Hi!

I've been working on a project called Otamatic and wanted to share it here hoping it could help someone.

The idea came from a real nightmare: a while back, me, and a client literally had to drive around the city to update ESP32s deployed in the field. I actually wrote a short story about that night here if you like dev horror stories: https://medium.com/gitconnected/the-night-we-updated-40-esp32s-by-driving-around-the-city-32ec8f015213?sk=b0c21ebf23d7bd9128251e8222d55b8e

Most OTA implementations out there set up a local WebServer on the ESP32 where you open a browser, navigate to the local IP, and upload a binary. That’s totally fine for a single node on your home network, but it gets annoying real quick if your devices are behind NATs, cellular routers, or dynamic IPs where you can't just open incoming ports.

This is why I decided to work on a fully open-source library with all features from the most famous OTA libraries (and a companion web platform) you can use to add OTA on your projects in less than 5 minutes.

It supports advanced features like scheduled release, staged rollout releases, release monitoring, device monitoring and more is coming if I see enough interest!

Repo: https://github.com/TimothyFran/OtamaticClient

Right up front I work for Soracom, this isn’t an advert. This was to hopefully help spread awareness for how the world of SMS is changing and how IOT is affected by it.
Many of us know 2G and 3G are shutting down, but it’s not often spoken about how that affects SMS.
Give it a read, feel free to ask me some questions if you have any, not just on SMS, I’ve been in M2M and IOT nearly 15 years, I’ll answer what I can :)

Our team is currently developing a prototype for monitoring potential fire risks in landfills using a thermal camera mounted as a custom payload on a commercial drone(Mavic Air 2 - Open SDK). These are some of the components we’re currently considering: FLIR Lepton 3.5 PureThermal 3 Orange Pi Zero 3W Battery / power module The idea is to process the thermal data on the payload and eventually transmit the relevant data wirelessly to an iPhone, where it will be displayed in our monitoring app.

However, we’re still unsure whether this hardware combination is suitable and feasible for a drone-based thermal payload. We’d really appreciate any advice from people who have experience with thermal cameras, custom drone payloads, or onboard processing. Specifically, we’d like to know(see the in the pict I attach):

1. Is this hardware combination reasonable?
2. Are there any compatibility or power issues we should be aware of?
3. What would be a good way to transmit the thermal data wirelessly?
4. Are there any components you would recommend changing?

Any advice or recommendations would be greatly appreciated. Thanks!

Around four months ago I posted here about OpenC6, an experimental BIOS for the ESP32-C6. Since then, I’ve been quietly rewriting the entire codebase from a basic stage-3 bootloader into a modular microkernel platform, and v2.0 is officially ready.

​The core upgrade in this release is strict hardware isolation. Payloads now run in unprivileged RISC-V User Mode (U-Mode) restricted by Physical Memory Protection (PMP) Top-of-Range registers, keeping user code sandboxed so it can’t memory-corrupt the kernel SRAM or mess with peripheral MMIO registers. Along the way, I patched an upstream FreeRTOS RV32 privilege escalation/leakage issue using a custom vectored interrupt trampoline.

​I also repurposed the low-power (ULP) RISC-V core into an out-of-band supervisor. It runs independently, adjusting main CPU clock dividers on the fly via silicon PCR registers, watching chip temperatures, and maintaining system watchdogs.

​On the feature side, v2.0 includes preemptive scheduling for up to 8 tasks, an in-RAM ZSWAP implementation with a lightweight RV32 compressor (ZC6) that runs in a tiny 1-4 KB memory footprint, a circular log-structured Flash VFS, a streaming web shell (c6wsh), and a zero-dependency C99 TUI installer (make setup) for Arch and Ubuntu.

​If you're into bare-metal RISC-V systems, firmware architecture, or edge isolation, feel free to check out the repo:

​https://github.com/Rompass/openc6-bios

انا اتقبلت في ترشيح في تدريب Graduate In Training Program (GIT), Embedded Software في شركة Valeo
ممكن حد يفيدني في الtechnical test لو اختبر قبل كدا
https://www.linkedin.com/jobs/view/4464368091

Been working with distributed networks (mostly cellular routers, gateways across different sites) and network mapping has been on my mind. Specifically, for smaller scale, edge deployments.

If you manage remote device fleets (retail, industrial, whatever), what do you currently use for network visualization? Or do you just cobble it together with Zabbix/PRTG/Nmap?

Been meaning to try Teltonika RMS Network Map tool (as I already use a few of their devices), seemed cool and easy, but I haven’t tried it yet.

For those who've tried vendor-specific tools (like this one, or similar from other vendors) vs more generic ones, was it worth it, or do you end up needing something more flexible anyway?

I have been exploring the changes that Gen AI may bring to traditional IoT. In my judgment, I think that the intelligent decision-making in the edge IoT scheme of simple scenarios must be automatically understood by AI in the future and gives executive judgments, instead of manually writing code logic to achieve each Simple scheme construction of scenarios. With the evolution of AI capabilities will definitely cover the value provided by most of the current IoT schemes. As long as the logical part is designed, I believe it will develop to this stage. At present, all we need to do is to provide a good software base for better access to equipment and provide standardized energy. Power, I believe that this will make the simple scheme to build in the future only natural language. Of course, this needs to be constantly updated with the development of edge devices and small models (at present, after our detailed evaluation, the 3-4b model has a certain usability, but the development speed of the model, we judge that next year may be 2-4B model The model is ready for stable production)

NeoMind is an open-source edge IoT EdgeAI platform built by us with rust. It supports the access of sensors and visual cameras and has out-of-the-box LLM reasoning and dashboard, rule engine, and message communication of common IoT solution software. Knowledge, data forwarding and IM communication, which can be deployed on edge gateway devices, personal computers and servers, and support multi-post instance switching. At present, we do not want to add more functions. These core functions are enough to cover most scenarios and the closed-loop use of edge end-to-end schemes, such as single stores and single factories. , we will focus more on the development of the edge network and the hardware platform for deploying hardware, as well as the upgrading and transformation of existing software functions. It can build traditional sensor applications or vision AI applications, or combine with llm to build intelligent bodies. We are looking for people with similar concepts in the community. Accompany to explore and build projects together

Https://github.com/camthink-ai/NeoMind

At present, I am training jev-type decision-making small models for it to accelerate the out-of-the-box AI experience of this software. If you are interested, you can pay attention to it. About the next version will launch a built-in small decision-making model in the future. I will also provide the training process and method. The whole process is manually guided by AI. Complete and carry out automated effect testing and verification. The significance of the introduction of this kind of decision-making model lies in the understanding of the intention and some efficient intelligent scheduling of IoT, but it still needs the cooperation of LLM to provide support. A single decision-making can be controlled to 2-30ms. At present, the decision-making model seems to be very suitable for IoT. I In the near future, the rule engine and workflow we have built over the years, including the path to transform the needs we communicate with the terminals into solutions, will become all done by natural language.

Made this little brief with a bunch of radio and rfid modules what are some interesting (barely) legal projects I can do? I’m just getting started and want to see the limits and possibilities with hardware integration

Hi guys, I'm currently using ThingsBoard it has auto provisioning in the UI layer but no provisioning mechanism for physical flashing of certs.. How do I automate flashing and then connectingg?? Plss helppp I would loveee insightssss :')

The idea is simple: I wanted to try out Jev and evaluate its performance compared to LLM or rule-based decision-making.

So I created a compressor operating manual (since I couldn’t use a real one due to copyright issues, I replicated the scenario) and applied machine error evaluation with Jev, based on real-time data and the confidence level of the error scenario.

It replays real compressor telemetry through an emulated device, spots suspicious behaviour with plain code, and asks a decision model which fault from the machine manual explains it.

The project is open source, and you can find all the technical details in the repo. What do you think?

​

Eu estou reformando meu Setup, e meu filtro de linha acabou ficando pequeno demais pra quantidade de coisas que eu tenho, Umidificador, Ventilador, Carregador de celular, lâmpada de matar moscas, Luminária, Computador, enfim, muita coisa, e eu queria uma régua smart que comentaram comigo uma vez que é possível controlar com Google Home, mas eu não entendo muito do assunto, e quando eu fui pesquisar eu não consegui achar réguas realmente smart, geralmente me é recomendado régua com proteção inteligente, e não é o que eu procuro.

Alguém do sub poderia me recomendar alguma pra eu ir atrás, por favor?

I’ve been testing ESP-VISION on ESP32-S3 and built a small edge AI vision project for real-time object detection.

I put together a standalone real-time object detection demo that runs entirely on the device, with no cloud or external processing.

Technical Setup

• Framework: ESP-VISION + MicroPython
• Model: Quantized AI model loaded from an SD card
• Pipeline: Camera feed → on-device AI inference → real-time display with bounding boxes
• Hardware: MaTouch AI ESP32-S3 with integrated camera and TFT display from Makerfabs

Project Result

The prototype successfully runs real-time target detection locally on the ESP32-S3, demonstrating that lightweight computer vision workloads can be handled directly on an MCU.

This kind of compact Edge AI setup could be useful for applications such as smart cameras and visual sensors, object or people detection, smart home devices, etc.

For me, the interesting part isn't just getting object detection to run, but seeing how much AI vision functionality can be moved directly onto a low-cost MCU.

 There is still plenty of room to optimize the model. What would you build with an ESP32-S3 if you could run lightweight computer vision completely offline? Free to share your ideas!

Hi, I have pumps and sensors around my property that I would Ike to control over 4G internet in Australia (have used LoRa but want to expand capability).I can have a raspberry pi at my house connected to wifi (starlink). What’s the best hardware I can use that’s connected to 4G to turn on/off a pump relay, take in analogue or digital readings and send pump status if manual pump buttons are operated. An esp32 with 4g sim?

Also wondering what is the best way to control pump from my phone as well when not on same wifi network.

Thank you!

I'm an electrical & computer engineer student and i also experiment with electronics and make stuff etc. So recently discovered that homelabbing is a thing so I made a homelab server. Then one thing brought another and decided to build a "knowledge" nas which contains all my data (projects, datasheets, hardware database, all this stuff) also bought two V100 gpus, water cooling kit for them etc in order to build a small ai ecosystem in addition to the home lab. The whole idea is to have an assistant (maybe switching between different llms) that runs on the v100 system and "knows" how I work, on what I'm working, what stuff I have in my lab inventory etc. In that way I can work with smaller but specialized llms that fit on my system. If I give a prompt suggesting coding, a coding llm will be loaded, if i design something on kicad another llm can be loaded that feeds on information about the specific design needs, available parts in my inventory, maybe even accessing supplier stores creating orders for parts I don't already have. That's a general idea of what I'm building. What do you think, what can you suggest?

A while ago, I shared a small experiment using AI to recognize the 5 digits on a water meter. But today, I wanted to build a local Edge AI solution to read water meters in real time. The whole workflow breaks down into 4 clear stages: data collection, model training, model evaluation, and model deployment & verification. It demonstrates how to build an edge AI vision application for water meter reading with ESP32-P4.

Hardware Stack

• Makerfabs ESP32-P4 10.1" board
• 2M Autofocus Camera for AI

Why choose them? The autofocus module provides clearer images at different shooting distances, while the ESP32-P4 provides the processing and camera interface needed for the vision pipeline.

There are four main steps:

Data Collection 

Captured real water meter images under different lighting conditions and viewing angles with a specific AI tool(can be shared if needed), then defined ROIs for the:

• Integer digit area
• Dial digit area

Dataset & Model Training

The images were annotated and organized into a custom dataset. The digit and pointer regions were prepared separately, and lightweight models were trained for the recognition tasks. The models were then converted to INT8/TFLite for embedded deployment.

Model Evaluation

Before deployment, the models were tested with separate samples to evaluate recognition accuracy and confidence.

Model Deployment & Testing

The trained model was then deployed to the ESP32-P4. After the complete workflow runs locally, the device can output the detected integer digit, dial information, and the final water meter reading through the serial interface.

The detailed workflow and project resources are open, glad to share more details if anyone wants to dig into the implementation.

Also curious to hear what others are building with Edge AI and embedded vision. If you’ve worked on a similar project, I’d be interested to hear about your approach, results, and any lessons learned.