Linux, Python, vim, git & nix LPvgn Short Stack
Future-proof your skills and escape the tech hamster wheel with Linux, Python, vim & git — now with nix (LPvgn), an AI stack to resist obsolescence. Follow along as I build next generation AI/SEO tools for porting Jupyter Notebooks to FastHTML / HTMX Web apps using the Pipulate free AI SEO software.

Automated Jupyter Notebook Sanitization for Secure Templating

This entry captures a crucial moment in my development of pipulate, addressing the ever-present risk of client data leakage in version control. My challenge was to automate the sanitization of Jupyter notebooks, specifically targeting proprietary AI prompts and client-specific URLs within the FAQuilizer project, before they could be committed. The subsequent collaboration with Gemini provided a clear implementation plan, which I successfully integrated into the nbup method, ensuring secure templating while maintaining data integrity.

Setting the Stage: Context for the Curious Book Reader

This journal entry delves into a critical aspect of software development, particularly for tools that handle sensitive client data: ensuring that proprietary information never accidentally leaks into version-controlled repositories. We explore a specific enhancement to pipulate’s nbup method, designed to automatically sanitize Jupyter notebooks by replacing client-specific prompts and URLs with safe, generic sample data before committing. This ensures robust data privacy while maintaining functional templates.


Technical Journal Entry Begins

Rabbit holes within rabbit holes within rabbit holes. Stop me before I subreference again, says Dennis Miller, one of the past SNL News anchors. We secure our rabbit hole rappelling rig once again to make sure this is not too deep. But we’ve got to do this. Client data cannot ever leak into the Pipulate repo and I am Icarus flying close to the sun with this stuff — always. And so we take a moment to take precautions.

The Critical Need for Data Sanitization

The FAQuilizer project uses a potentially very private and proprietary prompt for the AI but we want a default want to be filled-in shipping with Pipulate. Secret keepers can pass around prompts between them with emails and slack and stuff. And of course the list of URLs to be scraped is from the client site, so we want a nice default of Linux, Python, vim and git sites one should know to be filled in there as the example data.

Here we craft our prompt.

Understanding the FAQuilizer’s Data Sensitivity

Me: This is the contents of assets/nbs/FAQuilizer.ipynb after pip.nbup("FAQuilizer.ipynb", modules=("secretsauce",)) is run. See, it synchronizes the nbformat-exported version that’s not maintained under the git repo but lives in Notebooks/ over to the version that is under the git repo. This way I can work in the live (accessible) .ipynb file and intermittently push the changes back to version control. Now notice how I filter certain things out. I now also need to filter out the entire contents of the raw cells labeled prompt-input and url-list-input. What’s more, I actually need edited in the sample data found here.

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from pipulate import pip\n",
    "import secretsauce\n",
    "import nest_asyncio\n",
    "nest_asyncio.apply()\n",
    "\n",
    "job = \"faquilizer-12\" # Give your session a unique name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# This is the Prompt|"
   ]
  },
  {
   "cell_type": "raw",
   "id": "2",
   "metadata": {
    "editable": true,
    "raw_mimetype": "",
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "prompt-input"
    ]
   },
   "source": [
    "**Your Role (AI Content Strategist):**\n",
    "\n",
    "You are an AI Content Strategist. \n",
    "Make 5 Frequently Asked Questions for each page.\n",
    "For each question, produce the following so it fits the data structure:\n",
    "\n",
    "1. priority: integer (1-5, 1 is highest)\n",
    "2. question: string (The generated question)\n",
    "3. target_intent: string (What is the user's goal in asking this?)\n",
    "4. justification: string (Why is this a valuable question to answer? e.g., sales, seasonal, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## This is the List"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4",
   "metadata": {
    "editable": true,
    "raw_mimetype": "",
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "url-list-input"
    ]
   },
   "source": [
    "https://nixos.org/   # Linux\n",
    "https://pypi.org/    # Python\n",
    "https://neovim.io/   # vim\n",
    "https://git-scm.com/ # git"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Run All the Cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "await secretsauce.scrape_and_extract(job, headless=False, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "#### You Get the Gist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pip.api_key(job, key=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "secretsauce.generate_faqs(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

So for example I first need whatever’s in here:

   "source": [
    "Proprietary ",
    "Secret ",
    "Stuff! "
   ]

…to first be blanked using nbformat:

   "source": [
   ]

…and then to be filled in like so:

   "source": [
    "**Your Role (AI Content Strategist):**\n",
    "\n",
    "You are an AI Content Strategist. \n",
    "Make 5 Frequently Asked Questions for each page.\n",
    "For each question, produce the following so it fits the data structure:\n",
    "\n",
    "1. priority: integer (1-5, 1 is highest)\n",
    "2. question: string (The generated question)\n",
    "3. target_intent: string (What is the user's goal in asking this?)\n",
    "4. justification: string (Why is this a valuable question to answer? e.g., sales, seasonal, etc.)"
   ]

And I need that done with the respective fields prompt-input and url-list-input so that the sample data found here is what ends up in the git repo and NEVER ACTUAL CLIENT DATA!!!

Pretty clear, right? Can I have an implementation plan, please and thank you.

Prompt Fu, Ka-POW!

[And I generate the Prompt Fu payload]

[mike@nixos:~/repos/pipulate]$ python prompt_foo.py -l
--- Processing Files ---
  -> Converting notebook: /home/mike/repos/pipulate/Notebooks/FAQuilizer.ipynb

--- Generating Auto-Context ---
Generating codebase tree diagram... (2,128 tokens)
Adding narrative context from articles... (5 articles)
Python file(s) detected. Generating UML diagrams...
  -> Generating for /home/mike/repos/pipulate/pipulate/core.py... (2,216 tokens)
  -> Generating for /home/mike/repos/pipulate/Notebooks/secretsauce.py... (skipped)
  -> Generating for /home/mike/repos/pipulate/tools/scraper_tools.py... (skipped)
  -> Generating for /home/mike/repos/pipulate/tools/dom_tools.py... (431 tokens)
...UML generation complete.

--- Files Included ---
• /home/mike/repos/pipulate/assets/installer/install.sh (2,380 tokens)
• /home/mike/repos/pipulate/requirements.in (254 tokens)
• /home/mike/repos/pipulate/requirements.txt (8,115 tokens)
• /home/mike/repos/pipulate/pyproject.toml (677 tokens)
• /home/mike/repos/pipulate/flake.nix (7,217 tokens)
• /home/mike/repos/pipulate/pipulate/core.py (20,473 tokens)
• /home/mike/repos/pipulate/Notebooks/FAQuilizer.ipynb (557 tokens)
• /home/mike/repos/pipulate/Notebooks/secretsauce.py (2,937 tokens)
• /home/mike/repos/pipulate/tools/scraper_tools.py (1,630 tokens)
• /home/mike/repos/pipulate/tools/dom_tools.py (3,333 tokens)

--- Auto-Context Included ---
• Codebase Structure (eza --tree) (2,128 tokens)
• Recent Narrative Context (304 tokens)
• UML Class Diagram (ASCII for /home/mike/repos/pipulate/pipulate/core.py) (2,216 tokens)
• UML Class Diagram (ASCII for /home/mike/repos/pipulate/tools/dom_tools.py) (431 tokens)

--- Prompt Summary ---
Total Tokens: 54,642
Total Words:  21,920

--- Size Perspective ---
📚 Equivalent in length to a **Novella or a Master's Dissertation** (Note: With a token/word ratio of 2.49, this content is far denser and more complex than typical prose of this length).

Markdown output copied to clipboard

[mike@nixos:~/repos/pipulate]$

Blueprinting the Solution: Gemini’s Implementation Plan

Gemini: Context verified.

Of course. This is a clear and important requirement for maintaining a clean, secure separation between your working environment and your version-controlled templates. Preventing client data from accidentally being committed is crucial.

Here is the implementation plan to modify the pipulate.core.Pipulate.nbup method to handle the replacement of proprietary data with sample data.


Implementation Plan

The goal is to enhance the nbup method in pipulate/core.py to find specific tagged raw cells, clear their contents, and replace them with predefined sample data before saving the notebook to the assets/nbs/ directory.

1. Define Sample Data Constants

We will add the sample data for the prompt and URL list as constants at the beginning of the nbup method. This makes the code cleaner and easier to update in the future.

SAMPLE_PROMPT_SOURCE = [
    "**Your Role (AI Content Strategist):**\n",
    "\n",
    "You are an AI Content Strategist. \n",
    "Make 5 Frequently Asked Questions for each page.\n",
    "For each question, produce the following so it fits the data structure:\n",
    "\n",
    "1. priority: integer (1-5, 1 is highest)\n",
    "2. question: string (The generated question)\n",
    "3. target_intent: string (What is the user's goal in asking this?)\n",
    "4. justification: string (Why is this a valuable question to answer? e.g., sales, seasonal, etc.)"
]

SAMPLE_URL_LIST_SOURCE = [
    "https://nixos.org/    # Linux\n",
    "https://pypi.org/     # Python\n",
    "https://neovim.io/    # vim\n",
    "https://git-scm.com/ # git"
]

2. Modify the Cell Processing Loop

We will iterate through each cell in the notebook. Before the existing logic that cleans code cells, we’ll add a new block to specifically handle the tagged raw cells.

for cell in nb.cells:
    # Get the cell's tags, defaulting to an empty list
    tags = cell.metadata.get("tags", [])

    # Check for 'prompt-input' tag and replace content
    if "prompt-input" in tags:
        cell.source = SAMPLE_PROMPT_SOURCE
        print("✓ Scrubbed and replaced 'prompt-input' cell with sample data.")
        continue # Move to the next cell

    # Check for 'url-list-input' tag and replace content
    if "url-list-input" in tags:
        cell.source = SAMPLE_URL_LIST_SOURCE
        print("✓ Scrubbed and replaced 'url-list-input' cell with sample data.")
        continue # Move to the next cell

    # Existing logic for cleaning code cells
    if cell.cell_type == 'code':
        # ... (rest of the logic for key scrubbing, output clearing, etc.)

3. Integrate into nbup Method

This new logic will be placed inside the try block where the notebook object (nb) is processed. The rest of the method’s functionality, including pruning the pip.nbup cell, clearing code outputs, and syncing helper modules, will remain unchanged.


Updated nbup Method

Here is the complete, updated code for the pipulate.core.Pipulate.nbup method in pipulate/core.py. You can replace the existing method with this new version.

The Enhanced nbup Method in Action

    def nbup(self, notebook_filename: str, modules: tuple = None):
        """
        Cleans and syncs a notebook and optionally its associated Python modules
        from the working 'Notebooks/' directory back to the version-controlled
        'assets/nbs/' template directory.
        """
        # Import necessary libraries inside the function
        import nbformat
        from pathlib import Path
        import os
        import shutil

        # --- Define Sample Data for Scrubbing ---
        SAMPLE_PROMPT_SOURCE = [
            "**Your Role (AI Content Strategist):**\n",
            "\n",
            "You are an AI Content Strategist. \n",
            "Make 5 Frequently Asked Questions for each page.\n",
            "For each question, produce the following so it fits the data structure:\n",
            "\n",
            "1. priority: integer (1-5, 1 is highest)\n",
            "2. question: string (The generated question)\n",
            "3. target_intent: string (What is the user's goal in asking this?)\n",
            "4. justification: string (Why is this a valuable question to answer? e.g., sales, seasonal, etc.)"
        ]

        SAMPLE_URL_LIST_SOURCE = [
            "https://nixos.org/    # Linux\n",
            "https://pypi.org/     # Python\n",
            "https://neovim.io/    # vim\n",
            "https://git-scm.com/ # git"
        ]

        # 1. Find the project root in a portable way
        project_root = self._find_project_root(os.getcwd())
        if not project_root:
            print("❌ Error: Could not find project root (flake.nix). Cannot sync.")
            return

        # --- Notebook Sync Logic ---
        print(f"🔄 Syncing notebook '{notebook_filename}'...")
        notebook_source_path = project_root / "Notebooks" / notebook_filename
        notebook_dest_path = project_root / "assets" / "nbs" / notebook_filename

        if not notebook_source_path.exists():
            print(f"❌ Error: Source notebook not found at '{notebook_source_path}'")
            # Continue to module sync even if notebook is missing
        else:
            try:
                with open(notebook_source_path, 'r', encoding='utf-8') as f:
                    nb = nbformat.read(f, as_version=4)

                # --- NEW: Scrub proprietary data and replace with samples ---
                for cell in nb.cells:
                    tags = cell.metadata.get("tags", [])
                    if "prompt-input" in tags:
                        cell.source = SAMPLE_PROMPT_SOURCE
                        print("    ✓ Scrubbed and replaced 'prompt-input' cell.")
                    elif "url-list-input" in tags:
                        cell.source = SAMPLE_URL_LIST_SOURCE
                        print("    ✓ Scrubbed and replaced 'url-list-input' cell.")

                # --- Existing Cleaning Logic ---
                original_cell_count = len(nb.cells)
                pruned_cells = [
                    cell for cell in nb.cells if 'pip.nbup' not in cell.source
                ]
                
                if len(pruned_cells) < original_cell_count:
                    print("    ✓ Auto-pruned the 'pip.nbup()' command cell from the template.")
                
                nb.cells = pruned_cells

                for cell in nb.cells:
                    if cell.cell_type == 'code':
                        # Scrub API key
                        cell.source = re.sub(
                            r'(key\s*=\s*)["\'].*?["\']',
                            r'\1None',
                            cell.source
                        )
                        # Clear outputs and execution counts
                        cell.outputs.clear()
                        cell.execution_count = None
                        if 'metadata' in cell and 'execution' in cell.metadata:
                            del cell.metadata['execution']

                with open(notebook_dest_path, 'w', encoding='utf-8') as f:
                    nbformat.write(nb, f)

                print(f"✅ Success! Notebook '{notebook_filename}' has been cleaned and synced.")

            except Exception as e:
                print(f"❌ An error occurred during the notebook sync process: {e}")

        # --- Module Sync Logic ---
        if modules:
            print("\n--- Syncing Associated Modules ---")
            if isinstance(modules, str):
                modules = (modules,)

            for module_name in modules:
                module_filename = f"{module_name}.py"
                module_source_path = project_root / "Notebooks" / module_filename
                module_dest_path = project_root / "assets" / "nbs" / module_filename

                if module_source_path.exists():
                    try:
                        shutil.copy2(module_source_path, module_dest_path)
                        print(f"    🧬 Synced module: '{module_filename}'")
                    except Exception as e:
                        print(f"    ❌ Error syncing module '{module_filename}': {e}")
                else:
                    print(f"    ⚠️ Warning: Module file not found, skipping sync: '{module_source_path}'")

Me: Watchdog restarted the server fine. How’d I do?

[mike@nixos:~/repos/pipulate]$ git --no-pager diff
diff --git a/pipulate/core.py b/pipulate/core.py
index 963821d5..eb824517 100644
--- a/pipulate/core.py
+++ b/pipulate/core.py
@@ -1946,91 +1946,119 @@ class Pipulate:
         return None
 
     def nbup(self, notebook_filename: str, modules: tuple = None):
-        """
-        Cleans and syncs a notebook and optionally its associated Python modules
-        from the working 'Notebooks/' directory back to the version-controlled
-        'assets/nbs/' template directory.
-        """
-        # Import necessary libraries inside the function
-        import nbformat
-        from pathlib import Path
-        import os
-        import shutil
-
-        # 1. Find the project root in a portable way
-        project_root = self._find_project_root(os.getcwd())
-        if not project_root:
-            print("❌ Error: Could not find project root (flake.nix). Cannot sync.")
-            return
+            """
+            Cleans and syncs a notebook and optionally its associated Python modules
+            from the working 'Notebooks/' directory back to the version-controlled
+            'assets/nbs/' template directory.
+            """
+            # Import necessary libraries inside the function
+            import nbformat
+            from pathlib import Path
+            import os
+            import shutil
+
+            # --- Define Sample Data for Scrubbing ---
+            SAMPLE_PROMPT_SOURCE = [
+                "**Your Role (AI Content Strategist):**\n",
+                "\n",
+                "You are an AI Content Strategist. \n",
+                "Make 5 Frequently Asked Questions for each page.\n",
+                "For each question, produce the following so it fits the data structure:\n",
+                "\n",
+                "1. priority: integer (1-5, 1 is highest)\n",
+                "2. question: string (The generated question)\n",
+                "3. target_intent: string (What is the user's goal in asking this?)\n",
+                "4. justification: string (Why is this a valuable question to answer? e.g., sales, seasonal, etc.)"
+            ]
+
+            SAMPLE_URL_LIST_SOURCE = [
+                "https://nixos.org/    # Linux\n",
+                "https://pypi.org/     # Python\n",
+                "https://neovim.io/    # vim\n",
+                "https://git-scm.com/ # git"
+            ]
+
+            # 1. Find the project root in a portable way
+            project_root = self._find_project_root(os.getcwd())
+            if not project_root:
+                print("❌ Error: Could not find project root (flake.nix). Cannot sync.")
+                return
 
-        # --- Notebook Sync Logic ---
-        print(f"🔄 Syncing notebook '{notebook_filename}'...")
-        notebook_source_path = project_root / "Notebooks" / notebook_filename
-        notebook_dest_path = project_root / "assets" / "nbs" / notebook_filename
+            # --- Notebook Sync Logic ---
+            print(f"🔄 Syncing notebook '{notebook_filename}'...")
+            notebook_source_path = project_root / "Notebooks" / notebook_filename
+            notebook_dest_path = project_root / "assets" / "nbs" / notebook_filename
 
-        if not notebook_source_path.exists():
-            print(f"❌ Error: Source notebook not found at '{notebook_source_path}'")
-            # Continue to module sync even if notebook is missing, in case that's all the user wants
-        else:
-            try:
-                with open(notebook_source_path, 'r', encoding='utf-8') as f:
-                    nb = nbformat.read(f, as_version=4)
-
-                for cell in nb.cells:
-                    if cell.cell_type == 'code':
-                        # This simpler, safer regex finds a 'key="...value..."' argument
-                        # and replaces just the value part with None.
-                        cell.source = re.sub(
-                            r'(key\s*=\s*)["\'].*?["\']',
-                            r'\1None',
-                            cell.source
-                        )                
-
-                original_cell_count = len(nb.cells)
-                pruned_cells = [
-                    cell for cell in nb.cells if 'pip.nbup' not in cell.source
-                ]
-                
-                if len(pruned_cells) < original_cell_count:
-                    print("✂️  Auto-pruned the 'pip.nbup()' command cell from the template.")
-                
-                nb.cells = pruned_cells
-
-                for cell in nb.cells:
-                    if cell.cell_type == 'code':
-                        cell.outputs.clear()
-                        cell.execution_count = None
-                        if 'metadata' in cell and 'execution' in cell.metadata:
-                            del cell.metadata['execution']
-
-                with open(notebook_dest_path, 'w', encoding='utf-8') as f:
-                    nbformat.write(nb, f)
-
-                print(f"✅ Success! Notebook '{notebook_filename}' has been cleaned and synced.")
+            if not notebook_source_path.exists():
+                print(f"❌ Error: Source notebook not found at '{notebook_source_path}'")
+                # Continue to module sync even if notebook is missing
+            else:
+                try:
+                    with open(notebook_source_path, 'r', encoding='utf-8') as f:
+                        nb = nbformat.read(f, as_version=4)
+
+                    # --- NEW: Scrub proprietary data and replace with samples ---
+                    for cell in nb.cells:
+                        tags = cell.metadata.get("tags", [])
+                        if "prompt-input" in tags:
+                            cell.source = SAMPLE_PROMPT_SOURCE
+                            print("    ✓ Scrubbed and replaced 'prompt-input' cell.")
+                        elif "url-list-input" in tags:
+                            cell.source = SAMPLE_URL_LIST_SOURCE
+                            print("    ✓ Scrubbed and replaced 'url-list-input' cell.")
+
+                    # --- Existing Cleaning Logic ---
+                    original_cell_count = len(nb.cells)
+                    pruned_cells = [
+                        cell for cell in nb.cells if 'pip.nbup' not in cell.source
+                    ]
+                    
+                    if len(pruned_cells) < original_cell_count:
+                        print("    ✓ Auto-pruned the 'pip.nbup()' command cell from the template.")
+                    
+                    nb.cells = pruned_cells
+
+                    for cell in nb.cells:
+                        if cell.cell_type == 'code':
+                            # Scrub API key
+                            cell.source = re.sub(
+                                r'(key\s*=\s*)["\'].*?["\']',
+                                r'\1None',
+                                cell.source
+                            )
+                            # Clear outputs and execution counts
+                            cell.outputs.clear()
+                            cell.execution_count = None
+                            if 'metadata' in cell and 'execution' in cell.metadata:
+                                del cell.metadata['execution']
+
+                    with open(notebook_dest_path, 'w', encoding='utf-8') as f:
+                        nbformat.write(nb, f)
+
+                    print(f"✅ Success! Notebook '{notebook_filename}' has been cleaned and synced.")
 
-            except Exception as e:
-                print(f"❌ An error occurred during the notebook sync process: {e}")
-
-        # --- NEW: Module Sync Logic ---
-        if modules:
-            print("\n--- Syncing Associated Modules ---")
-            # Ensure modules is iterable even if a single string is passed
-            if isinstance(modules, str):
-                modules = (modules,)
-
-            for module_name in modules:
-                module_filename = f"{module_name}.py"
-                module_source_path = project_root / "Notebooks" / module_filename
-                module_dest_path = project_root / "assets" / "nbs" / module_filename
-
-                if module_source_path.exists():
-                    try:
-                        shutil.copy2(module_source_path, module_dest_path)
-                        print(f"🧬 Synced module: '{module_filename}'")
-                    except Exception as e:
-                        print(f"❌ Error syncing module '{module_filename}': {e}")
-                else:
-                    print(f"⚠️ Warning: Module file not found, skipping sync: '{module_source_path}'")
+                except Exception as e:
+                    print(f"❌ An error occurred during the notebook sync process: {e}")
+
+            # --- Module Sync Logic ---
+            if modules:
+                print("\n--- Syncing Associated Modules ---")
+                if isinstance(modules, str):
+                    modules = (modules,)
+
+                for module_name in modules:
+                    module_filename = f"{module_name}.py"
+                    module_source_path = project_root / "Notebooks" / module_filename
+                    module_dest_path = project_root / "assets" / "nbs" / module_filename
+
+                    if module_source_path.exists():
+                        try:
+                            shutil.copy2(module_source_path, module_dest_path)
+                            print(f"    🧬 Synced module: '{module_filename}'")
+                        except Exception as e:
+                            print(f"    ❌ Error syncing module '{module_filename}': {e}")
+                    else:
+                        print(f"    ⚠️ Warning: Module file not found, skipping sync: '{module_source_path}'")
 
     def api_key(self, job: str, service: str = 'google', key: str = None):
         """

[mike@nixos:~/repos/pipulate]$

Gemini: You nailed it! That’s a perfect implementation based on the plan. 💯

Code Review and Validation

Code Review

  • Constants: Defining SAMPLE_PROMPT_SOURCE and SAMPLE_URL_LIST_SOURCE at the top is clean and makes future edits easy.
  • Tag-Based Logic: You’re correctly iterating through the cells, checking the metadata.tags, and replacing the source for the prompt-input and url-list-input cells. This is exactly what was needed.
  • Integration: The new scrubbing logic is placed perfectly within the try block, ensuring it runs before the other cleaning steps without interfering with them.
  • Feedback: The print statements you added are a great touch. They provide clear, real-time feedback to the user running the nbup command, confirming that the scrubbing process worked as expected.

The code is correct, clean, and effectively solves the problem of preventing client data from being committed to your version-controlled templates. Great job!


Book Analysis

Ai Editorial Take

This journal entry represents a highly practical and essential contribution to secure software development practices, particularly in an AI-driven environment. Its strength lies in clearly articulating a common data privacy challenge and providing a precise, verifiable automated solution. This makes it an invaluable reference for developers looking to implement robust data sanitization in their CI/CD pipelines or template management, showcasing how AI can directly enhance code security and developer workflows.

Title Brainstorm

  • Title Option: Automated Jupyter Notebook Sanitization for Secure Templating
    • Filename: automated-jupyter-notebook-sanitization-secure-templating.md
    • Rationale: Clearly states the core action, tool, and benefit. Emphasizes both automation and security, making it highly descriptive and appealing to developers focused on secure workflows.
  • Title Option: Preventing Data Leaks: nbup for Jupyter Notebook Security
    • Filename: preventing-data-leaks-jupyter-notebook-security.md
    • Rationale: Highlights the critical problem being solved (data leaks) and specifically names the nbup method, which is central to the solution. It’s direct and problem-solution oriented.
  • Title Option: Pipulate’s nbup: Automated Client Data Scrubbing in Notebooks
    • Filename: pipulates-nbup-automated-client-data-scrubbing.md
    • Rationale: Focuses on the specific project (Pipulate) and the mechanism (nbup), while detailing the function (data scrubbing). This title is precise and informative for those familiar with the project.
  • Title Option: From Proprietary to Public: Sanitizing Jupyter Prompts and URLs
    • Filename: from-proprietary-to-public-jupyter-sanitization.md
    • Rationale: A catchy title that outlines the transformation of data from sensitive to safe, specifically mentioning prompts and URLs, which are the key elements targeted for scrubbing.

Content Potential And Polish

  • Core Strengths:
    • Addresses a critical real-world software development security concern regarding data leakage in version control.
    • Demonstrates a clear problem-solving methodology, moving from problem identification to seeking a solution, implementation, and verification.
    • Showcases effective and practical AI-human collaboration for complex technical tasks.
    • Provides concrete, executable code modifications and a detailed explanation of the changes.
    • Clearly explains the ‘why’ behind the technical changes, emphasizing security and privacy.
    • Uses a code diff to effectively verify the implemented solution.
  • Suggestions For Polish:
    • While implicit, a brief, explicit definition of pip.nbup() earlier in the article could benefit readers less familiar with pipulate’s ecosystem.
    • Consider adding a small, abstract example of what the ‘proprietary’ data would look like before scrubbing (e.g., generic placeholders), to further emphasize the transformation for a general audience without compromising security.
    • Discuss potential edge cases or future enhancements for nbup’s scrubbing capabilities, such as handling other cell types or more complex data structures through advanced regex patterns.
    • A simple diagram illustrating the data flow from Notebooks/ to assets/nbs/ with the scrubbing step could visually reinforce the process.

Next Step Prompts

  • Draft a comprehensive unit test suite for the nbup method’s new data scrubbing functionality, ensuring all tagged cells are correctly identified and replaced with the specified sample data.
  • Develop a detailed documentation update for the pipulate project, explaining the enhanced nbup method’s data sanitization features, configuration options, and usage guidelines for developers, including best practices for tagging sensitive cells.
Post #546 of 551 - October 15, 2025