u/themoroccanship

Salam 3alaykom,

We've been working on something slightly ridiculous. A language model that can run almost everywhere.

After V1, Atome LM v2 (SuperESP) turns a 5$ ESP32 into a tiny AI appliance capable of running :

• Voice commands

• Motion recognition

• Machine anomaly detection

• Air-quality classification

• Energy disaggregation

• Occupancy sensing

• Water monitoring

• Sound events

• Tiny other custom classifiers and more...

All offline.

No accelerator

Everything was tested on a physical ESP32-WROOM-32.

Current numbers:

• ~27 KB runtime state

• ~265 KB free heap remaining

• Bit-for-bit reproducible decisions

• Ed25519 signed models.

• Tamper-evident inference logs

• CSV → Train → Flash workflow

Before anyone asks:

No, this is not ChatGPT on an ESP32.

No, it's not magic.

The idea is simple:

Collect your sensor data.

Export CSV.

Train.

Flash.

Deploy.

Open source GitHub repo :

https://github.com/TilelliLab/atome-lm

From Morocco with love.

* Looking for investors for my private proprietary, more advanced version, our entire operational cost is 22k per year, so even if I don't get investors, I can just keep building, we have 10 ready to go to market products and the edge AI market will grow to 100b by 2030, we are the only solution in the world that is EU AI ACT compliant. And yeah we benchmarkd it and we beat Google's TF lite Micro.

The 3 AI research prototypes ;

Tilelli LLM, our first attempt at solving hallucination, a language model that says I don't know when it does not know instead of bluffing.

https://github.com/TilelliLab/Tilelli-llm

Yaz, our first ever CRUD capable model.

https://github.com/TilelliLab/Yaz

Atome LM, an AI that runs in a 5$ chip, tested and verified in real hardware.

https://github.com/TilelliLab/atome-lm

That was just the start. Get ready, as all our previous releases, the claims may seem bold, but it's comes with open source code so you can verify my claims yourself.

What's next ? Our next releases answer these questions.

1. How to make any model forget anything - fast and cheap -

2. How to make RWKV recall 4 times more easily -

3. How to train any model 10 to 13 times cheaper

The release dates, August 3, 8, 13.

We've been working on Atome LM for embedded machine learning, and instead of showing a handful of cherry-picked examples, we wanted to evaluate it across a broader set of MCU workloads.

So we built a benchmark comparing Atome LM against deployable TensorFlow Lite Micro baselines.

Results

• 18 datasets

• 4 statistically significant wins

• 13 statistical ties

• 1 loss

Accuracy was evaluated with 95% confidence intervals, so we only count a win when the difference is statistically meaningful.

The part we found most interesting wasn't actually the accuracy—it was the model size.

Across many tasks, Atome LM achieved comparable accuracy while producing artifacts that were anywhere from about 5× to over 70× smaller than the best deployable TensorFlow Lite Micro model.

\*This is the benchmark of Atome LM V3, scheduled to be released next month.

Meanwhile if you find this interesting, V2(SuperESP edition) is open source and available in GitHub. It's a LM that runs in a $5 chip. Comes with 12 ai apps. Tested and verified in real hardware, a $5 ESP32.

See for yourself :

https://github.com/TilelliLab/atome-lm

We've been working on Atome LM for embedded machine learning, and instead of showing a handful of cherry-picked examples, we wanted to evaluate it across a broader set of MCU workloads.

So we built a benchmark comparing Atome LM against deployable TensorFlow Lite Micro baselines.

Results

• 18 datasets

• 4 statistically significant wins

• 13 statistical ties

• 1 loss

Accuracy was evaluated with 95% confidence intervals, so we only count a win when the difference is statistically meaningful.

The part we found most interesting wasn't actually the accuracy—it was the model size.

Across many tasks, Atome LM achieved comparable accuracy while producing artifacts that were anywhere from about 5× to over 70× smaller than the best deployable TensorFlow Lite Micro model.

*This is the benchmark of Atome LM V3, scheduled to be released next month.

Meanwhile if you find this interesting, V2 is open source and available in GitHub. It's a LM that runs in a $5 chip. Comes with 12 ai apps. Tested and verified in real hardware.

Verify yourself :

https://github.com/TilelliLab/atome-lm

We've been working on Atome LM for embedded machine learning, and instead of showing a handful of cherry-picked examples, we wanted to evaluate it across a broader set of MCU workloads.

So we built a benchmark comparing Atome LM against deployable TensorFlow Lite Micro baselines.

Results

• 18 datasets

• 4 statistically significant wins

• 13 statistical ties

• 1 loss

Accuracy was evaluated with 95% confidence intervals, so we only count a win when the difference is statistically meaningful.

The part we found most interesting wasn't actually the accuracy—it was the model size.

Across many tasks, Atome LM achieved comparable accuracy while producing artifacts that were anywhere from about 5× to over 70× smaller than the best deployable TensorFlow Lite Micro model.

*This is the benchmark of Atome LM V3, scheduled to be released next month.

Meanwhile if you find this interesting, V2 is open source and available in GitHub. It's a LM that runs in a $5 chip. Comes with 12 ai apps. Tested and verified in real hardware.

Verify yourself :

https://github.com/TilelliLab/atome-lm

We've been working on Atome LM for embedded machine learning, and instead of showing a handful of cherry-picked examples, we wanted to evaluate it across a broader set of MCU workloads.

So we built a benchmark comparing Atome LM against deployable TensorFlow Lite Micro baselines.

Results

• 18 datasets

• 4 statistically significant wins

• 13 statistical ties

• 1 loss

Accuracy was evaluated with 95% confidence intervals, so we only count a win when the difference is statistically meaningful.

The part we found most interesting wasn't actually the accuracy—it was the model size.

Across many tasks, Atome LM achieved comparable accuracy while producing artifacts that were anywhere from about 5× to over 70× smaller than the best deployable TensorFlow Lite Micro model.

*This is the benchmark of Atome LM V3, scheduled to be released next month.

Meanwhile if you find this interesting, V2 is open source and available in GitHub. It's a LM that runs in a $5 chip. Comes with 12 ai apps. Tested and verified in real hardware.

Verify yourself :

https://github.com/TilelliLab/atome-lm

We've been working on Atome LM for embedded machine learning, and instead of showing a handful of cherry-picked examples, we wanted to evaluate it across a broader set of MCU workloads.

So we built a benchmark comparing Atome LM against deployable TensorFlow Lite Micro baselines.

Results

• 18 datasets

• 4 statistically significant wins

• 13 statistical ties

• 1 loss

Accuracy was evaluated with 95% confidence intervals, so we only count a win when the difference is statistically meaningful.

The part we found most interesting wasn't actually the accuracy—it was the model size.

Across many tasks, Atome LM achieved comparable accuracy while producing artifacts that were anywhere from about 5× to over 70× smaller than the best deployable TensorFlow Lite Micro model.

*This is the benchmark of Atome LM V3, scheduled to be released next month.

Meanwhile if you find this interesting, V2 is open source and available in GitHub. It's a LM that runs in a $5 chip. Comes with 12 ai apps. Tested and verified in real hardware.

See for yourself :

https://github.com/TilelliLab/atome-lm

We've been working on Atome LM for embedded machine learning, and instead of showing a handful of cherry-picked examples, we wanted to evaluate it across a broader set of MCU workloads.

So we built a benchmark comparing Atome LM against deployable TensorFlow Lite Micro baselines.

Results

• 18 datasets

• 4 statistically significant wins

• 13 statistical ties

• 1 loss

Accuracy was evaluated with 95% confidence intervals, so we only count a win when the difference is statistically meaningful.

The part we found most interesting wasn't actually the accuracy—it was the model size.

Across many tasks, Atome LM achieved comparable accuracy while producing artifacts that were anywhere from about 5× to over 70× smaller than the best deployable TensorFlow Lite Micro model.

*This is the benchmark of Atome LM V3, scheduled to be released next month.

Meanwhile if you find this interesting, V2 is open source and available in GitHub. It's a LM that runs in a $5 chip. Comes with 12 ai apps. Tested and verified in real hardware.

Verify my claims yourself :

https://github.com/TilelliLab/atome-lm

Hey, let's get straight to the point, any one can say anything, that does not make it true. It's another story to make bold claims and offer proof for them.

Let's start. Appetizers.

I opened source 3 research prototypes.

https://github.com/TilelliLab

Main Meal :

Bring your own AI experts, fly to Morocco, sign an NDA, and let me show you the future of AI.

Claims :

We beat RWKV on recall/memory by 4 times.

Our architecture is 10 to 13 times cheaper to train.

Our architecture have CRUD capabilities (better than RAG)

Our architecture is meta-cognitive, it knows what it does not know.

Our Architecture is EU AI ACT compliant.

*We don't care about scaling laws, we found a way around them, and we did this after making many ai architectures that beat everyone at small scale and failed miserably at large scales.

*We are looking for just 3 investors. That's it. First come, first served. That's all we need to release our multi billions par models...and we start making profits with Atome LM's Family In 6 months tops.

Obviously, before you travel To Morocco, I'll do a good faith gesture and show you 1 of my private repos. I think the public open source repos on GitHub are enough, but if that does not do the trick, my private repo will.

Am dead serious. The next frontier model won't come from the US, China, Japan or France.

Shout-out to Deepseek, Mistral and to Sakana FUGU.

Hello,

Everything is easy to install and to customize, perfect for people who want to learn to make their own AI.

We are Tilelli Lab, a small AI lab from Morocco. Once upon a time, we popped the hood, to look what's all the buzz about AI, all what we saw is matrix multiplication, probability and hallucination...it need big data centers that consumes an entire city's electricity and water. Well, that's not intelligence.

And after more than 100 failed experiments, we did it, but we did not go looking for users, we went looking for researchers, we were looking for you and we made 3 research prototypes just for you, and here is what we made :

One model says "I don't know" when it does not know instead of bluffing.

Another one, is probably the first ever CRUD capable model.

The wildest one yet, is Atome LM, the ai that can run in a 5$ chip, comes with 12 ai apps.

GitHub and HuggingFace Available.

Don't take out words for it, every claim is verifiable and tied to a script.

See with your own eyes.

https://Tilelli.tech

https://github.com/TilelliLab

For years, edge AI has promised intelligence everywhere. In practice, most "edge AI" still means sending data to the cloud, relying on large Linux systems, or requiring expensive accelerator hardware.

SuperESP changes that.

Built on Atome LM v2, SuperESP transforms a standard ESP32 into a tiny AI appliance capable of running twelve practical applications entirely offline.

No GPUs.

No subscriptions.

No datacenter.

Just a microcontroller that costs less than a cup of coffee.

Every claim is verifiable and tied to a script.

What SuperESP Actually Is

SuperESP is not another chatbot squeezed onto a microcontroller.

It is a collection of specialized ternary AI models designed to classify events, patterns, behaviors, and anomalies directly on the device.

The current release includes:

Agriculture monitoring

Voice commands

Motion recognition

Gesture detection

Sound event classification

Machine anomaly detection

Air quality analysis

Energy monitoring

Occupancy estimation

Wearable activity tracking

Water leak detection

Predictive maintenance

It comes also with :

+ ESP32 OS

+ Universal Installer

Check out everything :

https://github.com/TilelliLab/atome-lm

Hey,

First of all, thank you all for your support, In total, our releases this week only, got 130K views, +400 up votes, +400 shares.

First release was, Atome LM, an ai that runs on 5 dollar chip. Tested on a real 5 dollar ESP32.

Second release was, Tilelli LLM, An AI that runs on your CPU, and says "I don't know" instead of bluffing.

And now, it's time for our third release, and as always, we came back with another worldwide novality, a new kind of model.

Brothers, It's our honor to present to you, Yaz. One of our best llms.

*Yaz from Tilelli Lab is a new open-source local language model that lets you directly edit its knowledge (add, update, or delete facts) like a simple database.

Key Highlights:

Editable Facts (CRUD): Change what the model knows without retraining — perfect for custom knowledge or keeping info accurate.

Honest AI: Like other Tilelli models, it says “I don’t know” instead of making things up when unsure.

Runs locally on CPU (small & efficient).

Great for privacy-focused users, personal assistants, or domain-specific tools.

A lightweight language model, Atome LM (944K parameters) has been successfully run on a $5 ESP32-WROOM-32 microcontroller — not in simulation, but on real hardware. The model generates text offline at about 1 token per second, proving that LLM inference is possible on tiny chips without cloud support.

For the little details and GitHub :

https://atomelm.com/blog/atome-runs-on-a-physical-esp32.html

Hello,

​

That post of the screenshot, is my most popular post, 52k Views, 80 up votes, 35 shares and many comments, some loved it, some criticised it.

​

Thank you for your criticism and support.

​

Here is the GitHub repo :

https://github.com/TilelliLab/Tilelli-llm

​

Clone it, tweak it, train it in more data...do what you want, you can start in minutes.

​

Made with patience in Marrakech.

​

​

Within its stated niche — a true byte-level, autoregressive language model that runs entirely as firmware on

a bare-metal MCU (no OS, no heap, no network, no PSRAM required)

Atome lm is, runs in a browser tab (see the live demo is the website https://www.AtomeLM.com) and as of June 2026, the only one of the four commonly-cited "tiny LLM" options (TinyLlama, llama2.c, TinyMaix/TFLite-Micro, Atome) that actually loads and runs as a language model on a sub-$10 MCU like a bare ESP32-WROOM-32, now demonstrated on physical silicon with a reproducible artifact.

That is a real, narrow, and verifiable claim. It is not a claim of being the highest-quality, fastest, or most power-efficient LLM in general.

What's next :

We are planning to deploy Atome LM in more MCUs.

We're going to benchmark Atome LM against the listed models.

We gonna release V2, more coherent, faster.

What do you think ?