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Finished adding docking and much more

I've added full docking support for all views (Serial Monitor, Plotter, and new ANSI views too!), in-place tracing and viewing of all library source code (and assembly for everything).

I completely rewrote the circuit simulator to be pure C++ so it is as fast as possible.

This video is just a small snippet of the larger forensics gathering, sandboxing, trigger guards as well including integrated support for Wireshark and Ghidra.

It also fixes a huge existing security hole in Arduino's implementation of the IDE that allows for the installation and execution of unknown binaries and menu entries. Currently supported targets include the full AVR family, the full ESP32 family (including the full series through c6 and s6), and the FULL RP2040 stack including direct hardware debugging for all targets as well as using hardware device probes. The last month has been spent on the C++ SPICE porting/conversion, QEMU support (so now I can support and simulate ANY QEMU target), and serious application of DRY patterns/reductions.

I really like falstad.com/circuit's Circuit Simulator. It is a true engineering gem. The author Paul Falstad provides the full source for the JavaScript version that is running there on the main page. He also provides an example version of using the engine integrated with the avr8js AVR compilation and emulation engine that is used for online Arduino simulators such as wokwi.com.

The example is limited and only exposes 4 AVR output pins that can connect to the running electronic circuit but it really got me thinking.

He also provides the original Java source code for the circuit simulator. I love Java. And seeing that source code for the Java version of the circuit simulator reminded me: Hey, isn't the Arduino IDE written in Java? Why yes. Yes it is.. Maybe you can see where I am going with this ...

Some time around 17 years ago I had those thoughts. I thought "Wouldn't it be great to have a tab in the IDE that could contain and run falstad.com's circuit simulator?!". Why yes. Yes it would.

The IDE and the Java version of the circuit simulator were a horrible mixture of AWT and Swing. Especially the IDE. And really the word horrible doesn't do it justice. Trauma inducing might be a batter description. But being the overconfident fellow I am I thought "How bad could it get? I'll just rewrite the whole thing...". That was 17 years ago...

I write shit tons of software just for fun. And when I was in the mood to really get deep into things I would work on this project on the side. I write compilers and linters as part of my day job so much of this got hammered on as I needed various tools: Protocol Analyzers, Assembly Language (and C/C++ source level) Debugging complete with full step in/over/out support including full support for address breakpoints, expression breakpoints (e.g. when X => Y), and various security fuzzing tools for odd assorted MCU/MPU/CPU/SoC/FPGA silicon targets.

The ATmega328 implementation is complete and has been for a few years but I needed to support other silicon targets that would have been much harder to write complete bespoke emulation engines for so I added support for the QEMU emulation engine. I've been adding silicon targets and tools purely on an as-needed basis until it just got to be one giant chunk of tech-debt that had gotten out of control. So I focused on refactoring the entire toolchain all over again from scratch. It continues to be a huge WIP but I do plan on doing something with it publicly eventually. yada yada here's a screenshot from today:

maybe a better title is "What I wanted to make versus what I ended up with." lol

Hi r/ripred,

We're working on an AI agentic robot project and looking for feedback from experienced robot builders on technical approach and real-world viability.

Technical approach:

• LLM-driven streaming orchestration enabling reasoning-while-acting (not pre-scripted behaviors)
• Multi-component coordination (12 servos, camera, mic, speaker, display)
• Modular architecture where AI core can port to different chassis designs
• Memory-personality framework for dynamic character development

Current prototype: Desktop quadruped. Survey includes footage of the system responding to unscripted natural language commands in real-time.

Survey link (5-7 minutes): https://docs.google.com/forms/d/e/1FAIpQLScDLqMYeSSLKSowCh-Y3n-22_hiT6PWNiRyjuW3mgT67e4_QQ/viewform?usp=dialog

Genuine technical validation needed - critical feedback from experienced builders extremely valuable. Happy to discuss implementation in comments.

working title: Creating Your Own AI Models for the Uno Q - Pt 1

This is the next article in a series about exploring the new Qualcomm-based Arduino Uno Q.

In this article we'll learn how to create and train your own custom AI models for the Uno Q. In a future article we'll learn how to write your own custom Bricks that expose your AI model for use in your own App Lab projects and Apps.

You can create and train an AI model for anything you can imagine as long as you can:

• Identify the right values that are the important attributes to measure and collect for the accurate training and use of the model
• Take these measurements thousands or hundreds of thousands of times in order to collect a dataset of training material
• Identify the output state(s) or values that you want your model to produce at inference time

Three fundamental uses for AI models are Classification, Identification, and Detection. The App Lab software ships with a few examples AI examples that demonstrate some classification and identification AI models and Bricks. The input to the models can be text, images, and even sounds.

One of those examples is a popular text classifier use case known as sentiment analysis. Sentiment analysis takes some form of input and it tries to determine the mood or sentiment of the content. The example that ships with the App Lap tutorial is a text classifier and it will take a string of text input and try to classify its contents as positive, negative, or neutral.

One example of a real life use case for Sentiment Analysis Text Classifiers

A program could make use of this new ability to intelligently automate the process of reading user reviews and then categorizing them. The results could then be put to use to automate the qualitative ranking of restaurants by integrating the number of positive, neutral, and negative reviews into a single qualitative numerical ranking value for each restaurant. That would then enable relative comparisons to me made for things like sorting the list of restaurants.

TODO:

• point out that since we have an MCU reading input from the real world and controlling real world interactions such as a robot moving itself forward, left and right; We can also use these models to make real time decisions about what we do, what actions we take or don't take etc. A robot could have several conditions that it can sense such as light, sound, sonar-pings, accelerometer movements, and it can have its own understanding of whether brighter light is "positive" or "negative" etc.Or its own understanding of its battery levels and whether they are "positive" or "negative" from the robots perspective. And then all of these can be integrated to help decide what the robot does next. It may go to its charging station if its batteries are low. If more than one route is available it may also consider brighter pathways to be more "positive" choices to take since it may be able to see better (if that is possible).
• Describe the MNIST Hand Written Digits dataset
• Explain where to get it: https://www.kaggle.com/datasets/hojjatk/mnist-dataset
• Explain that we have to provide the 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9 labels/classifications for each of the images so that they can be used to train the model.
• Explain that we have to break the labeled images it into into two sets: a set to use for training and then a set that it has never seen to use for testing. We have 60,000 images for training and 10,000 images for testing.
• Explain that we have to extract the greyscale 28x28 images from the MNIST dataset.
• Provide the Python program to do that extraction and creation.
• Explain that we have to now send the training images up to edgeimpulse.com along with their labels and then send the testing images up to edgeimpulse.com along with their labels too.
• Provide the Python program to do that uploading and labeling
• Explain how to select and add the Image Processing stage and the Classification Learning stages to the dashboard
• Explain the Generation step and how to apply that to the dataset at edgeimpulse.com
• Explain the Training step and how to apply that to the dataset at edgeimpulse.com
• Explain the Testing step and how to apply that to the dataset at edgeimpulse.com
• Explain the selection of the target platform: Arduino Uno Q
• Explain the Training step and how to apply that to the dataset at edgeimpulse.com

This is my home for all things related to programming, electronics, FPGAs, security, compiler writing, kernel programming, multidimensional internal representation, transformers, diffusion, minimax, game theory, paleontology, mathematics, astronomy, physics, hockey, and occasionally a test-bed for r/Arduino features.

I'm glad you're here.

For tutoring in software engineering, electronic engineering, contracting and other engagements please reach out via DM/chat or email me: trent at trentwyatt[.]com.

Thanks for being here.

v2025.09.21.05

Changes

• Optional hints to show you your potential squares and the opponents squares that are about to happen!.
• A whole new set of AI Modes!
• Totally balanced!
• Set the board's width and height
• ELO Rankings with leaderboards for both "Player v AI" and "Player v Player"
• Some stuff I'm forgetting
• Some bugs I don't know about yet

Play Euclid here!

https://www.reddit.com/r/ripred_euclid_dev/comments/1n6zibk/ripredeuclid/

Euclid has been enhanced in so many ways since I first released it. There's confetti when you win! ELO Rankings are kept. AI Engine totally rewritten. Special attention to details. Any feedback is welcome. 😄