ethernet/Wi-Fi/Bluetooth), if supported. Data Flow Diagram: Electrodes → Lead wires → Amplifier and filters → Analogue-to-Digital Converter (ADC) → Main processing unit → Display / Printer / Local storage / Network interface (if supported) |TRUST BOUNDARY|→ Electronic Health Record (EHR) / Clinical Information System 2. STRIDE Threats: Threats Description Spoofing in general ** - Spoofing is the act of impersonating a legitimate user, device, or system to gain unauthorized access to resources or services. Violates authentication. * Spoofing in ECG * - An attacker may impersonate an authorized clinician, connected medical device, or trusted clinical system to gain unauthoriz

A startup has created beagles without the gene that causes runny noses and watery eyes for allergy sufferers.

I didn't put Shelly Plus 1PM and 2PM behind my wall switches for the app or the cloud. I put them there because each one quietly does two jobs at once: it switches a circuit locally, and it measures exactly what that circuit draws. The moment I had them wired in, every light group and every roller shutter in the flat became both controllable AND measurable in Home Assistant — with no separate energy meter and, deliberately, no cloud in the loop. This is the part most Shelly write-ups skip: the metering isn't a bonus, it's the reason to use them. This is how I set them up, kept them fully local, wired the physical switches so they still work like switches, and pulled per-channel energy into the HA Energy Dashboard. Why the Plus series for in-wall use The Plus 1PM and 2PM are Gen2 devices. That matters: they speak a local RPC API over HTTP and WebSocket, they're supported by the native Home Assistant Shelly integration out of the box, and nothing about them needs the internet once they're on your Wi-Fi. The 1PM is a single relay with power metering — I use it behind light circuits. The 2PM has two relays with metering and a dedicated cover/roller-shutter mode — that's what sits behind my shutters. The catch you plan for up front: both need a neutral wire in the switch box. Older installations often don't have one at the switch, so check before you order. If the neutral is there, the rest is easy. The first thing I change: kill the cloud, pin the IP Straight out of the box I do two things in the Shelly web UI (just browse to the device's IP). First, I disable Bluetooth and Cloud under Settings — I don't want the device reaching out to anything, and Home Assistant talks to it directly on the LAN. Second, I give it a fixed address via a DHCP reservation on my router, so the native integration never loses it after a reboot. That's the whole hardening story. No firmware flashing, no third-party firmware — stock Shelly firmware with the cloud switched off is already a fully

I used to laugh at people who panicked over data limits. Then my carrier switched my "unlimited" plan...

Most conversations about CRA, DORA, and NIS2 compliance for IoT hardware boil down to one uncomfortable binary: redesign the board around newer, security-capable silicon, or accept that your existing product line falls out of compliance on a fixed deadline. For a product with years left in its lifecycle and a BOM that took months to qualify, "just redesign it" is rarely a real answer. There's a third option that gets far less attention than it deserves: pair the legacy chip with a modern security co-chip that absorbs the cryptographic boundary, while the legacy part keeps doing exactly what it already does well - application logic, peripherals, display, sensor polling. Call it a soft fade-out. The old silicon stays in service until its natural end-of-life; the compliance gap gets closed by a second, much cheaper part sitting next to it, not by replacing it. The Three Gaps a Legacy Chip Has - and Why a Co-Chip Fixes Them The regulatory pressure driving all of this isn't abstract. NIST finalized its post-quantum cryptography standards in 2024, and IR 8547 sets real dates: ECDSA and RSA are deprecated after 2030, disallowed after 2035. Germany's BSI has gone further - TR-02102-1 (2026 edition) sets a stricter 2030 deadline for high-protection-need data, and treats the migration as "alternativlos" (without alternative) rather than a recommendation. Older embedded silicon typically lacks three things simultaneously: a hardware-isolated key store (TEE/APM), side-channel countermeasures (DPA protection) strong enough for physical-access threat models, and enough RAM/compute headroom to run lattice-based PQC algorithms in software without starving the rest of the firmware. Redesigning the whole board to fix all three at once is expensive and slow. But none of those three gaps require touching the part that's already doing its job - they're all boundary problems. A second, purpose-built chip can own the boundary. Three concrete pairings Using the ESP32 family as a worked exa

Kris DeVault is desperate. His son, Brody, was born in March 2023. It wasn’t long before he started to show signs of developmental delay, says DeVault. As time went on, Brody started missing key milestones in speech, movement, and coordination, he says. When Brody was around two and a half years old, a genetic test…

The health tech data giant, which handles vast amounts of patients' medical data, said hackers struck one of its protected health data stores.

As of this week in Montana, any biotech company with an experimental drug has a clear path to selling it to consumers. Companies whose drugs have been through preliminary testing—sometimes in as few as 10 healthy people—can pay $12,500 to apply to a newly established review board for approval. Once its treatment is rubber-stamped, the…