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CONTRIBUTING.md

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+# Contributing to Dexter
+
+First off, thank you for considering contributing to Dexter! It's people like you that make Dexter such a great ecosystem for tinkering and fine-tuning Large Language Models.
+
+## 🤝 How to Contribute
+
+### 1. Code of Conduct
+By participating in this project, you are expected to uphold our standard community guidelines. Be respectful, inclusive, and collaborative.
+
+### 2. Issues & Bug Reports
+- Check if the issue already exists.
+- If it doesn't, create a new issue.
+- Clearly describe the bug: what happened, what you expected to happen, and how to reproduce it.
+
+### 3. Submitting Pull Requests
+1. Fork the repository and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs or added new Labs, update the documentation in `labs/docs/`.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. Issue that pull request!
+
+## 🔬 Adding New Labs
+If you want to contribute a new interactive notebook or AI research loop:
+1. Place it inside the `labs/` directory.
+2. Ensure it is **100% self-contained** (use `uv` or `pixi` for dependencies, avoid polluting global environments).
+3. Create a dedicated `README.md` for it inside `labs/docs/<lab_name>/`.
+4. Update the root `README.md` to link to your new lab.
+
+## 🚀 Environment Standards
+We strictly use:
+- **[uv](https://docs.astral.sh/uv/)** for lightning-fast Python package management.
+- **[Pixi](https://pixi.sh/)** for managing native dependencies and the **Mojo 🔥** compiler.
+- **MAX** for inference deployment.
+
+Please do not introduce `requirements.txt` or standard `pip` workflows into new labs. Stick to the modern ecosystem!
+
+---
+*Happy Tinkering!*

LICENSE

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+MIT License
+
+Copyright (c) 2026 lyffseba
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,24 +1,52 @@
-# Dexter 🚀
+<div align="center">
 
-This repository contains everything needed to spin up, run, and fine-tune Large Language Models (like `google/gemma-4-31B`) on cheap, high-performance cloud GPUs.
+# 🚀 Dexter
+
+**The Ultimate Ecosystem to Spin Up, Run, and Fine-Tune Large Language Models**
+
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model_Repo-ffcc66.svg?style=for-the-badge)](https://huggingface.co/lyffseba/dexter)
+[![Modular MAX](https://img.shields.io/badge/Powered_by-Modular_MAX-007FFF.svg?style=for-the-badge)](https://docs.modular.com/max/)
+[![Mojo](https://img.shields.io/badge/Language-Mojo_%F0%9F%94%A5-FF4500.svg?style=for-the-badge)](https://docs.modular.com/mojo/)
+[![Python](https://img.shields.io/badge/Language-Python-3776AB.svg?style=for-the-badge&logo=python&logoColor=white)](https://python.org)
+
+</div>
+
+---
+
+> [!IMPORTANT]
+> **Dexter** provides a straight line from your first API call to autonomous AI research loops and production endpoints. Designed for `Gemma-4-31B` and optimized for cheap, high-performance cloud GPUs.
+
+## 🗂️ Ecosystem Structure
+
+```text
+dexter/
+├── scripts/                 # Deployment scripts for Inference (Modular MAX)
+└── labs/                    # Interactive Tinkering & Fine-Tuning Labs
+    ├── docs/                      # 📖 Dedicated documentation per lab
+    ├── 00_getting_started.ipynb   # 🟢 START HERE: Data, Tokenizers & Mojo setup
+    ├── 01_inference_test.ipynb    # 💬 API Prompting & Generation
+    ├── 02_qlora_finetuning.ipynb  # 🧠 QLoRA fine-tuning 31B models
+    └── autoresearch/              # 🤖 Autonomous AI Agent Research Loop (PyTorch -> Mojo port)
+```
 
 ## 🌟 The Best Platform: Modular Cloud
 
-For cost-to-performance, **Modular Cloud** is currently the best platform. They have the fastest inference speeds for Gemma 4 using MAX, their GenAI native modeling & serving framework, completely outperforming vLLM on both NVIDIA and AMD platforms.
+For cost-to-performance, **[Modular Cloud](https://console.modular.com)** is currently the best platform. They have the fastest inference speeds for `Gemma-4` using **MAX**, their GenAI native modeling & serving framework, completely outperforming vLLM on both NVIDIA and AMD platforms.
+
+> [!TIP]
+> While Modular Cloud is ideal for inference and deployment, **RunPod** (RTX 3090/4090) remains the best value for bare-metal SSH access required for the autonomous PyTorch training labs.
 
 ---
 
 ## 🛠️ Getting Started on Modular Cloud
 
 ### 1. Account Setup
-1. Go to [Modular Console](https://console.modular.com) and sign up.
-2. Modular Cloud gives you a straight line from first API call to production endpoint.
-
----
+1. Go to the [Modular Console](https://console.modular.com) and sign up.
+2. Modular Cloud gives you a straight line from the first API call to a production endpoint.
 
-## 🚀 Running Gemma-4-31B (Inference)
+### 2. Running Gemma-4-31B (Inference)
 
-Inside your environment, install the Modular CLI and MAX:
+Inside your cloud environment, install the Modular CLI and MAX framework:
 
 ```bash
 bash scripts/01_setup_inference.sh
@@ -30,14 +58,21 @@ Then start the MAX engine server:
 bash scripts/02_start_server.sh
 ```
 
-Now you can interact with the model via Python or using a UI!
+Now you can interact with the model via Python or using any OpenAI-compatible UI!
 
 ---
 
 ## 🔬 Tinkering & Fine-Tuning Labs
 
-Check the `labs/` directory for Jupyter Notebooks designed to let you play with the model.
-- **[START HERE]** `labs/00_getting_started.ipynb`: An interactive, local introduction to AI data, tokenizers, and compiling Mojo. Start here to learn the fundamentals!
-- `labs/01_inference_test.ipynb`: Test prompting and generating text.
-- `labs/02_qlora_finetuning.ipynb`: Learn how to fine-tune Gemma-4-31B on your own custom data using LoRA.
-- `labs/autoresearch/`: An autonomous AI agent loop that tweaks LLM architectures. Includes the ongoing project to port the framework to **Mojo & MAX**.
+Check the `labs/` directory for Jupyter Notebooks designed to let you play with the model locally and in the cloud.
+
+- **[START HERE]** [`labs/00_getting_started.ipynb`](labs/00_getting_started.ipynb): An interactive, local introduction to AI data, tokenizers, and compiling Mojo. Start here to learn the fundamentals!
+- [`labs/01_inference_test.ipynb`](labs/01_inference_test.ipynb): Test prompting and generating text against your MAX endpoint.
+- [`labs/02_qlora_finetuning.ipynb`](labs/02_qlora_finetuning.ipynb): Learn how to fine-tune `Gemma-4-31B` on your own custom data using LoRA.
+- [`labs/autoresearch/`](labs/autoresearch/): An autonomous AI agent loop that tweaks LLM architectures. Includes the ongoing project to port the framework from PyTorch to **Mojo 🔥 & MAX**.
+
+---
+
+<div align="center">
+  <i>Built with ❤️ by lyffseba and the autonomous swarm</i>
+</div>