feat: add authenticated remote control UI and ngrok launcher

.gitignore

@@ -125,3 +125,9 @@ ngrok.log
 *.ngrok.log
 not for your use/
 Sage_v1.0/
+
+# Large dataset and processed outputs
+data/raw/
+data/processed/
+# Tokenizer corpus used only for local training
+tokenizer/training_corpus.txt

docs/Colab_Guide.md

@@ -0,0 +1,68 @@
+# SAGE UI — Colab Quickstart Guide
+
+So you started the SAGE server in Google Colab, got your Ngrok link, and loaded the webpage. Welcome to the **SAGE Browser IDE**!
+
+Right now, you are looking at the "Control Plane." The AI is essentially a blank slate. To get a "proper agent" that can chat with you, you need to use this interface to prepare data and train the model step-by-step.
+
+Here is exactly what to do.
+
+---
+
+### Step 1: Open the Terminal & Download Data
+
+To train a model, you need text. We recently added a 5-Billion-Token downloader, but we don't need all 5 billion for a quick test.
+
+1. In the SAGE IDE, open the **CLI Terminal** (click the `>_` icon on the left sidebar, or press `Ctrl + \``).
+2. Type the following command and press Enter to download a small 1% slice (~50 Million tokens):
+   ```bash
+   python download_5b_tokens.py --output-dir data/raw --scale 0.01
+   ```
+3. Watch the terminal. It will take a few minutes to download the General Web, Code, Math, Wikipedia, and Synthetic datasets into `data/raw/`.
+
+### Step 2: Train Your Tokenizer
+
+The AI doesn't read English words; it reads "Tokens". It needs to learn its vocabulary from your downloaded data.
+
+1. Click the **Presets** tab (the rocket icon 🚀) on the left sidebar.
+2. Select **Tokenizer Train** from the dropdown menu.
+3. Click the purple **Run Job** button.
+4. A new panel will slide out showing you the live logs. Wait until it says `Job finished successfully`.
+
+### Step 3: Fast-Pack Your Data (Sharding)
+
+Training directly from text files is too slow for a GPU. We need to tokenize the text and pack it into high-speed Parquet "shards".
+
+1. Go back to the **Presets** tab.
+2. Select **Build Data Shards**.
+3. Set the `shard_size` to `2048`.
+4. Click **Run Job**.
+5. Wait for the logs to finish. When done, your data is packed and ready!
+
+### Step 4: Begin Training the AI
+
+Now it's time to put the GPU to work.
+
+1. Go back to the **Presets** tab.
+2. Select **Training Run**.
+3. You can leave the steps at the default (e.g., `20` for a smoke test, or change it to `2000` for a real micro-run). Make sure `disable_wandb` is checked so it doesn't ask for a Weights & Biases login.
+4. Click **Run Job**.
+5. The live log viewer will now stream training metrics. You will see `loss` going down and `tokens_per_second` showing how fast your Colab T4 GPU is churning through data.
+6. The trainer automatically saves checkpoints (e.g., `ckpt_step_1000.pt`) into the `runs/` folder.
+
+### Step 5: Chat with Your New Agent
+
+Once the training has run for a decent amount of steps and a checkpoint is saved, the model is ready to talk!
+
+1. Click the **Chat** tab (the speech bubble icon 💬) on the left sidebar.
+2. Type a message like _"What is Python?"_ and hit Enter.
+3. The UI will send this prompt to the backend, run inference using your newly trained checkpoint and tokenizer, and stream the generated response back to your screen.
+
+_(Note: If you only trained for 20 steps, the AI will probably respond with random gibberish. Real reasoning requires thousands of steps over billions of tokens!)_
+
+---
+
+### Pro-Tips for the IDE
+
+- **Command Palette:** Press `Ctrl + K` anywhere to quickly jump between tools.
+- **Function Inspector:** You can click the Book 📖 icon on the right to browse the actual Python codebase from within the browser while your model trains.
+- **Stop a stray training job:** Go to the **Jobs** panel (the clipboard icon) and click the red "Stop" button on any running task to free up your GPU.