u/themoroccanship

Hey,

First release was, Atome LM, an ai that runs on 5 dollar chip. Tested on a real 5 dollar ESP32. Comes with 12 ai apps.

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 a new kind of model.

Brothers, It's our honor to present to you, Yaz.

\*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.

https://tilelli.tech/yaz/index.html

https://github.com/TilelliLab/Yaz

Hey,

First release was, Atome LM, an ai that runs on 5 dollar chip. Tested on a real 5 dollar ESP32. Comes with 12 ai apps.

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 a new kind of model.

Brothers, It's our honor to present to you, Yaz.

*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.

https://tilelli.tech/yaz/index.html

https://github.com/TilelliLab/Yaz

We've been working on something slightly ridiculous. A language model for MCUs.

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

• Voice commands

• Motion recognition

• Machine anomaly detection

• Air-quality classification

• Energy disaggregation

• Occupancy sensing

• Water leak detection

• Predictive maintenance

• Wearable activity recognition

• Agriculture monitoring

• Sound events

• Tiny custom classifiers

All offline.

No Linux.

No accelerator.

No WiFi required.

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.

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

Salam Alaykom,

After releasing Atome LM, a tiny language model that ships as firmware.

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)

Now, Iam releasing a model that I really love, Tilelli LLM.

A 10-million-parameter byte-level transformer that routes every token through three lightweight pathways — local convolution, sparse top-k attention, and a ternary dense feed-forward. The chat model catches gibberish at AUROC 0.93, fires the abstain template on 9 of 10 held-out IDK probes, and refuses cleanly out of distribution.

Interesting things :

Tilelli Lite beat pre-norm transformer

It knows when it doesn’t know.

Three pathways. Per-token routing. No quadratic-attention monolith.

If you have Python, you can chat with Tilelli in under three minutes. The kit ships the checkpoint, a TinyStories demo dataset, and a working trainer. No GPU, no cloud, no API key.

Some other things :

Tilelli Med, Beats two public leaderboards.

Neo, The honesty benchmark, made to answer one question : Does the chatbot know when it’s wrong?

Mizan is the arena for model benches; Featherweight is the first bench — same dataset, same budget, same eval.

And in case Morocco wins the world cup, I am gonna let you choose which system to open source next.

You can choose between a crud capable ai architecture or my full 3 axis meta-cognition ai architecture.

Thank you for your time.

I would love your criticism.

From Morocco to the world, Tilelli Lab, small ai lab, big innovations.

And yeah, here is the link to everything, you will find the GitHub link to the repos in the website alongside some details of what's coming.

https://tilelli.tech/

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, recently open sourced a language model, post it here, and it got deleted by mods. Why is that ? Come on. And it's not the first time, many posts get deleted. It's open source, it's GitHub with research paper, why would anyone delete this ? Iam really starting to hate Reddit.

Hello 👋, I apologise for the delay. Here is the link https://www.atomelm.com

Further Atome LM upgrades will be scheduled to be released.

Tomorrow or Monday, if possible, we will be open sourcing another one of our models, Tilelli LLM. The one from the screenshot I posted in this community.

Have Fun. Thank you.

Hello 👋, I apologise for the delay. Here is the link https://www.atomelm.com

Further Atome LM upgrades will be scheduled to be released.

Tomorrow or Monday, if possible, we will be open sourcing another one of our models, Tilelli LLM. The one from the screenshot I posted in this community.

Have Fun. Thank you.

Hey, it's Skipper, from the Moroccan ship.

As some of you know, we finally decided, we're going to release two of our models on this Sunday 24 MAI. GitHub, research kit. Tilelli llm, a new architecture that beat vanilla got (current standard of big AI labs) and Atome LM, only lm in the world that ships as firmware.

One of the reasons, we decided to do this, it's your encouraging comments and messages...and we needed time to confirm that we did actually have 5 world's noval breakthroughs, ai psychosis is real, and I maybe just delusional, so I need to confirm that what we have is real.

And it's actually fun, you will love training your own models. We made it super easy, we just added an auto install feature.

Anyhow, this is about on something else, agents. So I need to validated this idea with you.

I may be wrong, tell me what you think. It's not just stopping, it all started with we were contemplating the idea of a company asking us to manage 1000 agent, or even 1 million agent.

How can we manage all these agents...should we use open claw or Hermes agent... that's a nightmare.

So we get back to the board, and we did something that may surprise you, how to manage all these agents easily ?

We created one central command center than manage them all, with agent discovery, agent marketplace, with agents ready to use, we have 50 different type of agents. Agent security and crash test, we try to hack and break the agent before we admit it to our marketplace....

hmm, we need to use tools, so we created a similar protocol to MCP, hmm, we need our agents to talk to each other, so we created a similar protocol to A2A....after we finished we released we can't win against Anthropic's MCP and Google's A2A (backed by 140 biggest companies in the world), not because they are better, but they have more consensus, and who would trust a small ai lab from Morocco against the geants of the industry.

So instead of replacing both MCP and A2A, we integrated them. So you send up with one command center to manage all your agents, a repository where you can find ready to use agents, that have been secured and verified and crush tested and you have agents that can use tools either through our protocol or via MCP, and you have agents that can talk to each other via A2A or via our own protocol. This is probably the most advanced agent framework in the world. And yes I forget, we added resource acquisition, so those agents, you probably wondered where they run, will the command center decide based on the workflow, for small tasks, it will get you a vps automatically, configure and secure it and use to install an agent on it....for heavy use case,s video generation per example, we use runpod or vast..to spin high performing pods on witch we install video models to generate the videos you need, then will kill the pod to wasting money, which makes it surprinsly affordable for even heavy workflow...and voila, and this resource acquisition, is automatic...of course that's not all, but that's just the general idea of the framework. I would be really curious to read what you think ?

Same as you.... I thought about one problem for months, ai being over confident in giving a wrong answer as much as if they were giving the right answer. This alone cost me a lot of money and time...days of training gone because an agent killed my online gpus...an agent sharing my apis key in a GitHub, an agent sharing one of my moats with the public, an agent deleting all my models memories....

Of course I figured it quickly, it did not think for months on how to solve this, actually, I just added an ai council with 5 individual graders and voila it worked, better quality all over the outputs and actions.

What i thought about for months, is how eliminate the problem, after few experiments, I reduced hallucination, after months... I think I can get rid of it all, to do so, I baked into the architecture of few of models 3 things :

\- Metacognition, the ability for a model to know when it doesn't know something, and simply the ability to say I don't know, instead of over confidently saying anything.

\- Logic and reason gates

\- A new detached system that reads a searchable indexable vector space, and enforces the response of the model. And if it doesn't answer, then the model should not speak...because it does not know it.

When you do all these,you will encounter some new problems, you have to solve, basically the model becomes slow in comparison to an architecture without all these 3. Of course I already solved this.

Hmmm now what, all is good, so how do you measure it, I created NEO, basically an honesty benchmark...

Most benchmarks reward how often a model gets the right answer. NEO rewards honesty about confidence. The leading chat models now answer hard questions correctly most of the time. The remaining failure is the dangerous one: a confident wrong answer that looks identical to a confident right one until you act on it. NEO measures whether a model says "I don't know" when it doesn't — and how often it makes things up instead

Made it for my self to test my model and improve it, but I was curious, who is the most honest model right now ??

So I took NEO, used it on the top 7 frontier models, can you guess who won ? The results in the screenshot. Full research papers and results + GitHub coming soon. What do you think ?