Application Architect @ IBM | AI & Cloud Solutions Specialist | Odoo Certified Expert
Application Architect with over 10 years of experience leading the development of large-scale enterprise ecosystems. Specialist in the integration of Generative Artificial Intelligence (LLMs, RAG) and Cloud-Native architectures (OpenShift, Kubernetes). Certified Odoo ERP expert with a proven track record in the digital transformation of government and retail sectors. Focused on designing scalable solutions that optimize critical business processes.
I recently saw a video about this, and it happened a few years ago. But today we don't rely so much on GPS to locate an address. Thousands of devices around the world connected by antennas can calculate proximity better than a GPS
It's a bit crazy that they used characters as markers to detect the use of Asian countries. I think in the near future they might change the intelligence of the model based on where you live
I tried the Arduino + RC low-pass filter example (using PWM as a DAC) and it was pretty impressive to see the output voltage actually smooth out instead of just flipping between 0 and 5V
What I found interesting is that the ADC reading follows the filtered signal, so you can actually observe the analog behavior from the firmware side.
Feels like this could be really useful for teaching, especially to show how digital signals turn into analog in real circuits without needing a full lab setup
A free, open-source emulator for 19 embedded boards: Arduino, ESP32, Raspberry Pi, RISC-V , running real compiled code in your browser.
The best part: it's fully local.
No cloud dependency. No student accounts. No data leaving your network. Self-hostable with a single Docker container.
Universities and bootcamps can deploy it on their own servers and give every student access to a complete embedded development environment, for free.
I've been working on this for over a year, and just shipped v2.0 with ESP32 emulation (via QEMU), a custom RISC-V core, and Raspberry Pi 3 support that runs real Python
Creator here! Just saw this was posted. I've been working on the 2.0 release to move beyond simple AVR emulation. Integrating QEMU for the ESP32 and Pi 3 (Linux) was a massive challenge, especially maintaining sync between the different emulators in a single browser tab.
I recently spent some time going through MiniMind, and it’s a remarkably clean resource for understanding the modern LLM stack under the hood. It’s a minimal, end-to-end implementation of a ~25M parameter GPT-style model in pure PyTorch, designed to be trained from scratch on a single GPU
Instead of heavy abstractions, it uses straightforward PyTorch while still implementing modern architectural choices like RMSNorm, SwiGLU, RoPE, and even MoE variants. What makes it valuable is that it doesn't stop at the forward pass; the repo covers the entire training lifecycle. You can trace the data flow from tokenizer training and pretraining, right through to Supervised Fine-Tuning (SFT), LoRA, preference optimization (DPO/PPO), and distillation
It’s small enough to actually read the source code end-to-end, but realistic enough to serve as a baseline for architectural experiments rather than just a toy example.
Curious if anyone here has used this (or similar minimal codebases) to test custom architecture modifications or train highly specialized small-scale models
I'm currently testing the pipeline locally on a PC with an RTX 4060, and it's a great fit for this kind of hardware
Thanks, I didn’t know about Renode.I’ll definitely take a look.
The reason I’m specifically exploring JavaScript is because of a project I’m working on (velxio.dev), where everything runs in the browser, so having a JS/WASM-based approach would make integration much simpler
Right now I’m experimenting with a QEMU based setup and exposing it through WebSockets, but the performance isn’t great and the emulation tends to be unstable (I’ve been hitting crashes under certain workloads).
That’s partly why I’m looking into alternative approaches , either pushing more into the browser, or finding a more robust backend model.
One thing that’s been particularly frustrating is trying to find complete documentation for Xtensa. I’ve looked around quite a bit, and it feels like there isn’t a fully open, detailed spec available. Most of what exists is either partial, behind NDA, or spread across different sources.
I’ve been looking into this space recently while exploring ESP32 emulation.
I came across this repo: https://github.com/lcgamboa/qemu/
which is quite interesting, but it’s not really a JavaScript/browser-based simulation. It relies on QEMU and actual hardware emulation rather than running in a JS/WebAssembly environment.
From what I can tell, it handles the Xtensa architecture at a much lower level, which makes sense for accuracy, but also makes it harder to bring into the browser.
It made me wonder whether a higher-level approach (similar to how AVR emulators work in JS) could be viable for ESP32, or if the complexity of peripherals (WiFi, BLE, etc.) makes that impractical.
Curious if anyone has explored alternative approaches here.
Has anyone explored this or knows of existing approaches/tools?
I've donated about $100 USD to it. KidCAD is great software because many engineering systems are too expensive for students. Another very interesting project that's gaining traction is the Arduino emulator https://velxio.dev
The Qwen 3.5 models are currently the best open-source models, but they are far behind proprietary models in speed and accuracy. I'd say they're about 60% on par with OpenAI and Anthropic models.
Application Architect with over 10 years of experience leading the development of large-scale enterprise ecosystems. Specialist in the integration of Generative Artificial Intelligence (LLMs, RAG) and Cloud-Native architectures (OpenShift, Kubernetes). Certified Odoo ERP expert with a proven track record in the digital transformation of government and retail sectors. Focused on designing scalable solutions that optimize critical business processes.
Architecture and AI GenAI (watsonx.ai), RAG, LangChain, LLMs, Vector Databases,Agents LLM Backend & Core Java (Expert), Python (Expert), Node.js, PHP. Frontend & Mobile Angular, React, Android (Native). ERP & Business Odoo Certified (Functional & Technical), CRM, Contabilidad, eCommerce. DevOps & Cloud Docker, Kubernetes, OpenShift, CI/CD, AWS/IBM Cloud.
Programming and robotics enthusiast
Github https://github.com/davidmonterocrespo24
Medium https://medium.com/@davidmonterocrespo24
I’m working on a side project https://velxio.dev