content: rewrite Real-World Project objectives with Bloom's taxonomy verbs + opencode run option

docs/projects/agentic-code-reviewer/index.md

@@ -23,11 +23,11 @@ This assumes Python 101 and enough comfort with git to know what `git diff` show
 
 ## 🎯 What you'll do
 
-1. Install `uv`, get a free-tier LLM API key, and set up a small project — all in one place, before any building starts.
-2. Use Python's `subprocess` module to run `git diff` for real and capture its output as text.
-3. Design a system prompt that turns a general-purpose LLM into a focused, structured code reviewer.
-4. Send a diff to the model and print its feedback in a clear, readable format.
-5. Run the whole tool against a real diff — your own uncommitted changes, and a specific past commit from this course's own repo history.
+1. **Set up** `uv`, a free-tier LLM API key, and a small project — all in one place, before any building starts.
+2. **Capture** a real `git diff` with Python's `subprocess` module and read its output as text.
+3. **Design** a system prompt that turns a general-purpose LLM into a focused, structured code reviewer.
+4. **Evaluate** a diff with the model and **present** its feedback in a clear, readable format.
+5. **Apply** the whole tool to real diffs — your own uncommitted changes, and a specific past commit from this course's own repo history.
 
 ## Where to run this
 
@@ -41,6 +41,7 @@ This assumes Python 101 and enough comfort with git to know what `git diff` show
 [![Open In Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/abderrahim-lectures/python-data-analysis-course/blob/main/examples/agentic-code-reviewer/notebook.ipynb)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/abderrahim-lectures/python-data-analysis-course/main?filepath=examples%2Fagentic-code-reviewer%2Fnotebook.ipynb)
 
+**opencode** *(optional)* — a free, open-source AI coding agent that runs in your terminal. If you'd rather have an agent write and run this project for you than type the code yourself, install it with `curl -fsSL https://opencode.ai/install | bash` (or `npm install -g opencode-ai`) and point it at this repo with the same API key from Setup below. It's optional — this project's whole point is building it yourself, so treat it as a bonus, not a shortcut.
 ## Setup
 
 Everything you need before you write a line of the reviewer itself: a real Python, a free API key, and a small project to hold both.
@@ -417,3 +418,4 @@ Built something you're proud of? [`examples/student-projects/`](https://github.c
 Welcome to writing Python outside the browser. 🎓
 
 <ProjectProgressCheckbox projectId="agentic-code-reviewer" />
+

docs/projects/ai-agent/index.md

@@ -22,10 +22,10 @@ This is optional and ungraded — a good fit once you've finished Python 101 (da
 
 ## 🎯 What you'll do
 
-1. Install `uv`, a fast, modern tool for managing Python itself and your project's dependencies — no separate Python installer needed.
-2. Get a free-tier AI API key. **You're free to use whichever provider you like** — GitHub Models is the suggested default below since it needs no separate signup (you already have a GitHub account), but Gemini, Groq, Mistral, Cerebras, and OpenRouter all have workable free tiers too.
-3. Set up a small project and install LangChain's `deepagents`.
-4. Write and run one small agent, locally, from your own terminal.
+1. **Set up** `uv`, a fast, modern tool for managing Python itself and your project's dependencies — no separate Python installer needed.
+2. **Obtain** a free-tier AI API key. **You're free to use whichever provider you like** — GitHub Models is the suggested default below since it needs no separate signup (you already have a GitHub account), but Gemini, Groq, Mistral, Cerebras, and OpenRouter all have workable free tiers too.
+3. **Configure** a small project with LangChain's `deepagents` installed.
+4. **Write and run** one small agent, locally, from your own terminal.
 
 ## Where to run this
 
@@ -41,6 +41,7 @@ This is optional and ungraded — a good fit once you've finished Python 101 (da
 
 Be honest with yourself about the tradeoff, though: this is a lower-fidelity way to experience the project than a real local `uv` project — no separate files, no real project structure, just cells in a notebook. Treat it as a quick way to experiment, not the primary path.
 
+**opencode** *(optional)* — a free, open-source AI coding agent that runs in your terminal. If you'd rather have an agent write and run this project for you than type the code yourself, install it with `curl -fsSL https://opencode.ai/install | bash` (or `npm install -g opencode-ai`) and point it at this repo with the same API key from Setup below. It's optional — this project's whole point is building it yourself, so treat it as a bonus, not a shortcut.
 ## Setup
 
 ### Install `uv`
@@ -286,3 +287,4 @@ Built something you're proud of? [`examples/student-projects/`](https://github.c
 Welcome to writing Python outside the browser. 🎓
 
 <ProjectProgressCheckbox projectId="2026-ai-agent" />
+

docs/projects/browser-automation-agent/index.md

@@ -28,12 +28,10 @@ This is optional and ungraded. See [Real-World Projects](/docs/projects) for the
 
 ## 🎯 What you'll do
 
-1. Install Python [Playwright](https://playwright.dev/python/) and a real Chromium browser binary.
-2. Write a hardcoded script that fills out a real practice form by hand — and see exactly how brittle
-   that is.
-3. Wrap page-reading and field-filling as **tools** an LLM agent can call.
-4. Give the agent a plain-English goal ("fill this form with these details") and let it decide which
-   fields map to which tool calls, then run it end-to-end and verify the real submission.
+1. **Install** Python [Playwright](https://playwright.dev/python/) and a real Chromium browser binary.
+2. **Author** a hardcoded script that fills out a real practice form by hand — and **analyze** exactly how brittle that approach is.
+3. **Design** page-reading and field-filling as **tools** an LLM agent can call.
+4. **Direct** the agent with a plain-English goal ("fill this form with these details"), let it decide which fields map to which tool calls, then run it end-to-end and **verify** the real submission.
 
 ## Where to run this
 
@@ -59,6 +57,7 @@ demo only the agent's *decision-making* — which field it thinks matches which
 with no actual browser opened anywhere. That's a legitimate way to explore Step 3's reasoning in
 isolation, but it is not this project; treat it as a toy, not a substitute for Setup below.
 
+**opencode** *(optional)* — a free, open-source AI coding agent that runs in your terminal. If you'd rather have an agent write and run this project for you than type the code yourself, install it with `curl -fsSL https://opencode.ai/install | bash` (or `npm install -g opencode-ai`) and point it at this repo with the same API key from Setup below. It's optional — this project's whole point is building it yourself, so treat it as a bonus, not a shortcut.
 ## Setup
 
 ### Install `uv`
@@ -406,3 +405,4 @@ Built something you're proud of? [`examples/student-projects/`](https://github.c
 Welcome to writing Python outside the browser. 🎓
 
 <ProjectProgressCheckbox projectId="2027-browser-automation-agent" />
+

docs/projects/chat-with-pdfs/index.md

@@ -23,10 +23,10 @@ This is optional and ungraded. See [Real-World Projects](/docs/projects) for the
 
 ## 🎯 What you'll do
 
-1. Extract text from a folder of PDFs, page by page, and split it into small chunks — keeping each chunk's source filename and page number attached.
-2. Turn each chunk into a vector, entirely locally, with no API key and no cost, using `sentence-transformers`.
-3. Retrieve the chunks most relevant to a question across *all* the PDFs at once, then ask a free-tier LLM to answer using only that context — with a `(source, page N)` citation required for every fact.
-4. Wrap it all in a small interactive loop so you can keep asking questions without re-running a script each time.
+1. **Extract** text from a folder of PDFs, page by page, and **split** it into small chunks — keeping each chunk's source filename and page number attached.
+2. **Embed** each chunk as a vector, entirely locally, with no API key and no cost, using `sentence-transformers`.
+3. **Retrieve** the chunks most relevant to a question across *all* the PDFs at once, then **ask** a free-tier LLM to answer using only that context — with a `(source, page N)` citation required for every fact.
+4. **Build** a small interactive loop so you can keep asking questions without re-running a script each time.
 
 ## Where to run this
 
@@ -42,6 +42,7 @@ This is optional and ungraded. See [Real-World Projects](/docs/projects) for the
 
 Be honest with yourself about the tradeoff, though: this is a lower-fidelity way to experience the project than a real local `uv` project — no separate files, no real project structure, just cells in a notebook. Treat it as a quick way to experiment, not the primary path.
 
+**opencode** *(optional)* — a free, open-source AI coding agent that runs in your terminal. If you'd rather have an agent write and run this project for you than type the code yourself, install it with `curl -fsSL https://opencode.ai/install | bash` (or `npm install -g opencode-ai`) and point it at this repo with the same API key from Setup below. It's optional — this project's whole point is building it yourself, so treat it as a bonus, not a shortcut.
 ## Setup
 
 ### Install `uv`
@@ -512,3 +513,4 @@ Built something you're proud of? [`examples/student-projects/`](https://github.c
 Welcome to writing Python outside the browser. 🎓
 
 <ProjectProgressCheckbox projectId="2027-chat-with-pdfs" />
+

docs/projects/codebase-knowledge-graph/index.md

@@ -23,12 +23,12 @@ This assumes Python 101 and comfort with functions and imports — nothing from
 
 ## 🎯 What you'll do
 
-1. Install `uv` and set up a small project with `networkx` and `pyvis` — no API key, no signup, nothing to configure.
-2. Parse a single Python file's AST to find its function definitions, class definitions, and imports.
-3. Walk an entire repository and build a graph out of everything you find, using `networkx`.
-4. Add edges for **import** and **call** relationships, so the graph captures how the pieces actually connect, not just what exists.
-5. Visualize the graph as an interactive HTML page with `pyvis` (and, optionally, a static image with `matplotlib`).
-6. Write a small query function — "what does this function call?", "what imports this module?" — and run the whole thing against a real repository.
+1. **Set up** a small project with `uv`, `networkx`, and `pyvis` — no API key, no signup, nothing to configure.
+2. **Parse** a single Python file's AST to **identify** its function definitions, class definitions, and imports.
+3. **Build** a graph of an entire repository with `networkx`, walking everything you find.
+4. **Model** **import** and **call** relationships as graph edges, so the graph captures how the pieces actually connect, not just what exists.
+5. **Visualize** the graph as an interactive HTML page with `pyvis` (and, optionally, a static image with `matplotlib`).
+6. **Query** the graph with a small function — "what does this function call?", "what imports this module?" — and **evaluate** it against a real repository.
 
 ## Where to run this
 
@@ -448,3 +448,4 @@ Built something you're proud of? [`examples/student-projects/`](https://github.c
 Welcome to writing Python outside the browser. 🎓
 
 <ProjectProgressCheckbox projectId="codebase-knowledge-graph" />
+

docs/projects/commit-message-agent/index.md

@@ -23,11 +23,11 @@ This assumes Python 101 and enough comfort with git to know what `git add` and `
 
 ## 🎯 What you'll do
 
-1. Install `uv`, get a free-tier LLM API key, and set up a small project — all in one place, before any building starts.
-2. Use Python's `subprocess` module to run `git diff --staged` for real and capture its output as text.
-3. Design a system prompt that turns a general-purpose LLM into a focused Conventional-Commits-style message drafter.
-4. Build an interactive CLI loop: show the draft, let the user accept, edit, or regenerate it.
-5. Wire the loop up to actually run `git commit -m "..."` — but only after the user explicitly confirms.
+1. **Set up** `uv`, a free-tier LLM API key, and a small project — all in one place, before any building starts.
+2. **Capture** a staged `git diff --staged` with Python's `subprocess` module and read its output as text.
+3. **Design** a system prompt that turns a general-purpose LLM into a focused Conventional-Commits-style message drafter.
+4. **Build** an interactive CLI loop: show the draft, let the user accept, edit, or regenerate it.
+5. **Execute** the loop's `git commit -m "..."` step — but only after the user explicitly confirms.
 
 ## Where to run this
 
@@ -40,6 +40,7 @@ This assumes Python 101 and enough comfort with git to know what `git add` and `
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abderrahim-lectures/python-data-analysis-course/blob/main/examples/commit-message-agent/notebook.ipynb)
 [![Open In Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/abderrahim-lectures/python-data-analysis-course/blob/main/examples/commit-message-agent/notebook.ipynb)
 
+**opencode** *(optional)* — a free, open-source AI coding agent that runs in your terminal. If you'd rather have an agent write and run this project for you than type the code yourself, install it with `curl -fsSL https://opencode.ai/install | bash` (or `npm install -g opencode-ai`) and point it at this repo with the same API key from Setup below. It's optional — this project's whole point is building it yourself, so treat it as a bonus, not a shortcut.
 ## Setup
 
 Everything you need before you write a line of the drafter itself: a real Python, a free API key, and a small project to hold both.
@@ -454,3 +455,4 @@ Built something you're proud of? [`examples/student-projects/`](https://github.c
 Welcome to writing Python outside the browser. 🎓
 
 <ProjectProgressCheckbox projectId="2027-commit-message-agent" />
+

docs/projects/dependency-freshness-checker/index.md

@@ -23,10 +23,10 @@ This is optional and ungraded — a good fit once you've finished Python 101 (no
 
 ## 🎯 What you'll do
 
-1. Parse a real `pyproject.toml` file and extract its dependency list.
-2. Query PyPI's public JSON API to find each dependency's current published version.
-3. Compare your pinned/installed version against the latest, using real semantic-version parsing — not naive string comparison.
-4. Print a clean, categorized freshness report (up to date / outdated / unable to check).
+1. **Parse** a real `pyproject.toml` file and **extract** its dependency list.
+2. **Query** PyPI's public JSON API to find each dependency's current published version.
+3. **Compare** your pinned/installed version against the latest, using real semantic-version parsing — not naive string comparison.
+4. **Produce** a clean, categorized freshness report (up to date / outdated / unable to check).
 
 ## Where to run this
 
@@ -290,3 +290,4 @@ Built something you're proud of? [`examples/student-projects/`](https://github.c
 Welcome to writing Python outside the browser. 🎓
 
 <ProjectProgressCheckbox projectId="2027-dependency-freshness-checker" />
+

docs/projects/docs-qa-bot/index.md

@@ -25,11 +25,11 @@ This is optional and ungraded. See [Real-World Projects](/docs/projects) for the
 
 ## 🎯 What you'll do
 
-1. Create a Discord bot application and grab its token from Discord's free developer portal.
-2. Install `uv`, set up a project, and add `discord.py` alongside the same embedding/retrieval libraries from the RAG App project.
-3. Reuse and adapt the RAG App's retrieval pipeline over a folder of documentation instead of personal notes.
-4. Wire a `discord.py` message handler so the bot retrieves relevant docs and generates an answer whenever it's mentioned.
-5. Invite the bot to a test server and ask it real questions, end to end.
+1. **Create** a Discord bot application and **retrieve** its token from Discord's free developer portal.
+2. **Set up** a project with `uv`, adding `discord.py` alongside the same embedding/retrieval libraries from the RAG App project.
+3. **Adapt** the RAG App's retrieval pipeline to a folder of documentation instead of personal notes.
+4. **Implement** a `discord.py` message handler so the bot retrieves relevant docs and generates an answer whenever it's mentioned.
+5. **Evaluate** the bot on a test server by asking it real questions, end to end.
 
 ## Where to run this
 
@@ -45,6 +45,7 @@ That said, the RAG pipeline *underneath* the bot — chunking, embedding, retrie
 [![Open In Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/abderrahim-lectures/python-data-analysis-course/blob/main/examples/docs-qa-bot/notebook.ipynb)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/abderrahim-lectures/python-data-analysis-course/main?filepath=examples%2Fdocs-qa-bot%2Fnotebook.ipynb)
 
+**opencode** *(optional)* — a free, open-source AI coding agent that runs in your terminal. If you'd rather have an agent write and run this project for you than type the code yourself, install it with `curl -fsSL https://opencode.ai/install | bash` (or `npm install -g opencode-ai`) and point it at this repo with the same API key from Setup below. It's optional — this project's whole point is building it yourself, so treat it as a bonus, not a shortcut.
 ## Setup
 
 Everything in this section only needs to happen once, before you write a line of the bot itself: installing `uv`, creating the Discord bot application and grabbing its token, getting a free LLM key, and setting up the project. Every step after this one assumes all of it is already done.
@@ -486,3 +487,4 @@ Built something you're proud of? [`examples/student-projects/`](https://github.c
 Welcome to writing Python outside the browser. 🎓
 
 <ProjectProgressCheckbox projectId="docs-qa-bot" />
+