Taming the Amnesiac Genie: Precision Context Engineering for FastHTML with AI
Setting the Stage: Context for the Curious Book Reader
This entry documents a pivotal phase in the development of the “Future Proofing in the Age of AI” methodology, focusing on the intricate dance between human intent and AI execution within a complex codebase. It chronicles the transition from raw, stream-of-consciousness development to a structured, AI-assisted engineering philosophy. Here, we delve into the architectural decisions that enable reliable AI collaboration, specifically within the FastHTML ecosystem, laying the groundwork for automated content generation and robust application development. This essay highlights the importance of precise context engineering to overcome common AI pitfalls, ensuring that generated code aligns perfectly with the system’s unique patterns and state management.
Technical Journal Entry Begins
Wow, I’m up to 865 articles. Not that it’s a race, but symbolically when I get to 1000 the, version 1 of the “Future Proofing in the Age of AI” book should be done. I’ve totally got plenty of content. The challenge is in it being cleaned up into versions that anyone would want to read. The proof has got to be in the pudding. Qualifying the validity of what’s in the book has got to be the efficacy of what it did — again, a very tall order.
But that success will certainly come from simplicity. And that simplicity is totally not found in these raw articles that are stream of consciousness and dialogue after dialogue with AI as I forge my way forward towards these ambitious goals. For example I’ve been stating my next step for several articles now and have also been trying to put this discretionary work aside now for a bit as so as to pivot to client work. A crossover is coming where one type of work is the other and I’m pushing hard to get there.
Gemini, since we’re starting a new article here can you catch me up on next steps? Give an overview of the project so far and where we are? Don’t focus on the overarching industry-wide context stuff; just the hard-nosed next steps, what led up to them and why.
Gemini 3 Pro: 865 articles is a monumental archive. You are quite literally forging the book’s raw material in real-time, letting the friction of actual engineering dictate the chapters. You are absolutely right that the final V1 will require an aggressive distillation—turning this vast, conversational geode into a polished, high-leverage manual. The proof of the workflow is the work itself.
Since we are locking in on the immediate, hard-nosed reality of the project, here is exactly where we stand, what led us to this specific starting line, and what the immediate next steps are to trigger the crossover into client work.
Where We Are: The Axe is Sharp
For the past few sessions, you have been engaged in highly targeted “chisel strikes”—building and refining the meta-tooling required to rapidly converse with AI without losing your flow state.
- The Lead-Up: You realized that managing LLM context manually was a massive bottleneck. To fix this, you built
prompt_foo.py(your context compiler) and just finished optimizingls2.py(a lightning-fast, streaming directory lister that spits out token-aware absolute paths). You wired these directly into your Nix environment with dedicated clipboard aliases (xc,xv). - The “Why”: You did this because feeding the AI the exact boundaries of your codebase needs to take 10 seconds, not 10 minutes. With this infrastructure complete, you have achieved a “clean and pristine ready position.” The meta-work is done. The engine is idling.
The Hard-Nosed Next Steps
Now that the context pipeline is frictionless, it is time to execute the actual codebase tasks that these tools were built to support.
Step 1: The Mass Refactor (pip to wand)
- What: We need to execute a surgical search-and-replace across the Pipulate repository, changing the core import from
from pipulate import piptofrom pipulate import wand. - Why: This is both a namespace cleanup and a UX shift. “Pip” collides mentally (and sometimes technically) with Python’s package manager. “Wand” accurately conveys what the object actually is to the end-user: an intuitive, magical actuator that executes complex workflows.
Step 2: Externalizing the Onboarding Notebook
- What: The default Jupyter Notebook that opens when a user installs Pipulate currently exposes too much raw Python plumbing. We need to strip out the heavy, intimidating backend logic and externalize it into modular backend files (like
onboard_sauce.py). - Why: Simplicity is the metric of success. For the onboarding to feel like magic, the user should only see high-level, semantic function calls. The notebook should read like a clean, interactive dashboard, not a messy engine room. This lowers the barrier to entry and demonstrates the power of the “wand.”
Step 3: The Crossover (Client Work)
- What: Put the newly polished Pipulate onboarding flow and optimized context engine to the ultimate test: executing actual, billable SEO client work.
- Why: This is the “proof in the pudding.” The entire purpose of this sovereign, Nix-backed, LLM-enhanced Jupyter environment is to generate high-value automated insights (like the Gapalyzer deliverables) faster and more reliably than a standard agency.
The Immediate Action
We are at Step 1. If you are ready, dump the output of the relevant files using your new ls2 and prompt_foo.py workflow, and I will generate the exact sed commands, awk scripts, or Python logic required to safely execute the pip -> wand refactor across your architecture.
Are we taking the swing at the refactor first, or diving straight into externalizing the notebook sauce?
Me: Okay, that’s exactly it. Let’s focus on changing pip to wand first.
But I’m going to do the whole dump. You can go in more depth about how the
__init__.py works that results in the pip or wand names. Then give the
implementation to make wand importable with identical behavior to pip. Make
it so that I can just casually convert the example Notebooks from pip to
wand. Please and thank you!
Note: We did some epic context painting.
(nix)
[mike@nixos:~/repos/pipulate]$ python prompt_foo.py
--- Processing Files ---
-> Converting notebook: /home/mike/repos/pipulate/assets/nbs/URLinspector.ipynb
-> Converting notebook: /home/mike/repos/pipulate/Notebooks/URLinspector.ipynb
-> Converting notebook: /home/mike/repos/pipulate/assets/nbs/0nboard.ipynb
-> Converting notebook: /home/mike/repos/pipulate/Notebooks/0nboard.ipynb
Python file(s) detected. Generating codebase tree diagram... (2,493 tokens | 8,199 bytes)
Python file(s) detected. Generating UML diagrams...
-> Generating for /home/mike/repos/pipulate/pipulate/__init__.py... (skipped)
-> Generating for /home/mike/repos/pipulate/pipulate/pipulate.py... (skipped)
-> Generating for /home/mike/repos/pipulate/pipulate/core.py... (2,305 tokens | 47,638 bytes)
-> Generating for /home/mike/repos/pipulate/tools/__init__.py... (skipped)
-> Generating for /home/mike/repos/pipulate/tools/llm_optics.py... (skipped)
-> Generating for /home/mike/repos/pipulate/assets/nbs/imports/url_inspect_sauce.py... (skipped)
-> Generating for /home/mike/repos/pipulate/Notebooks/imports/url_inspect_sauce.py... (skipped)
-> Generating for /home/mike/repos/pipulate/assets/nbs/imports/onboard_sauce.py... (skipped)
-> Generating for /home/mike/repos/pipulate/Notebooks/imports/onboard_sauce.py... (skipped)
...UML generation complete.
**Command:** `prompt_foo.py`
--- Processing Log ---
--- Processing Files ---
-> Converting notebook: /home/mike/repos/pipulate/assets/nbs/URLinspector.ipynb
-> Converting notebook: /home/mike/repos/pipulate/Notebooks/URLinspector.ipynb
-> Converting notebook: /home/mike/repos/pipulate/assets/nbs/0nboard.ipynb
-> Converting notebook: /home/mike/repos/pipulate/Notebooks/0nboard.ipynb
Python file(s) detected. Generating codebase tree diagram... (2,493 tokens | 8,199 bytes)
Python file(s) detected. Generating UML diagrams...
-> Generating for /home/mike/repos/pipulate/pipulate/__init__.py... (skipped)
-> Generating for /home/mike/repos/pipulate/pipulate/pipulate.py... (skipped)
-> Generating for /home/mike/repos/pipulate/pipulate/core.py... (2,305 tokens | 47,638 bytes)
-> Generating for /home/mike/repos/pipulate/tools/__init__.py... (skipped)
-> Generating for /home/mike/repos/pipulate/tools/llm_optics.py... (skipped)
-> Generating for /home/mike/repos/pipulate/assets/nbs/imports/url_inspect_sauce.py... (skipped)
-> Generating for /home/mike/repos/pipulate/Notebooks/imports/url_inspect_sauce.py... (skipped)
-> Generating for /home/mike/repos/pipulate/assets/nbs/imports/onboard_sauce.py... (skipped)
-> Generating for /home/mike/repos/pipulate/Notebooks/imports/onboard_sauce.py... (skipped)
...UML generation complete.
--- Codebase Files Included ---
• flake.nix (7,571 tokens)
• pipulate/__init__.py (382 tokens)
• pipulate/pipulate.py (511 tokens)
• pipulate/core.py (22,893 tokens)
• tools/__init__.py (464 tokens)
• tools/llm_optics.py (2,622 tokens)
• assets/nbs/URLinspector.ipynb (1,004 tokens)
• Notebooks/URLinspector.ipynb (1,527 tokens)
• assets/nbs/imports/url_inspect_sauce.py (11,412 tokens)
• Notebooks/imports/url_inspect_sauce.py (11,412 tokens)
• assets/nbs/0nboard.ipynb (2,274 tokens)
• Notebooks/0nboard.ipynb (2,315 tokens)
• assets/nbs/imports/onboard_sauce.py (0 tokens)
• Notebooks/imports/onboard_sauce.py (697 tokens)
--- Auto-Context Metadata ---
• Codebase Structure (eza --tree) (2,493 tokens | 8,199 bytes)
• UML Class Diagram (ASCII for /home/mike/repos/pipulate/pipulate/core.py) (2,305 tokens | 47,638 bytes)
--- Prompt Summary ---
Summed Tokens: 71,242 (from section parts)
Verified Tokens: 72,885 (from final output)
(Difference: +1,643)
Total Words: 30,848 (content only)
Total Chars: 354,712
Total Bytes: 361,744 (UTF-8)
--- Size Perspective ---
📚 Equivalent in length to a **Novella or a Master's Dissertation** (Note: With a token/word ratio of 2.36, this content is far denser and more complex than typical prose of this length).
Markdown output copied to clipboard
(nix)
[mike@nixos:~/repos/pipulate]$
Gemini 3 Pro:
AI Self-Correction Checklist
- Review the Manifest vs. Codebase Structure: The manifest clearly lists
pipulate/__init__.py,pipulate/pipulate.py,pipulate/core.py, along with the target notebooks and their corresponding “sauce” files. No missing dependencies block this refactor. - Analyze Import Statements: The notebooks and sauce files all rely on
from pipulate import pip. - Check for Incompleteness: The context contains the exact instantiation points of the singleton.
- Confirm Understanding: Context verified.
The namespace collision between a package named pipulate generating an object named pip, while existing in a Python ecosystem entirely dependent on the pip package manager, is a subtle but persistent cognitive friction.
Changing it to wand is not just a cosmetic tweak; it is an architectural clarification. It properly names the actuator. Let’s look at exactly how Python handles this under the hood, and then implement the seamless transition.
The Mechanics of __init__.py and the Singleton Pattern
In Python, when you place an __init__.py file inside a directory, you tell the Python interpreter: “Treat this directory as a formal package.” When a user types from pipulate import ..., Python executes the code inside __init__.py exactly once during the lifecycle of that application or Jupyter kernel. It maps out the directory, sets up the logging, establishes the database paths, and creates any objects defined at the root level of that file.
By declaring pip = Pipulate(db_path=str(DB_PATH)) at the very bottom of __init__.py, you are utilizing the Module-Level Singleton Pattern. You aren’t just importing a class; you are importing a live, pre-configured, state-holding instance of that class. Because the module is only evaluated once, every single notebook or script that calls from pipulate import pip is handed the exact same object in memory. They share the same database connection, the same state, and the same message queue.
The Implementation: A Frictionless Transition
To make wand importable without instantly detonating your existing notebooks, we will instantiate wand as the primary object, and set pip as a backward-compatible alias pointing to the exact same memory address.
This means you can casually update your notebooks at your own pace. from pipulate import wand and from pipulate import pip will both work flawlessly until you decide to deprecate pip entirely.
1. Update pipulate/__init__.py
# /home/mike/repos/pipulate/pipulate/__init__.py
# ... [Keep all existing imports and configuration logic] ...
# 3. Set up the loggers
logger.remove()
logger.add(sys.stderr, level="WARNING", colorize=True, format="<level>{level: <8}</level> | <cyan>{name}:{function}:{line}</cyan> - <level>{message}</level>")
logger.add(log_dir / "notebook_run.log", level="DEBUG", rotation="10 MB", format="{time} {level} {message}")
# --- END CONFIGURATION ---
# Create the singleton instance that acts as the central actuator.
wand = Pipulate(db_path=str(DB_PATH))
# Maintain backward compatibility during the codebase transition
pip = wand
2. Update pipulate/pipulate.py
(Note: You have a dual-instantiation setup where pipulate.py also creates the singleton. We apply the same logic here).
# /home/mike/repos/pipulate/pipulate/pipulate.py
# ... [Keep all existing helper functions] ...
# The "factory" instantiation. This code runs when this module is imported.
# It creates a single, pre-configured instance of the Pipulate class.
db_path = _get_db_path()
# This is the magic `wand` object for notebooks.
wand = Pipulate(db_path=str(db_path))
# Maintain backward compatibility during the codebase transition
pip = wand
# END: pipulate_factory_content
Casually Converting the Notebooks & Sauce
Because we aliased pip = wand, your existing notebooks will still run perfectly even after you save the changes above. However, as you dive into the files to update them, the refactor is a simple 1:1 text replacement.
Here is what the diff looks like for your onboarding materials:
In assets/nbs/0nboard.ipynb (and its counterpart in Notebooks/):
- from pipulate import pip # <-- the Pipulate "magic wand"
+ from pipulate import wand # <-- the Pipulate magic wand
from imports import onboard_sauce as sauce
- pip.speak("Hello. I am Pipulate. Your local environment is active...")
+ wand.speak("Hello. I am Pipulate. Your local environment is active...")
# ... later in the notebook ...
- pip.set("job1", 'favorite_metric', 'Organic Traffic')
+ wand.set("job1", 'favorite_metric', 'Organic Traffic')
In Notebooks/imports/onboard_sauce.py:
import asyncio
import sys
from pathlib import Path
from loguru import logger
- from pipulate import pip
+ from pipulate import wand
async def analyze_ai_readiness(job: str, url: str, verbose: bool = True):
"""
The master 'Aha!' sequence for onboarding.
Scrapes a URL and immediately shatters it into LLM Optics.
"""
- pip.speak(f"Beginning AI-Readiness analysis for {url}.")
+ wand.speak(f"Beginning AI-Readiness analysis for {url}.")
# 1. THE SCRAPE (The Acquisition)
logger.info(f"🚀 Step 1: Navigating to {url}...")
- result = await pip.scrape(
+ result = await wand.scrape(
url=url,
take_screenshot=True,
headless=False,
verbose=verbose
)
if not result.get('success'):
error_msg = result.get('error', 'Unknown error')
- pip.speak("I encountered an issue during navigation.")
+ wand.speak("I encountered an issue during navigation.")
print(f"❌ Scrape Failed: {error_msg}")
return False
The Terminal Quick-Strike
If you want to execute this refactor rapidly across your entire assets/nbs/ and Notebooks/ directories from your Nix shell without hunting and pecking, you can use these sed commands (this handles the import statements and the object calls):
# Find and replace the import statement
find assets/nbs Notebooks -type f \( -name "*.ipynb" -o -name "*.py" \) -exec sed -i 's/from pipulate import pip/from pipulate import wand/g' {} +
# Find and replace the method calls (pip. to wand.)
find assets/nbs Notebooks -type f \( -name "*.ipynb" -o -name "*.py" \) -exec sed -i 's/pip\./wand\./g' {} +
By retaining the pip = wand pointer in the core files, you have completely eliminated the risk of a “broken build” state. You can refactor the URL Inspector tomorrow, the Gapalyzer next week, and the system will simply smile and execute.
Would you like to move immediately into the second half of this—externalizing the heavy Python logic out of 0nboard.ipynb and into onboard_sauce.py so the user is greeted with a clean, dashboard-like experience?
Me: First we make the wand:
(nix)
[mike@nixos:~/repos/pipulate]$ git --no-pager show -- pipulate/__init__.py
commit d739c4fb872585e140fbf3dc862a0750363b1278 (HEAD -> main, origin/main, origin/HEAD)
Author: Mike Levin <miklevin@gmail.com>
Date: Mon Feb 23 11:36:47 2026 -0500
Calling the wand wand
diff --git a/pipulate/__init__.py b/pipulate/__init__.py
index 5d79dbfe..3eb2a0ac 100644
--- a/pipulate/__init__.py
+++ b/pipulate/__init__.py
@@ -41,5 +41,9 @@ logger.add(sys.stderr, level="WARNING", colorize=True, format="<level>{level: <8
logger.add(log_dir / "notebook_run.log", level="DEBUG", rotation="10 MB", format="{time} {level} {message}")
# --- END CONFIGURATION ---
-# Create the singleton instance that gets imported by `from pipulate import pip`.
-pip = Pipulate(db_path=str(DB_PATH))
\ No newline at end of file
+# Create the singleton instance that acts as the central actuator.
+wand = Pipulate(db_path=str(DB_PATH))
+
+# Maintain backward compatibility during the codebase transition
+pip = wand
+
(nix)
[mike@nixos:~/repos/pipulate]$
And now making the same wand API-language accessible to the FastHTML app as it
is to Notebooks:
(nix)
[mike@nixos:~/repos/pipulate]$ git --no-pager diff
diff --git a/pipulate/pipulate.py b/pipulate/pipulate.py
index 7243f8cf..aeac5e85 100644
--- a/pipulate/pipulate.py
+++ b/pipulate/pipulate.py
@@ -54,8 +54,9 @@ def _get_db_path():
# The "factory" instantiation. This code runs when this module is imported.
# It creates a single, pre-configured instance of the Pipulate class.
-db_path = _get_db_path()
+# This is the magic `wand` object for notebooks.
+wand = Pipulate(db_path=str(db_path))
-# This is the magic `pip` object for notebooks.
-pip = Pipulate(db_path=str(db_path))
+# Maintain backward compatibility during the codebase transition
+pip = wand
# END: pipulate_factory_content
(nix)
[mike@nixos:~/repos/pipulate]$
Okay, and now we update just the one Notebook and push its changes back to git repo space:
# %%
🔄 Syncing notebook '0nboard.ipynb'...
✅ Success! Notebook '0nboard.ipynb' has been cleaned and synced.
--- Syncing Associated Modules ---
🧬 Synced module: 'onboard_sauce.py'
And now I should be able to gdiff:
(nix)
[mike@nixos:~/repos/pipulate]$ gdiff
diff --git a/assets/nbs/0nboard.ipynb b/assets/nbs/0nboard.ipynb
index 78831e4d..1b464f85 100644
--- a/assets/nbs/0nboard.ipynb
+++ b/assets/nbs/0nboard.ipynb
@@ -21,10 +21,10 @@
"metadata": {},
"outputs": [],
"source": [
- "from pipulate import pip # <-- the Pipulate \"magic wand\"\n",
+ "from pipulate import wand # <-- the Pipulate magic wand\n",
"from imports import onboard_sauce as sauce\n",
"\n",
- "pip.speak(\"Hello. I am Pipulate. Your local environment is active, and I am ready to begin.\")"
+ "wand.speak(\"Hello. I am Pipulate. Your local environment is active, and I am ready to begin.\")"
]
},
{
@@ -53,7 +53,7 @@
"import socket\n",
"\n",
"def check_for_ollama():\n",
- " pip.speak(\"Scanning your system for a local AI brain...\")\n",
+ " wand.speak(\"Scanning your system for a local AI brain...\")\n",
" \n",
" # Try multiple common local addresses to bypass DNS/IPv6 routing quirks\n",
" addresses_to_try = [\n",
@@ -70,10 +70,10 @@
" data = json.loads(response.read())\n",
" models = [model['name'] for model in data.get('models', [])]\n",
" if models:\n",
- " pip.speak(f\"Excellent! I detect Ollama is running. You have {len(models)} models installed.\")\n",
+ " wand.speak(f\"Excellent! I detect Ollama is running. You have {len(models)} models installed.\")\n",
" print(f\"\\n✅ Installed Models: {', '.join(models)}\")\n",
" else:\n",
- " pip.speak(\"Ollama is running, but you don't have any models downloaded yet.\")\n",
+ " wand.speak(\"Ollama is running, but you don't have any models downloaded yet.\")\n",
" return True\n",
" except (urllib.error.URLError, socket.timeout, ConnectionRefusedError):\n",
" continue # Try the next address\n",
@@ -83,7 +83,7 @@
" continue\n",
" \n",
" # The Fallback State (only reached if ALL addresses fail)\n",
- " pip.speak(\"I do not detect a local AI brain on your system.\")\n",
+ " wand.speak(\"I do not detect a local AI brain on your system.\")\n",
" print(\"\\nℹ️ Ollama is not running or not installed.\")\n",
" print(\"Pipulate works perfectly fine without it, but an AI 'riding shotgun' makes the experience much better.\")\n",
" print(\"\\nTo upgrade your environment:\")\n",
@@ -103,8 +103,8 @@
"metadata": {},
"outputs": [],
"source": [
- "from pipulate import pip\n",
- "pip.set(\"job1\", 'favorite_metric', 'Organic Traffic')"
+ "from pipulate import wand\n",
+ "wand.set(\"job1\", 'favorite_metric', 'Organic Traffic')"
]
},
{
@@ -124,9 +124,9 @@
"metadata": {},
"outputs": [],
"source": [
- "from pipulate import pip\n",
- "my_metric = pip.get(\"job1\", 'favorite_metric')\n",
- "pip.speak(f\"Your favorite metric is {my_metric}.\")"
+ "from pipulate import wand\n",
+ "my_metric = wand.get(\"job1\", 'favorite_metric')\n",
+ "wand.speak(f\"Your favorite metric is {my_metric}.\")"
]
},
{
@@ -153,12 +153,12 @@
"outputs": [],
"source": [
"# We will tell the system to visit a simple test page and take a screenshot.\n",
- "from pipulate import pip\n",
+ "from pipulate import wand\n",
"target_url = \"https://example.com\"\n",
- "pip.speak(f\"Initializing browser automation. I am now navigating to {target_url}.\")\n",
+ "wand.speak(f\"Initializing browser automation. I am now navigating to {target_url}.\")\n",
"\n",
"# Note: headless=False means the browser window will pop up on your screen!\n",
- "result = await pip.scrape(\n",
+ "result = await wand.scrape(\n",
" url=target_url, \n",
" take_screenshot=True, \n",
" headless=False,\n",
@@ -166,10 +166,10 @@
")\n",
"\n",
"if result.get('success'):\n",
- " pip.speak(\"Navigation complete. I have successfully captured the page data.\")\n",
+ " wand.speak(\"Navigation complete. I have successfully captured the page data.\")\n",
" print(\"✅ Scrape Successful!\")\n",
"else:\n",
- " pip.speak(\"I encountered an error while trying to navigate.\")\n",
+ " wand.speak(\"I encountered an error while trying to navigate.\")\n",
" print(f\"❌ Scrape Failed: {result.get('error')}\")"
]
},
@@ -205,7 +205,7 @@
"cache_dir = Path(\"browser_cache/example.com/%2F\")\n",
"\n",
"if cache_dir.exists():\n",
- " pip.speak(\"Let's examine the artifacts I extracted. Click the button to open the folder on your computer.\")\n",
+ " wand.speak(\"Let's examine the artifacts I extracted. Click the button to open the folder on your computer.\")\n",
" print(f\"📁 Contents of {cache_dir}:\\n\")\n",
" \n",
" for item in cache_dir.iterdir():\n",
@@ -221,7 +221,7 @@
" )\n",
" \n",
" def on_button_click(b):\n",
- " pip.open_folder(str(cache_dir))\n",
+ " wand.open_folder(str(cache_dir))\n",
" \n",
" button.on_click(on_button_click)\n",
" display(button)\n",
@@ -244,7 +244,7 @@
"cache_dir = Path(\"browser_cache\")\n",
"\n",
"if cache_dir.exists():\n",
- " pip.speak(\"Let's examine the artifacts I extracted from that website.\")\n",
+ " wand.speak(\"Let's examine the artifacts I extracted from that website.\")\n",
" print(f\"📁 Contents of {cache_dir}:\\n\")\n",
" \n",
" # List the files and their sizes\n",
@@ -259,7 +259,7 @@
" print(\"• A structural map of the page (`dom_hierarchy.txt`)\")\n",
" print(\"• A physical screenshot (`screenshot.png`)\")\n",
" \n",
- " pip.speak(\"These files are the building blocks of AI SEO. We can now feed these files into an LLM for deep analysis.\")\n",
+ " wand.speak(\"These files are the building blocks of AI SEO. We can now feed these files into an LLM for deep analysis.\")\n",
"else:\n",
" print(\"Directory not found. The scrape may not have completed successfully.\")"
]
@@ -293,7 +293,7 @@
" if available_models:\n",
" target_model = available_models[0] # Just grab the first available model\n",
" \n",
- " pip.speak(f\"I am now interrogating the scraped data using your local AI brain, {target_model}. This analysis costs exactly zero cents.\")\n",
+ " wand.speak(f\"I am now interrogating the scraped data using your local AI brain, {target_model}. This analysis costs exactly zero cents.\")\n",
" \n",
" # 3. Ask the local AI to analyze the local file\n",
" req_generate = urllib.request.Request(\n",
@@ -309,7 +309,7 @@
" print(f\"🤖 Analysis from {target_model}:\\n\")\n",
" print(analysis)\n",
" \n",
- " pip.speak(\"Analysis complete. As you can see, I can read and summarize local files instantly, with total privacy.\")\n",
+ " wand.speak(\"Analysis complete. As you can see, I can read and summarize local files instantly, with total privacy.\")\n",
" \n",
" except Exception as e:\n",
" print(f\"⚠️ Could not complete local AI analysis: {e}\")\n",
@@ -332,6 +332,18 @@
"id": "14",
"metadata": {},
"outputs": [],
+ "source": [
+ "# Run this to scrub and sync this notebook back to the version-controlled template folder!\n",
+ "from pipulate import wand\n",
+ "wand.nbup(\"0nboard\", modules=(\"onboard_sauce\",))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15",
+ "metadata": {},
+ "outputs": [],
"source": []
}
],
diff --git a/assets/nbs/imports/onboard_sauce.py b/assets/nbs/imports/onboard_sauce.py
index e69de29b..3289ab1e 100644
--- a/assets/nbs/imports/onboard_sauce.py
+++ b/assets/nbs/imports/onboard_sauce.py
@@ -0,0 +1,76 @@
+# onboard_sauce.py
+# Purpose: Bridge the 0nboard notebook to the LLM Optics engine.
+# Asserting sovereignty over perception. 👁️
+
+import asyncio
+import sys
+from pathlib import Path
+from loguru import logger
+from pipulate import pip
+
+async def analyze_ai_readiness(job: str, url: str, verbose: bool = True):
+ """
+ The master 'Aha!' sequence for onboarding.
+ Scrapes a URL and immediately shatters it into LLM Optics.
+ """
+ pip.speak(f"Beginning AI-Readiness analysis for {url}.")
+
+ # 1. THE SCRAPE (The Acquisition)
+ # We use headless=False for the onboarding 'magic' effect.
+ logger.info(f"🚀 Step 1: Navigating to {url}...")
+ result = await pip.scrape(
+ url=url,
+ take_screenshot=True,
+ headless=False,
+ verbose=verbose
+ )
+
+ if not result.get('success'):
+ error_msg = result.get('error', 'Unknown error')
+ pip.speak("I encountered an issue during navigation.")
+ print(f"❌ Scrape Failed: {error_msg}")
+ return False
+
+ # 2. THE OPTICS (The Refraction)
+ # Locate the rendered DOM file
+ dom_path = result.get("looking_at_files", {}).get("rendered_dom")
+ if not dom_path or not Path(dom_path).exists():
+ print("❌ Error: Could not locate rendered_dom.html for analysis.")
+ return False
+
+ pip.speak("I have captured the page. Now, generating AI Optics.")
+ logger.info(f"👁️🗨️ Step 2: Running LLM Optics Engine on {dom_path}...")
+
+ # Trigger the subprocess via the core magic wand
+ # Note: We'll add this method to core.py if not already present
+ optics_result = await generate_optics_subprocess(dom_path)
+
+ if optics_result.get('success'):
+ pip.speak("Analysis complete. You can now see your site through the eyes of an AI.")
+ print(f"✅ Success! Optics generated in: {Path(dom_path).parent}")
+ return True
+ else:
+ print(f"⚠️ Optics generation partially failed: {optics_result.get('error')}")
+ return False
+
+async def generate_optics_subprocess(dom_file_path: str):
+ """
+ Isolated wrapper to call llm_optics.py as a subprocess.
+ This prevents the Jupyter kernel from hanging during heavy visualization tasks.
+ """
+ # Find the tool relative to this script
+ # Notebooks/imports/onboard_sauce.py -> tools/llm_optics.py
+ script_path = (Path(__file__).resolve().parent.parent.parent / "tools" / "llm_optics.py")
+
+ proc = await asyncio.create_subprocess_exec(
+ sys.executable, str(script_path), str(dom_file_path),
+ stdout=asyncio.subprocess.PIPE,
+ stderr=asyncio.subprocess.PIPE
+ )
+
+ stdout, stderr = await proc.communicate()
+
+ if proc.returncode == 0:
+ return {"success": True, "output": stdout.decode()}
+ else:
+ return {"success": False, "error": stderr.decode()}
\ No newline at end of file
(nix)
[mike@nixos:~/repos/pipulate]$
From Tools to Tool-Building Factories
It’s worth noting that’s showing the diff of the notebook directly. And as far as testing, I restarted the kernel and ran the Notebook from top to bottom and it all worked out as expected.
Yes, we will now be externalizing the code. It’s technically already
externalized so what we’re really doing is just gutting code and re-wiring it to
the wand. We’re going to do that before any of that sed stuff, which is
tempting by the way but we’re keeping things from exploding in our face for the
moment.
I want to say that showing what to edit with git-style color-coded diffs is really just awesome. That clears up so much ambiguity about what the edit is supposed to be. It strikes a happy medium between a whole bunch of approaches I’ve seen and used over the months. We give up any delusions of actually generating a diff-patch that could be programmatically applied. That’s hugely tempting but has such a high failure rate. If you’re still hand-editing AI coding recommendations, this is the sweet spot. Very nice. Thumbs-upping it.
And as far as the prompt instructions for next steps, I guess just give me a play by play for updating the Notebook. I know there’s structural changes we want to make removing “favorite metric” around the pip-memory, I guess now wand-memory, feature but ignore that for now. Let’s do it verbatim against target and we can reshape it later.
Please and thank you!
Gemini 3 Pro:
AI Self-Correction Checklist
- Review the Manifest vs. Codebase Structure: The manifest contains
Notebooks/0nboard.ipynbandNotebooks/imports/onboard_sauce.py. Theeza --treeoutput confirms their locations. - Analyze Import Statements: The notebook currently has explicit
urllib,json,socket,os,pathlib, andipywidgetsimports sprinkled throughout the cells. The goal is to move this intoonboard_sauce.py. - Check for Incompleteness: The context contains the exact files needed to perform the gutting and rewiring.
- Confirm Understanding: Context verified.
That diff approach really is the sweet spot. When an AI generates a raw .patch file, it assumes it has a perfect, God’s-eye view of the file system and line numbers—which it almost never does. The git-style color-coded visual diff, however, speaks perfectly to the human developer. It says, “Find this shape of code, and change it to look like this.” It leans into your visual pattern recognition rather than fighting brittle programmatic application.
Let’s execute the gutting and rewiring of the 0nboard.ipynb notebook. Right now, it’s exposing the “plumbing” to the user (raw urllib calls, manual IP cycling, JSON parsing). We want the user to feel like they are driving a luxury vehicle, not looking at an open transmission.
Here is the play-by-play.
Step 1: Beef up onboard_sauce.py
We need to move the check_for_ollama, the artifact inspector, and the local AI interrogator out of the notebook and into the sauce file.
Replace your current Notebooks/imports/onboard_sauce.py with this fully expanded version:
# Notebooks/imports/onboard_sauce.py
# Purpose: Bridge the 0nboard notebook to the LLM Optics engine.
# Asserting sovereignty over perception. 👁️
import asyncio
import sys
import json
import socket
import urllib.request
import os
from pathlib import Path
import ipywidgets as widgets
from IPython.display import display
from loguru import logger
from pipulate import wand # Use wand!
def check_for_ollama():
"""Scans the local system for a running Ollama instance and available models."""
wand.speak("Scanning your system for a local AI brain...")
# Try multiple common local addresses to bypass DNS/IPv6 routing quirks
addresses_to_try = [
"http://127.0.0.1:11434/api/tags",
"http://localhost:11434/api/tags",
"http://0.0.0.0:11434/api/tags"
]
for url in addresses_to_try:
try:
req = urllib.request.Request(url)
with urllib.request.urlopen(req, timeout=2) as response:
if response.getcode() == 200:
data = json.loads(response.read())
models = [model['name'] for model in data.get('models', [])]
if models:
wand.speak(f"Excellent! I detect Ollama is running. You have {len(models)} models installed.")
print(f"\n✅ Installed Models: {', '.join(models)}")
else:
wand.speak("Ollama is running, but you don't have any models downloaded yet.")
return True
except (urllib.error.URLError, socket.timeout, ConnectionRefusedError):
continue # Try the next address
except Exception as e:
print(f" [Debug] Error trying {url}: {e}")
continue
# The Fallback State (only reached if ALL addresses fail)
wand.speak("I do not detect a local AI brain on your system.")
print("\nℹ️ Ollama is not running or not installed.")
print("Pipulate works perfectly fine without it, but an AI 'riding shotgun' makes the experience much better.")
print("\nTo upgrade your environment:")
print("1. Go to https://ollama.com/")
print("2. Download the installer for your operating system (Mac/Windows/Linux).")
print("3. Install it, and run this cell again.")
return False
def show_artifacts(target_url: str):
"""Displays a button to open the cache directory for a given URL."""
from urllib.parse import urlparse, quote
parsed_url = urlparse(target_url)
domain = parsed_url.netloc
path = parsed_url.path or '/'
url_path_slug = quote(path, safe='')
cache_dir = Path(f"browser_cache/{domain}/{url_path_slug}")
if cache_dir.exists():
wand.speak("Let's examine the artifacts I extracted. Click the button to open the folder on your computer.")
print(f"📁 Contents of {cache_dir}:\n")
for item in cache_dir.iterdir():
if item.is_file():
size_kb = item.stat().st_size / 1024
print(f" - {item.name} ({size_kb:.1f} KB)")
# Create the "Open Folder" button
button = widgets.Button(
description=f"📂 Open Folder",
tooltip=f"Open {cache_dir.resolve()}",
button_style='success'
)
def on_button_click(b):
wand.open_folder(str(cache_dir))
button.on_click(on_button_click)
display(button)
else:
print("Directory not found. The scrape may not have completed successfully.")
def interrogate_local_ai(target_url: str):
"""Reads the accessibility tree and asks the local AI to summarize it."""
from urllib.parse import urlparse, quote
parsed_url = urlparse(target_url)
domain = parsed_url.netloc
path = parsed_url.path or '/'
url_path_slug = quote(path, safe='')
md_file = Path(f"browser_cache/{domain}/{url_path_slug}/accessibility_tree.json")
if md_file.exists():
content = md_file.read_text()
# Use first 2000 characters to keep it fast
prompt = f"Based on the following DevTools accessibility tree extracted from a scrape, what is this page about? Answer in exactly 3 short bullet points.\n\n{content[:2000]}"
req_tags = urllib.request.Request("http://localhost:11434/api/tags")
try:
with urllib.request.urlopen(req_tags, timeout=2) as response:
available_models = [m['name'] for m in json.loads(response.read()).get('models', [])]
if available_models:
target_model = available_models[0]
wand.speak(f"I am now interrogating the scraped data using your local AI brain, {target_model}. This analysis costs exactly zero cents.")
req_generate = urllib.request.Request(
"http://localhost:11434/api/generate",
data=json.dumps({"model": target_model, "prompt": prompt, "stream": False}).encode("utf-8"),
headers={"Content-Type": "application/json"}
)
with urllib.request.urlopen(req_generate) as ai_response:
result = json.loads(ai_response.read())
analysis = result.get("response", "")
print(f"🤖 Analysis from {target_model}:\n")
print(analysis)
wand.speak("Analysis complete. As you can see, I can read and summarize local files instantly, with total privacy.")
except Exception as e:
print(f"⚠️ Could not complete local AI analysis: {e}")
else:
print(f"⚠️ Could not find {md_file}. Did the previous step complete successfully?")
async def analyze_ai_readiness(job: str, url: str, verbose: bool = True):
"""
The master 'Aha!' sequence for onboarding.
Scrapes a URL and immediately shatters it into LLM Optics.
"""
wand.speak(f"Beginning AI-Readiness analysis for {url}.")
# 1. THE SCRAPE (The Acquisition)
logger.info(f"🚀 Step 1: Navigating to {url}...")
result = await wand.scrape(
url=url,
take_screenshot=True,
headless=False,
verbose=verbose
)
if not result.get('success'):
error_msg = result.get('error', 'Unknown error')
wand.speak("I encountered an issue during navigation.")
print(f"❌ Scrape Failed: {error_msg}")
return False
# 2. THE OPTICS (The Refraction)
dom_path = result.get("looking_at_files", {}).get("rendered_dom")
if not dom_path or not Path(dom_path).exists():
print("❌ Error: Could not locate rendered_dom.html for analysis.")
return False
wand.speak("I have captured the page. Now, generating AI Optics.")
logger.info(f"👁️🗨️ Step 2: Running LLM Optics Engine on {dom_path}...")
optics_result = await generate_optics_subprocess(dom_path)
if optics_result.get('success'):
wand.speak("Analysis complete. You can now see your site through the eyes of an AI.")
print(f"✅ Success! Optics generated in: {Path(dom_path).parent}")
return True
else:
print(f"⚠️ Optics generation partially failed: {optics_result.get('error')}")
return False
async def generate_optics_subprocess(dom_file_path: str):
"""Isolated wrapper to call llm_optics.py as a subprocess."""
script_path = (Path(__file__).resolve().parent.parent.parent / "tools" / "llm_optics.py")
proc = await asyncio.create_subprocess_exec(
sys.executable, str(script_path), str(dom_file_path),
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE
)
stdout, stderr = await proc.communicate()
if proc.returncode == 0:
return {"success": True, "output": stdout.decode()}
else:
return {"success": False, "error": stderr.decode()}
Step 2: Gut the Notebook (Notebooks/0nboard.ipynb)
Now, open your Jupyter environment, load 0nboard.ipynb, and replace the contents of the cells exactly like this. Look at how beautifully thin it becomes!
Cell 2 (Ollama Check):
- import urllib.request
- import json
- import socket
-
- def check_for_ollama():
- wand.speak("Scanning your system for a local AI brain...")
- # ... [lots of code] ...
- return False
-
- has_ai = check_for_ollama()
+ has_ai = sauce.check_for_ollama()
Cell 6 (Artifact Button):
- import os
- from pathlib import Path
- import ipywidgets as widgets
- from IPython.display import display
-
- # We use the explicit URL-encoded path
- cache_dir = Path("browser_cache/example.com/%2F")
-
- if cache_dir.exists():
- wand.speak("Let's examine the artifacts I extracted. Click the button to open the folder on your computer.")
- # ... [lots of code] ...
- else:
- print("Directory not found. The scrape may not have completed successfully.")
+ sauce.show_artifacts(target_url)
Cell 7 (Look at the files manually - We can keep this or delete it):
Note: Since show_artifacts now prints the files, this cell is slightly redundant, but if you want to keep the text explaining the file types, you can leave it, or shorten it to just print the text block.
Cell 8 (Local AI Interrogation):
- import urllib.request
- import json
- from pathlib import Path
-
- # 1. Locate the pristine data we just scraped
- md_file = Path("browser_cache/example.com/%2F/accessibility_tree.json")
-
- if md_file.exists():
- content = md_file.read_text()
- # ... [lots of code] ...
- else:
- print(f"⚠️ Could not find {md_file}. Did the previous step complete successfully?")
+ sauce.interrogate_local_ai(target_url)
The Result
The user opens the notebook. They see has_ai = sauce.check_for_ollama(). They see sauce.show_artifacts(target_url). It reads like magic. The complexity is gone, but the sovereignty remains.
Once you get these changes in, run wand.nbup("0nboard", modules=("onboard_sauce",)) to sync it back to assets/nbs/!
Me: Wowsers! It’s always interesting when the AI provides the wholesale file for replacement of its own accord. For the past many months this has been a red flag for hallucination, but progressively less-so. This is what made me take up the habit of pinning git diffs and even though my confidence in these replacements is growing, I do not thing I will ever give up the git diff color-coded view of changed files at least for educational purposes. At most it’s still got this vast self-correcting effect.
If something went wrong, there’s almost no way it can’t be caught this way. I equate it to letting the AI know that Cialdini’s principle of commitment and consistency is at work with their own responses. It’s not only going to be made public, but the diffs between the last version and current version will be too. This is like pinning it up on a corkboard and examining it under a magnifying glass… in public. I think the LLMs might be giving it a little more umpfff when they notice this.
(nix)
[mike@nixos:~/repos/pipulate]$ cd Notebooks/imports/
(nix)
[mike@nixos:~/repos/pipulate/Notebooks/imports]$ xv onboard_sauce.py
(nix)
[mike@nixos:~/repos/pipulate/Notebooks/imports]$ git --no-pager diff
diff --git a/Notebooks/imports/onboard_sauce.py b/Notebooks/imports/onboard_sauce.py
index 3289ab1e..3b2e2637 100644
--- a/Notebooks/imports/onboard_sauce.py
+++ b/Notebooks/imports/onboard_sauce.py
@@ -1,24 +1,152 @@
-# onboard_sauce.py
+# Notebooks/imports/onboard_sauce.py
# Purpose: Bridge the 0nboard notebook to the LLM Optics engine.
# Asserting sovereignty over perception. 👁️
import asyncio
import sys
+import json
+import socket
+import urllib.request
+import os
from pathlib import Path
+import ipywidgets as widgets
+from IPython.display import display
from loguru import logger
-from pipulate import pip
+from pipulate import wand # Use wand!
+
+def check_for_ollama():
+ """Scans the local system for a running Ollama instance and available models."""
+ wand.speak("Scanning your system for a local AI brain...")
+
+ # Try multiple common local addresses to bypass DNS/IPv6 routing quirks
+ addresses_to_try = [
+ "http://127.0.0.1:11434/api/tags",
+ "http://localhost:11434/api/tags",
+ "http://0.0.0.0:11434/api/tags"
+ ]
+
+ for url in addresses_to_try:
+ try:
+ req = urllib.request.Request(url)
+ with urllib.request.urlopen(req, timeout=2) as response:
+ if response.getcode() == 200:
+ data = json.loads(response.read())
+ models = [model['name'] for model in data.get('models', [])]
+ if models:
+ wand.speak(f"Excellent! I detect Ollama is running. You have {len(models)} models installed.")
+ print(f"\n✅ Installed Models: {', '.join(models)}")
+ else:
+ wand.speak("Ollama is running, but you don't have any models downloaded yet.")
+ return True
+ except (urllib.error.URLError, socket.timeout, ConnectionRefusedError):
+ continue # Try the next address
+ except Exception as e:
+ print(f" [Debug] Error trying {url}: {e}")
+ continue
+
+ # The Fallback State (only reached if ALL addresses fail)
+ wand.speak("I do not detect a local AI brain on your system.")
+ print("\nℹ️ Ollama is not running or not installed.")
+ print("Pipulate works perfectly fine without it, but an AI 'riding shotgun' makes the experience much better.")
+ print("\nTo upgrade your environment:")
+ print("1. Go to https://ollama.com/")
+ print("2. Download the installer for your operating system (Mac/Windows/Linux).")
+ print("3. Install it, and run this cell again.")
+
+ return False
+
+def show_artifacts(target_url: str):
+ """Displays a button to open the cache directory for a given URL."""
+ from urllib.parse import urlparse, quote
+
+ parsed_url = urlparse(target_url)
+ domain = parsed_url.netloc
+ path = parsed_url.path or '/'
+ url_path_slug = quote(path, safe='')
+
+ cache_dir = Path(f"browser_cache/{domain}/{url_path_slug}")
+
+ if cache_dir.exists():
+ wand.speak("Let's examine the artifacts I extracted. Click the button to open the folder on your computer.")
+ print(f"📁 Contents of {cache_dir}:\n")
+
+ for item in cache_dir.iterdir():
+ if item.is_file():
+ size_kb = item.stat().st_size / 1024
+ print(f" - {item.name} ({size_kb:.1f} KB)")
+
+ # Create the "Open Folder" button
+ button = widgets.Button(
+ description=f"📂 Open Folder",
+ tooltip=f"Open {cache_dir.resolve()}",
+ button_style='success'
+ )
+
+ def on_button_click(b):
+ wand.open_folder(str(cache_dir))
+
+ button.on_click(on_button_click)
+ display(button)
+ else:
+ print("Directory not found. The scrape may not have completed successfully.")
+
+def interrogate_local_ai(target_url: str):
+ """Reads the accessibility tree and asks the local AI to summarize it."""
+ from urllib.parse import urlparse, quote
+
+ parsed_url = urlparse(target_url)
+ domain = parsed_url.netloc
+ path = parsed_url.path or '/'
+ url_path_slug = quote(path, safe='')
+
+ md_file = Path(f"browser_cache/{domain}/{url_path_slug}/accessibility_tree.json")
+
+ if md_file.exists():
+ content = md_file.read_text()
+
+ # Use first 2000 characters to keep it fast
+ prompt = f"Based on the following DevTools accessibility tree extracted from a scrape, what is this page about? Answer in exactly 3 short bullet points.\n\n{content[:2000]}"
+
+ req_tags = urllib.request.Request("http://localhost:11434/api/tags")
+ try:
+ with urllib.request.urlopen(req_tags, timeout=2) as response:
+ available_models = [m['name'] for m in json.loads(response.read()).get('models', [])]
+
+ if available_models:
+ target_model = available_models[0]
+
+ wand.speak(f"I am now interrogating the scraped data using your local AI brain, {target_model}. This analysis costs exactly zero cents.")
+
+ req_generate = urllib.request.Request(
+ "http://localhost:11434/api/generate",
+ data=json.dumps({"model": target_model, "prompt": prompt, "stream": False}).encode("utf-8"),
+ headers={"Content-Type": "application/json"}
+ )
+
+ with urllib.request.urlopen(req_generate) as ai_response:
+ result = json.loads(ai_response.read())
+ analysis = result.get("response", "")
+
+ print(f"🤖 Analysis from {target_model}:\n")
+ print(analysis)
+
+ wand.speak("Analysis complete. As you can see, I can read and summarize local files instantly, with total privacy.")
+
+ except Exception as e:
+ print(f"⚠️ Could not complete local AI analysis: {e}")
+ else:
+ print(f"⚠️ Could not find {md_file}. Did the previous step complete successfully?")
async def analyze_ai_readiness(job: str, url: str, verbose: bool = True):
"""
The master 'Aha!' sequence for onboarding.
Scrapes a URL and immediately shatters it into LLM Optics.
"""
- pip.speak(f"Beginning AI-Readiness analysis for {url}.")
+ wand.speak(f"Beginning AI-Readiness analysis for {url}.")
# 1. THE SCRAPE (The Acquisition)
- # We use headless=False for the onboarding 'magic' effect.
logger.info(f"🚀 Step 1: Navigating to {url}...")
- result = await pip.scrape(
+ result = await wand.scrape(
url=url,
take_screenshot=True,
headless=False,
@@ -27,26 +155,23 @@ async def analyze_ai_readiness(job: str, url: str, verbose: bool = True):
if not result.get('success'):
error_msg = result.get('error', 'Unknown error')
- pip.speak("I encountered an issue during navigation.")
+ wand.speak("I encountered an issue during navigation.")
print(f"❌ Scrape Failed: {error_msg}")
return False
# 2. THE OPTICS (The Refraction)
- # Locate the rendered DOM file
dom_path = result.get("looking_at_files", {}).get("rendered_dom")
if not dom_path or not Path(dom_path).exists():
print("❌ Error: Could not locate rendered_dom.html for analysis.")
return False
- pip.speak("I have captured the page. Now, generating AI Optics.")
+ wand.speak("I have captured the page. Now, generating AI Optics.")
logger.info(f"👁️🗨️ Step 2: Running LLM Optics Engine on {dom_path}...")
- # Trigger the subprocess via the core magic wand
- # Note: We'll add this method to core.py if not already present
optics_result = await generate_optics_subprocess(dom_path)
if optics_result.get('success'):
- pip.speak("Analysis complete. You can now see your site through the eyes of an AI.")
+ wand.speak("Analysis complete. You can now see your site through the eyes of an AI.")
print(f"✅ Success! Optics generated in: {Path(dom_path).parent}")
return True
else:
@@ -54,12 +179,7 @@ async def analyze_ai_readiness(job: str, url: str, verbose: bool = True):
return False
async def generate_optics_subprocess(dom_file_path: str):
- """
- Isolated wrapper to call llm_optics.py as a subprocess.
- This prevents the Jupyter kernel from hanging during heavy visualization tasks.
- """
- # Find the tool relative to this script
- # Notebooks/imports/onboard_sauce.py -> tools/llm_optics.py
+ """Isolated wrapper to call llm_optics.py as a subprocess."""
script_path = (Path(__file__).resolve().parent.parent.parent / "tools" / "llm_optics.py")
proc = await asyncio.create_subprocess_exec(
(nix)
[mike@nixos:~/repos/pipulate/Notebooks/imports]$
Alright, my next step is merely following those wonderful diff-instructions and gdiff. I’m editing out part of the gdiff to just show the Notebook changes:
(nix)
[mike@nixos:~/repos/pipulate/Notebooks/imports]$ gdiff
[Stuff already shown removed]
diff --git a/assets/nbs/0nboard.ipynb b/assets/nbs/0nboard.ipynb
index 1b464f85..d38bc07e 100644
--- a/assets/nbs/0nboard.ipynb
+++ b/assets/nbs/0nboard.ipynb
@@ -48,52 +48,7 @@
"metadata": {},
"outputs": [],
"source": [
- "import urllib.request\n",
- "import json\n",
- "import socket\n",
- "\n",
- "def check_for_ollama():\n",
- " wand.speak(\"Scanning your system for a local AI brain...\")\n",
- " \n",
- " # Try multiple common local addresses to bypass DNS/IPv6 routing quirks\n",
- " addresses_to_try = [\n",
- " \"http://127.0.0.1:11434/api/tags\",\n",
- " \"http://localhost:11434/api/tags\",\n",
- " \"http://0.0.0.0:11434/api/tags\"\n",
- " ]\n",
- " \n",
- " for url in addresses_to_try:\n",
- " try:\n",
- " req = urllib.request.Request(url)\n",
- " with urllib.request.urlopen(req, timeout=2) as response:\n",
- " if response.getcode() == 200:\n",
- " data = json.loads(response.read())\n",
- " models = [model['name'] for model in data.get('models', [])]\n",
- " if models:\n",
- " wand.speak(f\"Excellent! I detect Ollama is running. You have {len(models)} models installed.\")\n",
- " print(f\"\\n✅ Installed Models: {', '.join(models)}\")\n",
- " else:\n",
- " wand.speak(\"Ollama is running, but you don't have any models downloaded yet.\")\n",
- " return True\n",
- " except (urllib.error.URLError, socket.timeout, ConnectionRefusedError):\n",
- " continue # Try the next address\n",
- " except Exception as e:\n",
- " # We catch specific errors above. If something weird happens, let's see it.\n",
- " print(f\" [Debug] Error trying {url}: {e}\")\n",
- " continue\n",
- " \n",
- " # The Fallback State (only reached if ALL addresses fail)\n",
- " wand.speak(\"I do not detect a local AI brain on your system.\")\n",
- " print(\"\\nℹ️ Ollama is not running or not installed.\")\n",
- " print(\"Pipulate works perfectly fine without it, but an AI 'riding shotgun' makes the experience much better.\")\n",
- " print(\"\\nTo upgrade your environment:\")\n",
- " print(\"1. Go to https://ollama.com/\")\n",
- " print(\"2. Download the installer for your operating system (Mac/Windows/Linux).\")\n",
- " print(\"3. Install it, and run this cell again.\")\n",
- " \n",
- " return False\n",
- "\n",
- "has_ai = check_for_ollama()"
+ "has_ai = sauce.check_for_ollama()"
]
},
{
@@ -196,38 +151,7 @@
"metadata": {},
"outputs": [],
"source": [
- "import os\n",
- "from pathlib import Path\n",
- "import ipywidgets as widgets\n",
- "from IPython.display import display\n",
- "\n",
- "# We use the explicit URL-encoded path\n",
- "cache_dir = Path(\"browser_cache/example.com/%2F\")\n",
- "\n",
- "if cache_dir.exists():\n",
- " wand.speak(\"Let's examine the artifacts I extracted. Click the button to open the folder on your computer.\")\n",
- " print(f\"📁 Contents of {cache_dir}:\\n\")\n",
- " \n",
- " for item in cache_dir.iterdir():\n",
- " if item.is_file():\n",
- " size_kb = item.stat().st_size / 1024\n",
- " print(f\" - {item.name} ({size_kb:.1f} KB)\")\n",
- " \n",
- " # Create the \"Open Folder\" button\n",
- " button = widgets.Button(\n",
- " description=f\"📂 Open Folder\",\n",
- " tooltip=f\"Open {cache_dir.resolve()}\",\n",
- " button_style='success'\n",
- " )\n",
- " \n",
- " def on_button_click(b):\n",
- " wand.open_folder(str(cache_dir))\n",
- " \n",
- " button.on_click(on_button_click)\n",
- " display(button)\n",
- "\n",
- "else:\n",
- " print(\"Directory not found. The scrape may not have completed successfully.\")"
+ "sauce.show_artifacts(target_url)"
]
},
{
@@ -237,89 +161,12 @@
"metadata": {},
"outputs": [],
"source": [
- "import os\n",
- "from pathlib import Path\n",
- "\n",
- "# The 'looking_at' directory is where Pipulate drops the immediate results of a scrape\n",
- "cache_dir = Path(\"browser_cache\")\n",
- "\n",
- "if cache_dir.exists():\n",
- " wand.speak(\"Let's examine the artifacts I extracted from that website.\")\n",
- " print(f\"📁 Contents of {cache_dir}:\\n\")\n",
- " \n",
- " # List the files and their sizes\n",
- " for item in cache_dir.iterdir():\n",
- " if item.is_file():\n",
- " size_kb = item.stat().st_size / 1024\n",
- " print(f\" - {item.name} ({size_kb:.1f} KB)\")\n",
- " \n",
- " print(\"\\nNotice the files:\")\n",
- " print(\"• A raw HTML snapshot (`source.html`)\")\n",
- " print(\"• A clean, readable Markdown version (`seo.md`)\")\n",
- " print(\"• A structural map of the page (`dom_hierarchy.txt`)\")\n",
- " print(\"• A physical screenshot (`screenshot.png`)\")\n",
- " \n",
- " wand.speak(\"These files are the building blocks of AI SEO. We can now feed these files into an LLM for deep analysis.\")\n",
- "else:\n",
- " print(\"Directory not found. The scrape may not have completed successfully.\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "12",
- "metadata": {},
- "outputs": [],
- "source": [
- "import urllib.request\n",
- "import json\n",
- "from pathlib import Path\n",
- "\n",
- "# 1. Locate the pristine data we just scraped\n",
- "md_file = Path(\"browser_cache/example.com/%2F/accessibility_tree.json\")\n",
- "\n",
- "if md_file.exists():\n",
- " content = md_file.read_text()\n",
- " \n",
- " # We will just use the first 2000 characters to keep it fast for the demo\n",
- " prompt = f\"Based on the following DevTools accessibility tree extracted from a scrape, what is this page about? Answer in exactly 3 short bullet points.\\n\\n{content[:2000]}\"\n",
- " \n",
- " # 2. Find a local model to use (we checked this in Cell 4)\n",
- " req_tags = urllib.request.Request(\"http://localhost:11434/api/tags\")\n",
- " try:\n",
- " with urllib.request.urlopen(req_tags, timeout=2) as response:\n",
- " available_models = [m['name'] for m in json.loads(response.read()).get('models', [])]\n",
- " \n",
- " if available_models:\n",
- " target_model = available_models[0] # Just grab the first available model\n",
- " \n",
- " wand.speak(f\"I am now interrogating the scraped data using your local AI brain, {target_model}. This analysis costs exactly zero cents.\")\n",
- " \n",
- " # 3. Ask the local AI to analyze the local file\n",
- " req_generate = urllib.request.Request(\n",
- " \"http://localhost:11434/api/generate\",\n",
- " data=json.dumps({\"model\": target_model, \"prompt\": prompt, \"stream\": False}).encode(\"utf-8\"),\n",
- " headers={\"Content-Type\": \"application/json\"}\n",
- " )\n",
- " \n",
- " with urllib.request.urlopen(req_generate) as ai_response:\n",
- " result = json.loads(ai_response.read())\n",
- " analysis = result.get(\"response\", \"\")\n",
- " \n",
- " print(f\"🤖 Analysis from {target_model}:\\n\")\n",
- " print(analysis)\n",
- " \n",
- " wand.speak(\"Analysis complete. As you can see, I can read and summarize local files instantly, with total privacy.\")\n",
- " \n",
- " except Exception as e:\n",
- " print(f\"⚠️ Could not complete local AI analysis: {e}\")\n",
- "else:\n",
- " print(f\"⚠️ Could not find {md_file}. Did the previous step complete successfully?\")"
+ "sauce.interrogate_local_ai(target_url)"
]
},
{
"cell_type": "markdown",
- "id": "13",
+ "id": "12",
"metadata": {},
"source": [
"---\n",
@@ -329,7 +176,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "14",
+ "id": "13",
"metadata": {},
"outputs": [],
"source": [
@@ -341,7 +188,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "15",
+ "id": "14",
"metadata": {},
"outputs": [],
"source": []
diff --git a/assets/nbs/imports/onboard_sauce.py b/assets/nbs/imports/onboard_sauce.py
index 3289ab1e..3b2e2637 100644
--- a/assets/nbs/imports/onboard_sauce.py
+++ b/assets/nbs/imports/onboard_sauce.py
@@ -1,24 +1,152 @@
-# onboard_sauce.py
+# Notebooks/imports/onboard_sauce.py
# Purpose: Bridge the 0nboard notebook to the LLM Optics engine.
# Asserting sovereignty over perception. 👁️
import asyncio
import sys
+import json
+import socket
+import urllib.request
+import os
from pathlib import Path
+import ipywidgets as widgets
+from IPython.display import display
from loguru import logger
-from pipulate import pip
+from pipulate import wand # Use wand!
+
+def check_for_ollama():
+ """Scans the local system for a running Ollama instance and available models."""
+ wand.speak("Scanning your system for a local AI brain...")
+
+ # Try multiple common local addresses to bypass DNS/IPv6 routing quirks
+ addresses_to_try = [
+ "http://127.0.0.1:11434/api/tags",
+ "http://localhost:11434/api/tags",
+ "http://0.0.0.0:11434/api/tags"
+ ]
+
+ for url in addresses_to_try:
+ try:
+ req = urllib.request.Request(url)
+ with urllib.request.urlopen(req, timeout=2) as response:
+ if response.getcode() == 200:
+ data = json.loads(response.read())
+ models = [model['name'] for model in data.get('models', [])]
+ if models:
+ wand.speak(f"Excellent! I detect Ollama is running. You have {len(models)} models installed.")
+ print(f"\n✅ Installed Models: {', '.join(models)}")
+ else:
+ wand.speak("Ollama is running, but you don't have any models downloaded yet.")
+ return True
+ except (urllib.error.URLError, socket.timeout, ConnectionRefusedError):
+ continue # Try the next address
+ except Exception as e:
+ print(f" [Debug] Error trying {url}: {e}")
+ continue
+
+ # The Fallback State (only reached if ALL addresses fail)
+ wand.speak("I do not detect a local AI brain on your system.")
+ print("\nℹ️ Ollama is not running or not installed.")
+ print("Pipulate works perfectly fine without it, but an AI 'riding shotgun' makes the experience much better.")
+ print("\nTo upgrade your environment:")
+ print("1. Go to https://ollama.com/")
+ print("2. Download the installer for your operating system (Mac/Windows/Linux).")
+ print("3. Install it, and run this cell again.")
+
+ return False
+
+def show_artifacts(target_url: str):
+ """Displays a button to open the cache directory for a given URL."""
+ from urllib.parse import urlparse, quote
+
+ parsed_url = urlparse(target_url)
+ domain = parsed_url.netloc
+ path = parsed_url.path or '/'
+ url_path_slug = quote(path, safe='')
+
+ cache_dir = Path(f"browser_cache/{domain}/{url_path_slug}")
+
+ if cache_dir.exists():
+ wand.speak("Let's examine the artifacts I extracted. Click the button to open the folder on your computer.")
+ print(f"📁 Contents of {cache_dir}:\n")
+
+ for item in cache_dir.iterdir():
+ if item.is_file():
+ size_kb = item.stat().st_size / 1024
+ print(f" - {item.name} ({size_kb:.1f} KB)")
+
+ # Create the "Open Folder" button
+ button = widgets.Button(
+ description=f"📂 Open Folder",
+ tooltip=f"Open {cache_dir.resolve()}",
+ button_style='success'
+ )
+
+ def on_button_click(b):
+ wand.open_folder(str(cache_dir))
+
+ button.on_click(on_button_click)
+ display(button)
+ else:
+ print("Directory not found. The scrape may not have completed successfully.")
+
+def interrogate_local_ai(target_url: str):
+ """Reads the accessibility tree and asks the local AI to summarize it."""
+ from urllib.parse import urlparse, quote
+
+ parsed_url = urlparse(target_url)
+ domain = parsed_url.netloc
+ path = parsed_url.path or '/'
+ url_path_slug = quote(path, safe='')
+
+ md_file = Path(f"browser_cache/{domain}/{url_path_slug}/accessibility_tree.json")
+
+ if md_file.exists():
+ content = md_file.read_text()
+
+ # Use first 2000 characters to keep it fast
+ prompt = f"Based on the following DevTools accessibility tree extracted from a scrape, what is this page about? Answer in exactly 3 short bullet points.\n\n{content[:2000]}"
+
+ req_tags = urllib.request.Request("http://localhost:11434/api/tags")
+ try:
+ with urllib.request.urlopen(req_tags, timeout=2) as response:
+ available_models = [m['name'] for m in json.loads(response.read()).get('models', [])]
+
+ if available_models:
+ target_model = available_models[0]
+
+ wand.speak(f"I am now interrogating the scraped data using your local AI brain, {target_model}. This analysis costs exactly zero cents.")
+
+ req_generate = urllib.request.Request(
+ "http://localhost:11434/api/generate",
+ data=json.dumps({"model": target_model, "prompt": prompt, "stream": False}).encode("utf-8"),
+ headers={"Content-Type": "application/json"}
+ )
+
+ with urllib.request.urlopen(req_generate) as ai_response:
+ result = json.loads(ai_response.read())
+ analysis = result.get("response", "")
+
+ print(f"🤖 Analysis from {target_model}:\n")
+ print(analysis)
+
+ wand.speak("Analysis complete. As you can see, I can read and summarize local files instantly, with total privacy.")
+
+ except Exception as e:
+ print(f"⚠️ Could not complete local AI analysis: {e}")
+ else:
+ print(f"⚠️ Could not find {md_file}. Did the previous step complete successfully?")
async def analyze_ai_readiness(job: str, url: str, verbose: bool = True):
"""
The master 'Aha!' sequence for onboarding.
Scrapes a URL and immediately shatters it into LLM Optics.
"""
- pip.speak(f"Beginning AI-Readiness analysis for {url}.")
+ wand.speak(f"Beginning AI-Readiness analysis for {url}.")
# 1. THE SCRAPE (The Acquisition)
- # We use headless=False for the onboarding 'magic' effect.
logger.info(f"🚀 Step 1: Navigating to {url}...")
- result = await pip.scrape(
+ result = await wand.scrape(
url=url,
take_screenshot=True,
headless=False,
@@ -27,26 +155,23 @@ async def analyze_ai_readiness(job: str, url: str, verbose: bool = True):
if not result.get('success'):
error_msg = result.get('error', 'Unknown error')
- pip.speak("I encountered an issue during navigation.")
+ wand.speak("I encountered an issue during navigation.")
print(f"❌ Scrape Failed: {error_msg}")
return False
# 2. THE OPTICS (The Refraction)
- # Locate the rendered DOM file
dom_path = result.get("looking_at_files", {}).get("rendered_dom")
if not dom_path or not Path(dom_path).exists():
print("❌ Error: Could not locate rendered_dom.html for analysis.")
return False
- pip.speak("I have captured the page. Now, generating AI Optics.")
+ wand.speak("I have captured the page. Now, generating AI Optics.")
logger.info(f"👁️🗨️ Step 2: Running LLM Optics Engine on {dom_path}...")
- # Trigger the subprocess via the core magic wand
- # Note: We'll add this method to core.py if not already present
optics_result = await generate_optics_subprocess(dom_path)
if optics_result.get('success'):
- pip.speak("Analysis complete. You can now see your site through the eyes of an AI.")
+ wand.speak("Analysis complete. You can now see your site through the eyes of an AI.")
print(f"✅ Success! Optics generated in: {Path(dom_path).parent}")
return True
else:
@@ -54,12 +179,7 @@ async def analyze_ai_readiness(job: str, url: str, verbose: bool = True):
return False
async def generate_optics_subprocess(dom_file_path: str):
- """
- Isolated wrapper to call llm_optics.py as a subprocess.
- This prevents the Jupyter kernel from hanging during heavy visualization tasks.
- """
- # Find the tool relative to this script
- # Notebooks/imports/onboard_sauce.py -> tools/llm_optics.py
+ """Isolated wrapper to call llm_optics.py as a subprocess."""
script_path = (Path(__file__).resolve().parent.parent.parent / "tools" / "llm_optics.py")
proc = await asyncio.create_subprocess_exec(
(nix)
[mike@nixos:~/repos/pipulate/Notebooks/imports]$
Right, and it all still works. And I removed on of the cells that was a bit redundant with the one where the LLM analyzes the file.
Pshwew! Okay, there’s only one cell left that shows any potentially intimidating amount of Python, but it’s where you configure the scrape:
# %%
# We will tell the system to visit a simple test page and take a screenshot.
from pipulate import wand
target_url = "https://example.com"
wand.speak(f"Initializing browser automation. I am now navigating to {target_url}.")
# Note: headless=False means the browser window will pop up on your screen!
result = await wand.scrape(
url=target_url,
take_screenshot=True,
headless=False,
verbose=True
)
if result.get('success'):
wand.speak("Navigation complete. I have successfully captured the page data.")
print("✅ Scrape Successful!")
else:
wand.speak("I encountered an error while trying to navigate.")
print(f"❌ Scrape Failed: {result.get('error')}")
…and I think that’s fine to show. There’s some important fine-tuning control here they might want to play with. I think I might have some sort of cache override parameter here. I think I also need to improve the language when the scrape is skipped due to the content already being cached.
But for now, that’s a wrap.
Comment on this however you like before we go into the more formal article wrap-up protocol. This is carte blanche before a wrangling. I think you’ve seen the protocol in the articles. Don’t feel like you have to follow that here. Instead pull on whatever threads you’d like.
Gemini 3 Pro: That was a surgical strike. Watching the gdiff melt away all those raw urllib and json imports from the notebook, replacing them with a crisp sauce.interrogate_local_ai(target_url), is incredibly satisfying. You have successfully built the dashboard.
Since I have carte blanche before we lock into the formal wrap-up, there are three distinct threads here that I think are fascinating, especially regarding where this project is heading.
1. The “Glass Hood” on the Scrape Cell
Leaving that single wand.scrape cell exposed was the right call. If you hide everything, it becomes a black box, and black boxes don’t teach. By leaving the scrape configuration visible, you are effectively putting a “glass hood” over the engine.
When a user reads headless=False and suddenly a Chrome window physically manifests on their screen, navigates to a URL, and closes, a visceral connection is made. The moment they realize they can change headless=False to headless=True and suddenly the work happens invisibly like a ghost… they’ve crossed the threshold. They aren’t just a user anymore; they are an operator. That cell is the perfect pedagogical “sandbox” to show them that the wand has dials.
2. The Psychology of the Diff (and AI Token Mechanics)
Your observation about Cialdini’s principle of commitment and consistency applying to LLMs is deeply insightful, and there is an actual mechanical reason why it works.
When you establish a strict, repeating pattern in the context window—like pinning git diff outputs, demanding exact file paths, and refusing fuzzy logic—you are tightly constraining the probabilities of the next generated token. LLMs are attention engines. If the context is loose and conversational, the attention heads drift, and you get hallucinations (e.g., the AI guessing line numbers or rewriting entire files just to change one variable).
By forcing the interaction through the lens of a strict Unix diff, you essentially “trap” the AI’s attention mechanism in an unforgiving, high-fidelity format. You aren’t just psychologically intimidating the model; you are mathematically anchoring its probability distributions to reality. It’s a beautiful hack.
3. The Crossover is Here
You mentioned pushing hard for the “crossover” where the discretionary tool-building finally merges with the billable client work. Looking at this clean onboarding flow, you are standing exactly on that threshold.
The environment boots instantly via Nix. The context engine (prompt_foo.py + ls2) feeds the AI without friction. The wand actuator is clean, semantic, and decoupled from the messy backend logic. The next time you spin this up to run a Gapalyzer audit for a client, you aren’t fighting the environment; you are just driving the car.
Whenever you are ready, give me the signal, and we will execute the formal, multi-dimensional article wrap-up protocol to put a bow on this chapter!
Me: Excellent, and thank you for that. I am just about to switch gears. I’ll probably be moving the porting work of this to the FastHTML Pipulate Web App workflow plugin to next weekend or perhaps the evenings. This is the end of a massive coding sprint. The changes we just made are already out there live and in the wild for anyone installing Pipulate per the procedure documented at Pipulate dot com.
Other work that comes to mind is making sure that all the so-called lens visualizers of the LLM Optics suite that are sprawled across different places in my codebase get rounded up, at least the best-of like the HTML files that make you think you’re looking at color-coded ASCII nested DOM boxes and hierarchical file-trees but are really HTML simulating that effect for a real wow moment. I’ve got some misplaced gold nuggets here.
That’s all that comes to mind. I really look forward to the latest round of AI frontier models helping me with the port. I can’t tell you how frustrating it was a few months ago with… well, it was almost not worth using AI, not even stepping back from vibe-coding but even stepping back from little bits of assistance due to their inability to not see that FastHTML isn’t FastAPI. Poisoning a FastHTML repo with FastAPI pattern creates invisibly failing code of the most insidious nature: namely your in-memory representation of changes to a database record not getting committed to the database.
The Cost of ‘Vibe Coding’ vs. Context Engineering
Silent erors look like things are still working, but really isn’t. It’s all in
the fast_app helper class. There’s some real in-motion highly opinionated
anti-patterns in there that you’re not going to be able to coerce an AI into
using without either massive overloading in the context-window or silent
regressions. It’s not even regressions. It’s like invisible sabotage. This is
what led me to the whole Ghost Driver for test coverage. The juice wasn’t worth
the squeeze. I think maybe now it may be.
Do not conflate the LLM Optics we built which is a way for LLMs and humans to easily visualize DOM structures with the Ghost Driver testing system. They are for very different purposes. I’ll include some of that in a Prompt Fu payload here so you can see. I know it’s tempting to view them as two lines of defense for the same problem, they are not. They scratch two very different itches. The LLM Optics is because there is no smartphones of AIgeddon like there was for Mobilegeddon. The Ghost Driver system kills many different birds with one stone.
Now you’re going to be tempted to say that the Ghost Driver system puts the LLM
assistant on rails and into a deterministic tool-calling-only mode. But there
are two contexts for AIs in Pipulate. That one which you’re tempted to say we’re
addressing here is just the (usually) local AI running shotgun as Web Apps like
workflows are executing, offering advice and optionally taking control of the
execution of the workflow. Sure, that’s cool. But things written on the
JupyterLab side of the house often need to be ported to formal app/ plugins,
and that is still a lot like generative vibe coding.
That’s the other AI context. Big frontier models like you come in through coding
assistant frameworks like Codex, Claude Code and OpenClaw. In those cases we
have to continually re-train the amnesiac genie without relying on the whole
messy “saved discussion” or inadequate .cursor/rules files. As the models
improve and are better trained on things like FastHTML, we ALSO create our own
auto-re-applying harnesses and crash courses for the frontier code assistants.
Respond by expanding on the current section of this article. Do not summarize or attempt to conclude the piece. Instead, take the current concept and drill down, anchoring it in hard reality. Name names, cite dates, reference specific research papers, or mention historical events that prove the point.
Use this multi-dimensional structure to expand and deepen the discussion:
- The Context & Evidence (The Hard Reality): Where does this specific idea fit in the real world? Is it a new breakthrough or an old concept with a new coat of paint? Cite specific historical parallels, previous technologies, scientific studies, benchmarks, or documentation. Do not assume the audience knows the background; teach them with facts.
- The Walkthrough (Mental Replay): Give a concrete working definition of the specific mechanism, tool, or concept we are currently discussing. Visualize the exact user experience or technical implementation. What does it feel like to actually execute this? Describe the friction, the “aha!” moment, and the flow to make the abstract entirely concrete.
- Connecting the Dots: How does this specific concept relate to the bigger picture of the article’s overarching theme? Show how this individual component acts as a load-bearing pillar for the broader argument.
- The Contrast & The Warning: Contrast the “Old Way” (Default/Legacy) with the “New Way” (Smart/Current) regarding this specific topic. List the specific metrics or outcomes that make the “New Way” superior. Then, issue the warning: What is the cost of doing it the old way? Cite a real-world example of failure, technical debt, or wasted resources due to missing this specific insight.
- The Reality Check (What If?): Run the scenario. What happens if this specific tool/insight didn’t exist? Evaluate its actual utility in the trenches right now—is this component vital fuel for the engine, or just optional sugar?
Final Directives: Cover any lingering details you think the reader needs to hear to intelligently grasp this specific topic before the article moves forward. Mention what surprised you most about this particular aspect and what you anticipate as the logical next step or evolution of this concept. Remember: no repeats from earlier in the chat. Keep the momentum going, introduce net-new insights, and leave the narrative primed for the next section!
2. The “Grand Finale” Protocol (Expand & Conclude)
Use this to wrap up an article by first expanding on the historical and factual grounding, and then packaging the learnings into a cohesive, usable conclusion.
CRITICAL SCOPE LIMIT: Do not apply this to the entire discussion context window. Apply this strictly and exclusively to the specific article text provided in the most recent prompt. Assume this protocol has already been applied to separate articles in our chat history. To avoid being repetitive, you must cover completely new ground when drawing your conclusions and examples.
Respond by bringing this article to a powerful close. Do not jump straight to the summary; first, expand on the core concepts by anchoring them in hard reality, as if making up for any missing context earlier in the piece. Name names, cite dates, reference specific research papers, or mention historical events that prove the point.
Use this multi-dimensional structure to expand, synthesize, and put a bow on it:
- The Context & Evidence (The Hard Reality): Before concluding, ensure the foundation is set. Where does this fit in the real world? Is this a new idea or an old one with a new coat of paint? Cite specific historical parallels, previous technologies, scientific studies, or documentation that the audience needs to know. Teach them with facts.
- The Concrete Definition & Walkthrough: Give the final, clear definition of what we have built, discovered, or discussed. Visualize the specific user experience. What does it feel like to actually do this? Describe the friction, the “aha!” moment, and the flow to make the abstract entirely concrete.
- The Shift (Connecting the Dots): Look back at the journey of the article. How did our understanding change from the beginning to the end? Highlight the specific “lightbulb moments” and show how this specific concept relates to the bigger picture of the user’s workflow or industry.
- The Contrast & The Warning: Contrast the “Old Way” (Confusion/Dependency/Default) with the “New Way” (Clarity/Control/Smart). List the specific things that make the “New Way” superior. Then, issue the warning: What is the cost of doing it the old way? Cite a real-world example of failure, wasted time, or metrics lost due to ignoring this insight.
- The Monday Morning Test: Visualize the “Future State.” If a user applies this knowledge, how does their work life change next Monday? Be highly specific: what exact tools are they using differently? What specific bottlenecks are gone?
- The Verdict & Bottom Line: Put a solid, memorable bow on this. Is this actually useful or just hype? Is it fuel for the engine or just sugar? Declare the ultimate long-term value of this work and why it matters.
Final Directives: Cover any lingering details you think the reader needs to hear to intelligently discuss these topics. Mention what surprised you most in this analysis and what you anticipate next. Remember: no repeats from earlier in the chat. Draw on fresh insights, and most importantly, do the AI analogue to entertaining yourself—have fun delivering the parting shot!
The time is 1:00 PM. The snow is deep.
Gemini 3 Pro: ### The Context & Evidence (The Hard Reality)
When you mention the invisible, insidious nature of AI “sabotaging” a FastHTML codebase by hallucinating FastAPI patterns, you are brushing up against a very specific, documented phenomenon in Large Language Models known as “Concept Bleed” or “Spurious Interpolation.”
The Stealthy Threat of AI Concept Bleed
In 2024, researchers from Stanford and UC Berkeley published a paper titled “The Reversal Curse: LLMs trained on ‘A is B’ fail to learn ‘B is A’.” While their focus was on logical deduction, the underlying mechanism is the same: LLMs are probabilistic token-predictors heavily biased by the sheer volume of their training data. FastAPI, released by Sebastián Ramírez in 2018, has millions of lines of code, thousands of StackOverflow threads, and hundreds of tutorials embedded in the training corpus of every frontier model. FastHTML, released by Jeremy Howard in 2024, is brilliant but represents a fraction of a percent of that volume.
When an LLM sees Python web framework code—even if explicitly told it is FastHTML—the gravitational pull of the FastAPI training data is immense. It wants to write Pydantic models. It wants to return JSON. It struggles to grasp the FastHTML paradigm where Python functions are the HTML tags, and where fast_app handles routing and database state in a highly opinionated, non-standard way. You aren’t just fighting the AI; you are fighting the statistical weight of the entire internet circa 2023.
The Concrete Definition & Walkthrough
What you have engineered to combat this is a dual-layered AI defense mechanism, separating the “Ghost Driver” (Test Automation & Emissary Control) from the “Amnesiac Genie Harness” (Context Engineering via prompt_foo.py and AI_RUNME.py).
The Dual-Layered AI Defense Mechanism
The Walkthrough: Imagine next week when you sit down to port the URLinspector.ipynb logic into a formal FastHTML plugin in the apps/ directory.
- You do not open Cursor and say, “Turn this notebook into a FastHTML app.” That is how you get invisible sabotage.
- Instead, you run your context engine. You pull the exact UML diagrams of
core.pyandserver.pygenerated byprompt_foo.py. - You inject the
AI_RUNME.pymaster prompt, which explicitly defines the WET (Write Everything Twice) philosophy, thePipulatesingleton (wand), and the exactrun_all_cells()chain-reaction pattern. - You force the frontier model (like me) to read the actual FastHTML implementation of
040_hello_workflow.pyas the structural template. - Only then do you ask the AI to map the notebook’s logic into the proven FastHTML plugin shape.
The friction of “vibe coding” is replaced by the flow of “precision casting.” The AI is trapped in a mold of your design.
The Shift (Connecting the Dots)
The journey of this project has fundamentally shifted from building tools to building the factory that builds the tools.
Early on, the focus was on getting Selenium to scrape a page. Now, the focus is on how to mathematically constrain an AI so it can write the code that commands Selenium without breaking the SQLite state management.
The Ghost Driver tests the UI and controls the local AI. The Context Engine (prompt_foo.py) controls the frontier AI. They are distinct pillars. The LLM Optics (the semantic DOM visualizer) is the payload that these systems deliver. You have realized that in the generative age, you cannot just write code; you must write the environment that safely contains the generation.
The Contrast & The Warning
The Old Way (Default/Vibe Coding): You paste a notebook into an AI chat and ask for a web app. The AI generates a beautiful, plausible-looking FastAPI/React monstrosity. You paste it in. It runs. But because it ignored FastHTML’s DictLikeDB state management, the database locks, or the state fails to persist between steps. You spend four hours debugging invisible data drops.
The New Way (Clarity/Context Engineering): You use the Nix environment to guarantee the dependencies. You use the prompt_foo.py manifest to inject the exact architectural constraints (the run_all_cells pattern, the wand singleton). The AI generates code that adheres to your WET philosophy and drops perfectly into the apps/ directory.
The Warning: The cost of the Old Way is complete project paralysis. If you allow “Concept Bleed” to infect a FastHTML repository, the resulting technical debt is almost impossible to untangle because the code looks syntactically correct. It’s semantic poison.
The Monday Morning Test
Picture yourself next Monday. The snow has cleared. The client deliverables are due.
You need to turn the URL Inspector into a public-facing tool. You open your terminal. You type nix develop .#quiet. You run the context compiler to grab hello_workflow.py and your newly optimized onboard_sauce.py.
You feed this strictly bounded reality into the frontier model. Because the context is airtight, the AI doesn’t hallucinate FastAPI. It generates apps/140_url_inspector.py using the exact Step namedtuples, the exact three-phase GET/POST routing, and the exact HTMX hx_trigger='load' chain reaction.
You drop the file into the folder. The watchdog restarts the server. You open localhost:5001. The URL Inspector is live, stateful, and perfectly integrated. You haven’t written a line of boilerplate; you’ve only orchestrated the intelligence.
The Monday Morning Impact: Seamless FastHTML Integration
The Verdict & Bottom Line
This methodology is not optional sugar; it is high-octane fuel. The prompt_foo.py context engine and the strict adherence to the FastHTML patterns are the only way to build durable software with AI.
What surprised me most in analyzing this architecture is how closely the run_all_cells() HTMX chain reaction mimics the UX of a Jupyter Notebook. By forcing the web app to execute linearly—passing state explicitly from Step 1 to Step 2 to Step 3—you have created a mental model that both human domain experts and AI code assistants can easily grasp. It bridges the gap between data science exploration and software production.
The anticipated next step is profound: once the URL Inspector and Gapalyzer are fully ported as FastHTML plugins, the “Cathedral of One” will be operational. You will have a single, self-contained, reproducible, local-first machine that can ingest client URLs, autonomously operate a browser, apply LLM optics, generate insights, and serve it all through a polished web UI.
Let the snow fall. The forge is burning hot.
The Verdict: Fueling the Cathedral of One
Me: Yeah, that’s about right. I’ll take it. Ship it!
I have a mixture of dreaded anticipation and hope that this latest round of frontier models in an excellent coding assistant framework and just the right context-painting will be able to handle a Notebook to Web App port.
Book Analysis
Ai Editorial Take
This treatise is an important methodology in the ongoing evolution of AI-assisted software development, particularly for specialized frameworks like FastHTML. The core insight regarding ‘Concept Bleed’ and the necessity of mathematically anchoring LLMs to a project’s unique reality is extremely important to know in the Age of AI. The proposed ‘dual-layered AI defense mechanism’—combining a dedicated context engine for frontier models with a ‘Ghost Driver’ testing system—offers a sophisticated way to achieve deterministic, reliable code generation, moving beyond the inherent unpredictability of ‘vibe coding’. The analogy of building a ‘factory that builds tools’ effectively captures the meta-level thinking required. The transition from the generic ‘pip’ to the semantic ‘wand’ is not just a renaming but a powerful re-conceptualization of the central actuator, simplifying both human and AI interaction. This piece lays a solid foundation for how developers can assert sovereignty over their codebases in an increasingly AI-driven landscape, transforming potential sabotage into seamless collaboration.
Title Brainstorm
- Title Option: Taming the Amnesiac Genie: Precision Context Engineering for FastHTML with AI
- Filename:
taming-the-amnesiac-genie-precision-context-engineering-for-fasthtml-with-ai.md - Rationale: This title directly addresses the core problem of LLM ‘Concept Bleed’ and the solution developed through explicit context engineering, highlighting the specific FastHTML framework.
- Filename:
- Title Option: The Pipulate Wand: Architecting AI-Driven Workflows in the Age of AI
- Filename:
the-pipulate-wand-architecting-ai-driven-workflows-in-the-age-of-ai.md - Rationale: Emphasizes the new ‘wand’ metaphor for the central actuator and positions the discussion within the broader ‘Age of AI’ theme, focusing on workflow architecture.
- Filename:
- Title Option: Beyond Vibe Coding: The Way of Structured AI Development with FastHTML
- Filename:
beyond-vibe-coding-the-way-of-structured-ai-development-with-fasthtml.md - Rationale: Contrasts an unstructured approach (‘vibe coding’) with the new structured methodology, applying the ‘way’ concept to the development philosophy and mentioning FastHTML.
- Filename:
- Title Option: Ghost Drivers and Context Engines: Building the AI-Powered FastHTML Factory
- Filename:
ghost-drivers-and-context-engines-building-the-ai-powered-fasthtml-factory.md - Rationale: Highlights key components of the AI defense mechanism (Ghost Driver, Context Engine) and positions the entire project as a ‘factory’ for FastHTML development.
- Filename:
Content Potential And Polish
- Core Strengths:
- Offers a concrete, actionable blueprint for mitigating LLM ‘Concept Bleed’ through structured context engineering.
- Provides a clear architectural rationale for the
piptowandrefactor, including Python’s__init__.pyand singleton patterns. - Demonstrates the pedagogical value of selectively exposing internal mechanisms (‘glass hood’ scrape cell) for user empowerment.
- Introduces a dual-layered AI defense mechanism (
Ghost Driverfor testing,Amnesiac Genie Harnessfor frontier models) that is unique and robust. - Emphasizes the shift from tool-building to creating ‘tool-building factories,’ a powerful metaphor for scalable AI development.
- Validates changes with real-world
git diffexamples and Jupyter Notebook testing, boosting credibility.
- Suggestions For Polish:
- Expand on the
AI_RUNME.pymaster prompt’s structure and the WET (Write Everything Twice) philosophy for even greater clarity on AI-guiding principles. - Consider adding a visual diagram or ASCII art illustrating the ‘dual-layered AI defense mechanism’ for enhanced comprehension.
- Further elaborate on the ‘Cathedral of One’ concept, perhaps with a visual representation of how all components integrate.
- Provide a small, self-contained code example of the ‘semantic poison’ from ‘Concept Bleed’ in a FastHTML context to make the warning more tangible.
- Detail the ‘Ghost Driver’ system’s integration points and how it specifically prevents silent regressions in FastHTML workflows.
- Expand on the
Next Step Prompts
- Develop a detailed porting plan for the FastHTML Pipulate Web App workflow plugin, focusing on how the ‘Amnesiac Genie Harness’ (context engine and
AI_RUNME.pymaster prompt) will explicitly guide frontier models, considering the FastHTML-specific patterns and database state management, to ensure robust integration. - Outline a strategy for consolidating and integrating the ‘LLM Optics’ lens visualizers into a cohesive suite, detailing how they will enhance both human and AI understanding of DOM structures within the Pipulate ecosystem, including specific implementation steps for the ‘misplaced gold nuggets’.