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RE: Using AI and Machine Learning in Embedded Software Development

dang74 — Wed, 01 Jul 2026 09:59:38 GMT

LOL, the questions are indeeded worded in a style used by instructors.

Using AI and Machine Learning in Embedded Software Development

suriyakumar — Fri, 17 Mar 2023 10:00:39 GMT

Hello everyone,

I'm interested in discussing the intersection of AI and Machine Learning with embedded software development. With the advancements in technology and the rise of the Internet of Things (IoT), AI and Machine Learning are becoming more integrated into embedded systems.

I wanted to start this discussion to explore how AI and Machine Learning can be utilized in embedded software development. Here are some questions to get us started:

What are some examples of AI and Machine Learning being used in embedded systems?
How can AI and Machine Learning improve the performance and efficiency of embedded software?
What challenges are there when integrating AI and Machine Learning into embedded systems?
What tools and resources are available for developers interested in integrating AI and Machine Learning into their embedded software projects?

Please feel free to share your experiences and thoughts on this topic. I'm excited to hear what everyone has to say!

Which AI use case do you think is currently delivering the biggest impact in healthcare, finance, or manufacturing?

Alex03 — Mon, 29 Jun 2026 12:17:26 GMT

AI is being adopted across many industries, but I'm curious to hear what everyone thinks is creating the most real-world value today.

Is it healthcare, finance, manufacturing, or another industry?

I'd love to hear about the AI use cases you've seen make the biggest impact, whether it's from projects you've worked on or tools your organization is using.

RE: Which AI use case do you think is currently delivering the biggest impact in healthcare, finance, or manufacturing?

kmikemoo — Wed, 01 Jul 2026 03:54:42 GMT

I agree with dougw . It's "generating reports".

RE: I've been messing about with LLMs - a bit on my experience

DAB — Tue, 30 Jun 2026 20:03:33 GMT

AI is clearly the undiscovered country.

I too have been slowly testing my installed ChatGPT to explore what it can usefully do for me.

So far I have been impressed with the output options and some of the helpful features.

I still need to verify and validate some of the results, but that will take some time.

I see glimmers of potential gold, but I need to ensure that it is not just pyrite.

I've been messing about with LLMs - a bit on my experience

cstanton — Mon, 29 Jun 2026 14:35:25 GMT

I have a lot of projects that kind of go on in parallel. It's not so much that I get bored of them, it's that I often hit a brick wall and struggle to progress, so I shelve them and come back to them later.

Often it's that I need information I don't have, I don't mean to sound arrogant when I say this, a huge blocker for me is often needing a 'rubber duck' to bounce ideas off and a huge hurdle for me is that I do not typically have a 'circle' of people I can approach to discuss these challenges with. I often find that I'm 'first' to the challenge, or that it's proprietary, or those that have the knowledge do not want to share (a typical issue in the tech' field).

One project I approached is reverse engineering a BIOS for a computer. There's not a lot of detail I'll necessarily go into here, but there are embedded systems where they prevent you from being able to 'flash' the BIOS to the chip through software, because it's digitally signed with a cryptographic key. For usability, I want to be able to modify the BIOS and still be able to flash the device through software, yes there are the options with chip programmers to directly dump and restore the BIOS, and frankly that's a fallback for recovery, but ultimately, I may want to share these modifications with others that when proven safe, can be used without a chip programmer.

So therein lies my problem, I have a BIOS binary, that's typically a UEFI bios, meaning it has 'driver' components, dxe, and other reference contents, padding, etc. So the first step is usually to grab UEFIExtract, UEFITools, etc, and pour through the files, maybe throw them into ghidra, decompile them, look through the resulting C code and stretch my programming mental muscles, assembly programming, etc. Maybe be lucky and identify some strings.

Perhaps though I can 'find' my rubber duck, and maybe the rubber duck can quack back.

I was handed a trial subscription of Claude Code, and if you're not familiar with it, it's basically a Command Line Interface (cli) method of interfacing with a "chat" ai, but it is extensible. It can expand to 'skills', to control 'MCP' and even run commands directly on your system.

I fired up a spare debian linux PC and installed claude code, I could have ran it in a VM, but isolating it sufficiently on a PC I didn't care about and would happily wipe is fine for me. Immediately I'm 'chatting' with Claude Code, and uploading the binary files I'm working with into my home directory, running `claude` from the directory sets it up as a project and immediately lets it access the files. I also tell it to use 'git' to version control the files so that it can iterate and revert, and then I look into installing 'ghidra-cli', and 'ghidra' on the debian box, and I do something that I would warn against unless you know what you're doing, getting fed up of having Claude Code ask me every time for permission, I let it have full control of the computer.

It updated everything it needed, resolved issues and even found a bug in ghidra-cli that prevented it from working with ghidra 12, and fixed it. I didn't tell it to push a pull request to the ghidra-cli repo, but I did note in the 'issues' on there, people were having similar issues that were, at the time of writing, unresolved. Claude Code had already overcome hurdles I was having.

Now at this point I was burning through tokens, and I was burning through the 1 million context window, it was starting to 'forget', and I thought to myself "what can I do about this, to reduce the burden, but also to ensure I get the most out of it?".

The first step was to advise Claude Code to keep track of what it was doing, so it started writing to memory.md to tell it to refer to reference files, a 'knowledge_base.md', and 'reference_'.md files. It kept track of it's learnings from ghidra, and the overall project. When this began to get too cumbersome, it created another 'prompt' .md file, a knowledge_compact.md, and then referred to 'knowledge_base.md' when it needed to go deeper.

That helped with cross-context 'memory' issues, but what about running all of those commands and watching the terminal output?

The first step I took was to have Claude Code write up python scripts to do the work, churn over in the background as a process, and produce an output file that it could get the answers from, instead of doing command by command and churning through 'fluff' output it didn't need and then adapting, it would write a python script whose output file gave it the minimal amount it needed, and then it could continue working.

Now it could also 'spawn agents' to do work in parallel, meaning Claude Code agents, but at that point you're burning through tokens even more, and I'm loathed to let it directly charge my card to eat at my money at the same time.

However, I still encountered a problem, this was a lot of work for it overall, I had gained some good answers, it had made discoveries. My intention was to be able to 'extract' a BIOS, and then re-wrap it with an appropriate structure that it was accepted for the software to flash.

It was mostly there, and it had created a reproducible output for me to simply 'push button, receive output' with the scripts, rather than Claude Code directly saying "I've done it for you, here's the binary file". I had something reproducible.

However, I needed a bit more work, and a few more tokens.

Before the great 'rampocalypse' that we're presently suffering, I had decided to build my own gaming PC and not intend on upgrading for a long while, it rocks an i9 13900K, 64gb of RAM, and a GeForce 5090 32gb.

That means I can run my own LLM, kinda, only I run Windows on that computer. So I picked up WSL, windows subsystem on linux, and I did something a bit weird after I installed openclaw, an "ai chat interface" that was a 'claude code' alike, which could directly pull AI models from hugging face (it can also use claude code).

It's not 'amazing' to run LLMs on it, there's a lot of reasoning and data it doesn't have access to like the models on ChatGPT or Claude, or Mistral, but it works.

And it's at this point I setup SSH keys and gave Claude Code access to it. Telling Claude "setup the open claw instance as an MCP server, and delegate investigatory tasks to the sub AI so that you're not investigating or processing, but ensure it's running correctly and guide it to further investigations".

It did.

Now I had an 'orchestrator' in the form of Claude Code, and my local LLM/AI was doing the majority of the grunt work, without eating up my tokens (only perhaps my electricity bill) and it was working in a similar way, generating scripts, processing the output, and determining its own "best next steps".

After about a week of hitting Claude Code limits (which were about every 4 hours, which I'd burn through in 3-4 days and then reset after a week on a sunday) I had the secondary LLM churning through everything outside of that time and feeding back to it. Directly communicating over SSH and then feeding back to me with updated knowledge bases, practical code, and a modified BIOS that could be 'signed' to fool the system that my modifications were in fact, legitimate.

It also discovered, as it searched online, other people attempting to resolve the same 'problem' that I was, and also found vulnerability 'papers' it was able to take advantage of. Like it was my own 'mythos'.

If you take away at least one thing from my experience here, it's that you perhaps should consider 'distributed' local LLM work, if you want to dive into 'agentic' or 'claude' code working, and 'claude code' can manage to do a little bit more than solely programming.

Meanwhile, I think I'll tweak the workflow a bit more, and perhaps I can find a single board computer that can run as a 'focused' learned model, on a particular topic, that responds to MCP controls. Perhaps home automation.

Let me know what you think.

RE: Which AI use case do you think is currently delivering the biggest impact in healthcare, finance, or manufacturing?

dougw — Tue, 30 Jun 2026 06:10:05 GMT

Mostly generating and translating reports and presentations. There are lots of more powerful applications but the actual number of highly technical uses is small compared with the number of casual users.

RE: I've been messing about with LLMs - a bit on my experience

dang74 — Mon, 29 Jun 2026 20:40:11 GMT

Wow. After reading this I am getting the impression that today's programmers are not unlike the sorcerer's apprentice in Fantasia or perhaps the conductor of an orchestra.

RE: Which AI use case do you think is currently delivering the biggest impact in healthcare, finance, or manufacturing?

manojroy123 — Mon, 29 Jun 2026 13:55:44 GMT

Manufacturing, healthcare and finance. Neural network for pattern recognition in all of the given sector and agentic AI for questioning specially in finance used by Jane Street.

RE: Little *** AI UI Version one

colporteur — Fri, 12 Jun 2026 15:13:39 GMT

And here I was trying to find the context of LB & LBOS in relation to AI. Man was I off!

Little *** AI UI Version one

e14 Contributor — Thu, 11 Jun 2026 22:55:51 GMT

Evening everyone,

Hope everyone's projects are going great!!

Believe in the process!!

I would love some feedback on version one of LBs interface the start of LBOS being built.

This is inspired by my favourite movies and my love for modified cars.

We went from concept to first version in 24 hours.

It has some tweaks/spacing needing done but i think it is brilliant...

[View:/cfs-file/__key/communityserver-discussions-components-files/81/Little-***-AI-_2D00_-Google-Chrome-2026_2D00_06_2D00_11-23_2D00_30_2D00_03.mp4:640:360]

Good morning, I thought i would add how i am trying to get this UI to work, At min everything is set as active so you can see everything. When active the voice waveform would be flat and is only activated on LBs voice.

The other detail I’m building into LBOS is that the background code rain won’t just be decorative. Later, LB’s states and actions will “leak” into the flowing code.

The normal background will show faint diagnostic-style data moving up and down behind the interface. When LB starts doing something, small words or packets will be injected into that stream. So instead of only showing random code, the system might briefly show things like LISTENING, AUDIO INPUT, PROCESSING, CAMERA CHECK, GPS LOCK, RESPONSE READY, or RETURNING TO STANDBY.

These won’t appear as popups or normal text labels. They will drift through the existing code stream like data moving through LB’s system. The idea is that the voice chamber shows LB’s voice, the state text shows what LB is currently doing, and the background code rain gives the feeling of LB’s internal thoughts and processing.

For now I’ve only added the visual code stream layer, but the plan is to link it later to LB’s actual states and specialist agents, so the interface reacts to what LB is really doing rather than just playing a fake animation.

Thanks for reading!

LB Architecture

Level 1 - Sensors (The Senses)

These don't think.

They only observe.

LEFT MIRROR MIC
RIGHT MIRROR MIC
INTERIOR MIC

PIR SENSORS
MMWAVE SENSOR

IMX500 CAMERA

GPS

VEHICLE DATA

Their job is simply:

Something happened.

Level 2 - Pi Specialists (The Nerves)

Running on the Pi.

Each specialist watches one area.

Audio Specialist

Listens to microphones.

Reports:

Wake word detected
Voice direction
Noise level
Speech present

Vision Specialist

Handles IMX500.

Reports:

Person detected
Face detected
Vehicle detected
Movement detected

Presence Specialist

Handles:

PIR
MMWave

Reports:

Someone approaching
Someone inside
Area clear

Location Specialist

Handles:

GPS
Speed
Direction

Reports:

Parked
Driving
Location update

Vehicle Specialist

Handles:

Battery
Voltages
Temperatures
Controller status

Reports:

Battery low
Controller offline
System healthy

Level 3 - Brain (Laptop)

This is LB.

LB does NOT read sensors directly.

Instead specialists send reports.

Example:

Audio:
Wake word detected

Vision:
Person detected

Presence:
Someone near driver door

LB receives:

A person is at the driver's door
and said "Hello LB"

Now LB thinks.

Level 4 - Personality Layer

LB decides:

Speak
Stay silent
Flash LEDs
Show animation
Arm sentry
Disarm sentry

Level 5 - Output Specialists

These are also nerves.

Voice Specialist

Controls:

Voice chamber
Waveform
TTS

Display Specialist

Controls:

State text
Code rain
Top bar
Status bar

Lighting Specialist

Controls:

Front grille
CPU display
Helix tanks
Ripple effects
Matrices

Example Flow

Someone walks up to LB.

RE: Little *** AI UI Version one

Jan Cumps — Fri, 12 Jun 2026 12:43:56 GMT

Posting the photo of a previous post, because the forum **** the name:

RE: Little *** AI UI Version one

cstanton — Fri, 12 Jun 2026 12:32:56 GMT

[quote userid="37381" url="~/technologies/ai-machine-learning/f/forum/57028/little-ai-ui-version-one/236089"]LB and LBOS[/quote]

"<name of project>" and "<name of project> Operating System"

RE: Little *** AI UI Version one

colporteur — Fri, 12 Jun 2026 11:54:26 GMT

Can you define the acronyms LB and LBOS in this context?

RE: Update on LB

stanto — Sat, 30 May 2026 02:12:30 GMT

Sweet ride, looking forward to seeing how this progresses!

Update on LB

e14 Contributor — Thu, 28 May 2026 07:46:50 GMT

Hi there i have been away for a bit but now i am back with an update on my Little *** Project.
Little *** is my AI car i am building. I am no tech expert and i am putting this together through pure passion for modified cars and tech.

So lets get in to it....

Little *** AI – Project Update

The Little *** AI project is continuing to evolve from a modified car concept into a fully integrated AI-driven vehicle system combining embedded control, local AI processing, visual systems, sensors, and custom electronics.

The original goal was never to build a polished corporate system or a supercar-style showcase. The aim has always been to create something unique, experimental, modular, fun, repairable, and achievable using affordable hardware and a lot of hands-on design work.

This is still very much an active build and development project. A lot of the system is currently being prototyped, redesigned, rebuilt, and refined as the project evolves.

Current Core Hardware Direction

Planned Main AI System

Lenovo Legion 5 gaming laptop
Intel i7 CPU
RTX 3060 GPU
Local AI model hosting and processing

The next major step for the project is moving Little *** AI onto a dedicated RTX-powered laptop system to allow proper local AI processing inside the vehicle.

The goal is for the laptop to eventually handle:

local language models
voice processing
reasoning
future AI camera processing
advanced vehicle behaviours

The move to a dedicated RTX system comes from wanting faster local AI performance while still allowing the vehicle to operate offline when needed.

At the moment, I am still sourcing the laptop and hoping to have this side of the project moving properly by the end of June.

Vehicle Control & Embedded Systems

Current hardware already being integrated includes:

Raspberry Pi 5 (16GB)
M5Stack module
ESP32-S3 controllers
NodeMCU ESP8266 controllers

The Raspberry Pi 5 is currently acting as the main bridge between systems, controllers, sensors, networking, and future AI integration.

The M5Stack is planned to handle lightweight fast-response interaction and quick vehicle actions while the larger AI systems process deeper requests.

The ESP32-S3 and NodeMCU boards are being distributed throughout the vehicle to control:

lighting
visual effects
displays
sensors
future smart-mod systems

A lot of people ask why I use NodeMCUs and S3 boards instead of more expensive industrial hardware.

The answer is simple:

they are affordable
easy to replace
easy to prototype with
widely supported
ideal for modular experimentation

This project is heavily focused on practical iteration and learning through building rather than chasing expensive hardware for the sake of it.

Wiring Philosophy

Another thing people usually notice is the use of screw terminal adapters instead of permanently soldering every connection directly.

This is intentional.

The system is being designed to stay:

modular
serviceable
replaceable
easier to debug

When experimenting with large distributed electronics systems inside a vehicle, being able to quickly swap or isolate a controller is far more practical than repeatedly desoldering wiring.

Sensor Systems

Current and planned sensors include:

digital microphones
PIR sensors
human presence sensors
mmWave presence detection
AI camera systems
vehicle telemetry integration

The long-term goal is for the vehicle to eventually react differently depending on:

presence
interaction
environment
AI state
security/sentry conditions

Rear “Circuit Display” System

One of the biggest custom fabrication parts of the project so far has been the rear AI circuit display.

The display combines:

mirrored surfaces
custom-cut perforated hex paneling
distributed controller mounting
RGB lighting systems
a custom illuminated “processor core”
visible modular electronics

The processor core has recently been redesigned to use an 8x8 LED matrix behind an opal acrylic diffuser after earlier LED strip prototypes produced uneven lighting results.

The controller layout has also already gone through several redesigns to improve:

symmetry
accessibility
cable routing
serviceability
visual balance

The aim is to make the display feel less like decorative RGB lighting and more like an exposed AI-powered electronic system.

Software Direction

The software side is also slowly evolving into a more structured “Little *** AI” architecture.

The long-term goal is for the system to eventually support:

local-first AI operation
offline functionality
distributed controller communication
AI-assisted vehicle interaction
onboard visual and sensor feedback systems
sentry/security behaviours
Android head-unit integration

The system will likely eventually be designed around multiple layers including:

local AI reasoning
fast-response embedded systems
distributed controller nodes
vehicle state systems
visual feedback systems

A large amount of the AI side is still being actively planned and rebuilt as the hardware direction becomes more finalised.

Current Focus

Current development is mainly focused on:

controller integration
wiring architecture
visual system refinement
processor core behaviour
embedded communication systems
modular mounting systems
preparing the project for the future AI migration

There is still a huge amount left to build, redesign, and learn, but the project is finally starting to move from scattered prototypes into a more cohesive system.

More updates soon.

[View:/cfs-file/__key/communityserver-discussions-components-files/225/3660.LBs-Processor-glow.mp4:640:360]

RE: Update on LB

e14 Contributor — Fri, 29 May 2026 23:37:05 GMT

Thank you, i will post updates soon. I hope to do tests on the voice visual very shortly. One of the s3 in the display is dedicated to the job using a neopxl at the 7 strips in the grill and running cat6 for data lines. I did post a question about this a bit back because i was getting interference when the voice of LB finished and it was supposed to drop back to knightrider scan mode but i have hopefully sorted that out now. As you can see it is all on the go at once, but it is coming along and hopefully be on the home stretch soon!!

RE: Update on LB

DAB — Fri, 29 May 2026 21:14:57 GMT

Interesting project.

I look forward to future updates.

RE: I made a Lightweight Fingers Detection model That dosent uses any Training Data or any AI and is almost accurate

Christopher678 — Tue, 03 Mar 2026 22:26:01 GMT

Great post

I made a Lightweight Fingers Detection model That dosent uses any Training Data or any AI and is almost accurate

Aniket_kumar_raj — Fri, 27 Feb 2026 15:13:50 GMT

Do you know where was i For a few Days....

I was Grounded home with no internet so i made something that i was not able to find on internet... but first lets start from beginning to explain everything .look at this thing

[View:https://www.youtube.com/shorts/TsgXpnmGRMQ:640:360]

I saw this short in my Short feed and tried to create it but the one thing required was a lightest hand detection model, but none ai could make it and i wasn't able to find any on internet , then i took everything in my own hands and made this code that detects fingers smoothly, but dont move your face in box, only hands are allowed in the green box

here is the code that i made roasting my 1.3 megapixel camera of 17 years old laptop that i use for daily study..

to use this you must have these liblaries in python --->

cv2 , numpy , math

I did add comments so its easy to understand and variables are good too

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Is the next leap of electronics brain organoids?

stanto — Sat, 28 Feb 2026 16:39:09 GMT

There's not a loud majority talking about this tech' at the moment, but it's somewhat concerning. Using stem cells groups are "finally" combining electronics and organics, notably stem cells. Doing everything from running Doom (of course) to running LLMs on/with them. This should be raising all sorts of questions.

Gabriel Torch does a great job at summarising legit research papers on this topic.

[View:https://www.youtube.com/watch?v=uQEg3pg7w9E:640:360]

https://www.sciencedirect.com/science/article/pii/S0896627322010339

https://www.nytimes.com/2019/08/29/science/organoids-brain-alysson-muotri.html

https://www.newscientist.com/article/2517389-human-brain-cells-on-a-chip-learned-to-play-doom-in-a-week/

And a few more scary videos on organoids that are worth watching:

[View:https://www.youtube.com/watch?v=9QXDdjp3OqA:640:360]

[View:https://www.youtube.com/watch?v=bzMjTiY7o7g:640:360]

You can also, apparently, play with them yourself:
[View:https://www.youtube.com/watch?v=vg2Y82rYnHM:640:360]

Anyone else welcoming our new stem-cell-organoid overlords?

RE: Is the next leap of electronics brain organoids?

stanto — Sun, 01 Mar 2026 22:29:17 GMT

Gabriel Torch did a video where they covered that the organoids were 'dying' because.. well they were starved of oxygen and nutrients, which is now a solved problem apparently.

[quote userid="220772" url="~/technologies/ai-machine-learning/f/forum/56726/is-the-next-leap-of-electronics-brain-organoids/234112"]I'm not sure what the moral concern is[/quote]

And then the question comes up about sentience, and running them in a 'virtual environment' so that they 'don't get bored'.

RE: Is the next leap of electronics brain organoids?

stanto — Sun, 01 Mar 2026 22:26:49 GMT

[quote userid="328965" url="~/technologies/ai-machine-learning/f/forum/56726/is-the-next-leap-of-electronics-brain-organoids/234109"]Wow, this kind of caught me off guard.[/quote]

Same. It's really being kept 'on the down low'.

RE: Is the next leap of electronics brain organoids?

kmikemoo — Sun, 01 Mar 2026 22:01:04 GMT

Awesome. Couple this with the blind trust of AI and we'll be living a video game. Or a movie. Not excited.

RE: Is the next leap of electronics brain organoids?

robogary — Sat, 28 Feb 2026 21:23:18 GMT

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