AI Chapter

01-prerequisites.md

@@ -31,7 +31,7 @@ the `gh auth login` command.
 Feel free to use your favorite.
 Any command-line or graphical editor is fine!
 
-[VSCode](https://code.visualstudio.com/) is a safe choice.
+VSCode is a safe choice.
 For the AI-enabled development portion, [Cursor](https://cursor.com/) is another good
 choice!
 

03-vocabulary.md

@@ -93,4 +93,28 @@ Launcher
 : When a new JupyterLab tab is opened, the launcher is displayed.
 It includes categorized buttons to launch any applications that are registered with the launcher.
 [See the official docs for more!](https://jupyterlab.readthedocs.io/en/latest/extension/extension_points.html#launcher)
+
+VRAM
+: Video Random Access Memory - dedicated memory on a graphics processing unit (GPU) used for storing image data, textures, and other graphics-related information.
+In the context of AI development, VRAM is crucial for loading and running large language models locally.
+Typical consumer GPUs have between 4GB to 24GB of VRAM, with high-end models offering more.
+
+LLM
+: Large Language Model - a type of artificial intelligence model trained on vast amounts of text data to understand and generate human-like text.
+LLMs can assist with coding tasks like writing code, debugging, refactoring, and explaining complex concepts.
+Examples include GPT-5, Claude, and Qwen.
+These models are characterized by having billions of parameters and the ability to perform diverse language tasks without task-specific training.
+
+Agentic AI
+: AI systems that can autonomously take actions like an agent, rather than just providing suggestions.
+The key differentiator is **tool use**. Agentic AI can execute commands (build, test, format), read files, search codebases, and make changes directly.
+These tools work in an **agentic loop**: they plan an action, use a tool to execute it, observe the result, and decide the next step, iterating until the task is complete.
+Examples include Cursor, Claude Code, and Cline.
+Unlike chat-based or autocomplete AI, agentic tools act like a team member with tools — they don't just suggest, they do.
+
+LLM token
+: In the context of {term}`LLMs <LLM>`, a token is a unit of text (roughly 3-4 characters or ~0.75 words in English) that the model processes.
+API providers charge based on the number of tokens consumed—both input tokens (text sent to the model) and output tokens (text generated by the model).
+For example, "JupyterLab extension" is approximately 4 tokens.
+Token counts determine both the cost of API calls and context window limits (e.g., a model with a 1M token context can process up to 1M tokens at once).
 :::

04-materials/02-anatomy-of-extensions.md

@@ -107,6 +107,7 @@ micromamba install python pip nodejs=22 gh "copier~=9.2" jinja2-time
 :::
 
 
+(gh-auth-setup)=
 ### 🔧 Create a GitHub repository and clone it locally
 
 0. Change to the parent directory where you want to work, e.g.