Making API Logs Copy/Paste-Ready For Jupyter Notebooks

Understanding API Interaction and AI-Assisted Development: A Pipulate Case Study

This document delves into the intricate world of software development, specifically focusing on how a developer interacts with complex Application Programming Interfaces (APIs)—in this case, Botify’s API for SEO data—and the evolving practice of using AI coding assistants (like Gemini and Cursor AI) to write and refactor code. The author is working on a project called “Pipulate,” a local-first application designed to automate SEO workflows. The core of this journal entry revolves around enhancing the transparency of API calls made by Pipulate, particularly those using Botify Query Language (BQL). This involves creating detailed logs of API requests and responses, including generating runnable Python code snippets for easier debugging and experimentation in a Jupyter Notebook environment. The narrative highlights the challenges of making precise code changes with AI tools, the importance of detailed “Prompt Fu” (skillful prompting), and the iterative process of refining software to achieve specific technical and usability goals. It’s a candid look at problem-solving, API exploration, and the practical realities of building specialized tools in the age of AI.

The Quest for GPU-Accelerated Link Graphs

I started writing this article and in the process of crafting the “Prompt Fu” that it’s designed to be — storytelling for an AI coding assistant in such a way that I provide as much and more context for it than if it were human in a human story-telling fashion — and in the process wrote an article on the improvements I just made to prompt_foo.py. And that one became article 1 for today even though today’s story starts here with this one.

Pivoting to a Core Need: BQL Query Transparency

Alright, next I implement the Cosmograph Viewer which was my original foray into this whole link graph visualization thing because of how it leverages the GPU of the local machine to visualize those massive link-graphs with hundreds of thousands or even millions of edges. If reality simulation is possible in videogames, certainly visualizing large network graphs is not so impossible as giphy and cytoscape make it seem.

Gemini: You’re diving into a fascinating area! Visualizing network graphs on websites has evolved significantly, with various libraries and tools emerging to tackle the challenge, especially as datasets grow larger and the need for interactivity increases. Before tools like Cosmograph pushed the boundaries with GPU acceleration, and alongside established players like Cytoscape.js, developers have used a range of solutions.

Here’s a rundown of other network graph visualizers people have used on websites categorized for clarity:

Dedicated JavaScript Graph Visualization Libraries

These libraries are specifically designed for creating and interacting with network graphs in the browser. They offer various rendering technologies (SVG, Canvas, WebGL) catering to different needs in terms of performance, customization, and ease of use.

• D3.js (Data-Driven Documents):
  • Rendering: Primarily uses SVG, but also HTML and CSS.
  • Description: A highly flexible and powerful low-level library. While not exclusively for graphs, it provides the building blocks to create almost any custom data visualization, including intricate network graphs. It has a steep learning curve but offers unparalleled control. Many other libraries are built on top of or inspired by D3.
• Cytoscape.js:
  • Rendering: Canvas (default for performance), SVG.
  • Description: A widely-used, feature-rich open-source library specifically for graph analysis and visualization. It’s highly optimized for performance and supports complex layouts, rich styling, event handling, and graph theory algorithms. It’s distinct from the Cytoscape desktop application but can work with similar data formats.
• Vis.js (Network Module):
  • Rendering: HTML Canvas.
  • Description: Offers a dynamic, browser-based network visualization module. It’s known for being relatively easy to use and good for interactive networks with features like physics-based layouts, clustering, and manipulation.
• Sigma.js:
  • Rendering: WebGL (primary focus for performance), Canvas (fallback).
  • Description: Designed for drawing large graphs efficiently. It leverages WebGL for hardware-accelerated rendering, making it suitable for larger networks. Often used with graphology for graph data structure manipulation.
• VivaGraphJS:
  • Rendering: WebGL, SVG, or CSS/DOM.
  • Description: Another library that emphasizes performance for large graphs, offering different rendering engines. It supports various layout algorithms.
• React-force-graph / three-forcegraph / vr-forcegraph / ar-forcegraph:
  • Rendering: WebGL (via Three.js) or 2D Canvas.
  • Description: React components for rendering force-directed graphs in 2D, 3D, VR, and AR. These are excellent for integrating graph visualizations into React applications with modern rendering capabilities.
• G6 (by AntV):
  • Rendering: Canvas, SVG.
  • Description: A robust graph visualization engine from Alibaba’s AntV, designed for ease of use and extensibility. It offers a good range of built-in features and layouts.
• GoJS (Commercial):
  • Rendering: Canvas.
  • Description: A powerful commercial JavaScript library for creating interactive diagrams and graphs. It offers extensive features for complex visualizations, layouts, and user interactions.
• yFiles for HTML (Commercial):
  • Rendering: SVG, Canvas, WebGL.
  • Description: A comprehensive commercial diagramming library that provides sophisticated algorithms for automatic graph layout, making it suitable for complex and large diagrams in enterprise applications.
• KeyLines (Commercial, by Cambridge Intelligence):
  • Rendering: HTML5 Canvas, WebGL.
  • Description: An enterprise-grade JavaScript toolkit for building high-performance graph visualization applications. It’s known for its ability to handle large, complex networks and offers features like time-based analysis and geospatial mapping.
• Ogma (Commercial, by Linkurious):
  • Rendering: WebGL.
  • Description: A high-performance commercial JavaScript library designed for visualizing and interacting with very large-scale graphs, focusing on speed and rich visual analytics.
• NetV.js:
  • Rendering: WebGL.
  • Description: Aims to be a very fast WebGL-based graph visualization library, claiming to handle millions of elements.
• Cola.js (Constraint-based Layout in JavaScript):
  • Rendering: Works with D3.js (SVG) or SVG.js.
  • Description: Not a rendering library itself, but a library for arranging HTML5 documents and diagrams using constraint-based optimization techniques. It’s particularly useful for graph layout.
• Uber React Digraph:
  • Rendering: SVG.
  • Description: A React component for rendering directed graphs, providing a set of UI components for building graph editors.

General-Purpose JavaScript Charting Libraries with Graph Capabilities

These are broader charting libraries that include support for network/graph visualizations as part of their offerings.

• ECharts (by Apache):
  • Rendering: Canvas (default), SVG.
  • Description: A powerful and popular open-source charting library with a wide array of chart types, including force-directed graphs, tree diagrams, and relationship graphs.
• Plotly.js:
  • Rendering: SVG, WebGL.
  • Description: A high-level declarative charting library built on D3.js and stack.gl. It supports various chart types, including network graphs (e.g., from NetworkX data), and can leverage WebGL for larger datasets.
• Google Charts:
  • Rendering: SVG.
  • Description: Offers a variety of chart types, including organizational charts and tree maps, which are specific types of graphs. While not a general-purpose network graph tool, it has been used for hierarchical data.

Server-Side/Desktop Tools with Web Output/Components

These tools are often used for analysis and visualization on the desktop but offer ways to export visuals for the web or have web-based companion tools.

• Graphviz (Graph Visualization Software):
  • Output: SVG, PNG, PDF, etc.
  • Description: A foundational open-source tool that takes descriptions of graphs in the DOT language and generates diagrams. Its SVG output is commonly embedded in web pages for static or semi-interactive visualizations.
  • Viz.js: A JavaScript port of Graphviz (using Emscripten) that allows client-side rendering of DOT language defined graphs directly in the browser.
• Gephi (The Open Graph Viz Platform):
  • Output: GEXF, SVG, PDF, PNG.
  • Description: A popular open-source desktop application for exploring and visualizing large networks. While primarily a desktop tool, it can export graphs to formats like GEXF (which can be loaded by libraries like Sigma.js or Gephi Lite) or static SVG.
  • Gephi Lite: A web-based companion to Gephi for exploring and interacting with graph visualizations in the browser.

Underlying Technologies & Helper Libraries

• Graphology:
  • Description: This is a specification and a collection of JavaScript libraries for graph data structures and algorithms. It’s often used as a “headless” component, managing the graph data, which is then rendered by a library like Sigma.js.
• Rendering Engines (WebGL, Canvas, SVG):
  • SVG (Scalable Vector Graphics): DOM-based, excellent for high-quality, resolution-independent graphics and interactivity on smaller to medium graphs. Each element is a DOM node, which can become a bottleneck for very large graphs.
  • HTML Canvas: A pixel-based drawing surface, generally offers better performance than SVG for larger numbers of elements as it’s a single DOM element. Less direct interactivity with individual elements compared to SVG.
  • WebGL (Web Graphics Library): Allows for GPU-accelerated graphics rendering in the browser. This is the most performant option for very large and complex graphs, enabling smooth interaction and animation where SVG or Canvas would struggle. Libraries like Sigma.js, VivaGraphJS, Cosmograph, and parts of KeyLines/Ogma utilize WebGL.

The choice of visualizer often depends on the size and complexity of the graph, the level of interactivity required, performance needs, rendering technology preference (and thus GPU leverage), and whether a commercial or open-source solution is preferred. Your focus on Cosmograph highlights the trend towards leveraging GPUs for these demanding visualization tasks, which many of the more modern and performance-oriented libraries are also embracing via WebGL.

Link Graph Visualizer

Thank you! That’s very useful. There’s going to be a whole lot of types of network graph visualizations worked into Pipulate for a whole lot of reasons. The “big one” for a largest-possible export from Botify for a link-graph visualization at maximum depth with the largest number of edges, I’ll use Cosmograph at least for the one that tries to actually render and show all the edges without sampling down. That one I’ll probably brand “Link Graph Visualizer” within Pipulate and it’s the one that creates the look of the Deathstar very often because of the tangled interlinking that e-commerce sites so frequently produce. There will be a collection of generic “site signatures” I’ll be able to produce as an educational piece from this. I’ll be able to answer visually such questions as: what’s the link-graph signature of an ecommerce site with crawlable faceted search versus one with non-crawlable faceted search. Answering this sort of question is key in my enterprise SEO space.

Segmentation Finder & Hierarchy Exposer

But also I will have incredibly lightweight visualizations of the link-graph, both from Botify exports because by that time (after the Link Graph Visualizer) it will just be easy to do from a Botify export, but this opens the door to what is probably the most exciting development direction for Pipulate. It will also be the Website Story Gleaner and the LLM AI Whisperer apps in how I will… during a real-time crawl… oh, just go read The Calm Before The Natural Language Web

/home/mike/repos/MikeLev.in/_posts/2025-05-22-the-calm-before-the-nlweb.md

The Quest for GPU-Accelerated Link Graphs

In summary, I want to do 1 click-depth crawls in real-time which are hardly crawls at all because technically it’s just a close examination of the links on the one page you crawled. To get the full picture of this the implementation plan is actually on Force Multiplying Ikigai In the AI Age

/home/mike/repos/MikeLev.in/_posts/2025-05-23-force-multiplying-ikigai-in-ai-age.md

The “Eye Dropper” Approach to Web Crawling

…where I have this vision of doing a crawl like the application of an eye dropper, with that eye-drop only being a single URL submitted and the flowing out only being looking at the links directly on that URL’s page. In this way you can really grab the user who has this vision of that kind of ginormous enterprise crawl that everybody intuitively wants and imagines in their heads (the kind my employer Botify profides) but the slams on the breaks and says: Hey dummy, let’s look at THIS PAGE your crawl is starting from before we move on — I bet there’s a bunch of things you don’t really realize about it, like the list of links on the page per view-source HTML versus the JavaScript-rendered DOM (on initial pageload)

Visualizing Semantic Structure

This is also a wonderful opportunity to visually show the scaffolding or schematic of semantic meaning through which those links are distributed on the page(<nav>, <header>, <footer>, <main>) and how they nest. There’s a simplified DOM we can show the user and how those links land on it. This could help immensely with browser automation too if we keep the aria labels intact when we make this DOM schematic.

Reading the Site’s Story

Obviously this leads to reading the story trying to be told by your site. Per the NLWeb proposal from Microsoft, there’s going to be a lot of attempts to make your structured data talk to the LLMs here too. We might work that into this page schematic or handling it separately. But this is WHEN we read it and bring it to the user’s attention — while they’re looking at a just 1-page crawl instead of a zillion.

The Anti-Arms Race Philosophy

Wow, I could go on. But you get my meaning. The one thing Pipulate will not do is one of those attempts at a crawl-at-scale the way Botify does and the way ScreamingFrog sort of does if the stars align just so and you jump through sufficient hoops. I will not enter the crawler arms race with Pipulate — nor the SERP-scraping arms race, nor the web-browser-automation to end-run APIs arms race. However, I will do semi-automation on each bringing it only up to the point where the human-in-the-loop takes over for last-mile touches. This is the way it should be anyway for a human to have oversight as to the process and for AIs to not squeeze domain experts out of their final gatekeeping jobs.

The Reality of AI and Domain Expertise

AI is going to take all our jobs, right? Not without figuring out each and every “best practice” process and procedure within each specialization domain, it won’t. So many people seem to think because it can code Snake and Androids in one shot that it will be able to do everything in every field, but the truth is if it’s not in a Stack Exchange site like Stack Overflow or on GitHub in a public repository, chances are the LLMs don’t really know how to do it. They still have to be trained. And a lot of that training is the family jewels and the secret sauce of all these respective industries and they’re going to keep those workflows close to their chest.

The Local-Host First Advantage

And for that, there’s Pipulate because it’s local-host first. What happens on localhost stays on localhost, so you can automate those procedures (or at least human-in-the-loop semi-automate) just like making a Jupyter Notebook — but as a Web App anyone can use!

The Notebook-Inspired Interface

That’s the beauty and the magic of Pipulate. It works just like Notebooks user-interface-wise, meaning you just step through a procedure or wizard top-down on the page, left-to-right, top-to-bottom in the precisely natural order of Jupyter Notebooks or just books. It’s linear sequential workflows because most things can be expressed in 1, 2, 3… step procedures.

The Single-Page Crawl Philosophy

This is why a 1-click-depth crawl fits so well into the Pipulate linear workflow model: we’re not trying to boil the ocean in a single step (or would that be crawl the ocean?). We do a quick grab of the HTML on that (and only that) page, we save it locally as the “View page source” HTML (pre-rendered) and as the inspect aka Developer Tools post-pageload rendered DOM, then we slam on the brakes and say STOP! Did you know by loading just 1 page we already started the crawl and are sitting on top of an enormous amount of data on your most important page (start of the crawl) that’s normally overlooked? Let’s slap on some horse blinders and myopically look at this page before proceeding, response headers and all! We’re going to make you understand your site like you’ve never understood your site before — exactly what’s needed in the dawn of AI!!!

The D&D-Style Website Mapping

And then you can go on to crawl the next URL and the next, flood-filling out the mapping of the website like D&D adventurers on an campaign mapping out a dungeon, URL-by-URL with Statler and Waldorf or MST3K commentary the whole way. The Website Story Gleaner is going to have attitude! It’s prepping you for all the things wrong with your site for Whispering to the LLMs. When they find unconstrained faceted search they’ll be like: “What are you trying to say, welcome to the Deathstar?”

Pivoting to BQL Query Transparency

Sheesh, boy to I weave a such a long story for such simple next-step implementation requests!

Before we can get to that super-cool flood-fill URL-by-URL snark-crawl, we’re going to do the ginormous link-graph exports from Botify to show those e-commerce Deathstars. And for that we’re going to start with an export a lot like the current 535_botify_trifecta.py:

/home/mike/repos/pipulate/plugins/535_botify_trifecta.py

These extremely large visualizations of the link-graph are going to do a maximum export of the link graph itself, which is not even represented in the queries of this example. I lead with this because it will be similar to step_02 “Download Crawl Analysis” but not exactly the same.

The Botify API Learning Journey

This is where I am going to start to chip away at learning the Botify API better. This is where I realize how very little I really know about the Botify API and need to lay the groundwork for accumulating knowledge, creating learning moments and having a clean audit trail for every BQL query that I do. The other day I wrote this article on AI Wrangling and Code Transparency where I planned for today’s work.

/home/mike/repos/MikeLev.in/_posts/2025-05-21-ai-wrangling-code-transparency.md

Crafting the BQL Query Audit Trail

So I put what I think is a fairly robust Botify API BQL transparency framework in place. The problem is now I need to isolate its output so that when I do follow-up queries to you with this article, I can have precise before-and-after code examples such as:

This is the full series of BQL requests and responses are being made by step 02 of Botify Trifecta including the requests, responses and endpoints used — maybe even request and response header information (everything but the API token), and here is what I need it to be including a sample query of how it’s working today from a previous implementation.

Current Logging Infrastructure

Currently the log files I have include:

/home/mike/repos/pipulate/logs/Botifython.log ([APP_NAME].log)
/home/mike/repos/pipulate/logs/chromedriver_persistent.log
/home/mike/repos/pipulate/logs/table_lifecycle.log

The “Lightest Touch” Doctrine for AI Code Modifications

…and I think what I need next in terms of implementation plans from you is explicit instructions for the Cursor AI coding assistant in such a way that it can’t misinterpret or screw up to crate a new log file for the BQL queries that fulfills all of the above reasonable criteria (http headers may be a bit much) in creating a new bql.log. It should not be cumulative. It should reset itself on every restart of server.py just like [APP_NAME].log (and hopefully the others) do.

The way I expect the Cursor AI coding assistant to screw up is by not noticing that I use loguru and not noticing the rather significant logging framework that already exists that it can leverage and take advantage of in doing this work. Normally it will try to import the standard Python logger and do many rounds of re-invention that mangles my code. What I want it to do is to slide this in with the lightest touch and minimal code possible riding on top of everything about the existing code that should make it easy.

Please and thank you!

Gemini: Okay, I understand the goal: to create a dedicated bql.log file for Botify Query Language (BQL) interactions, leveraging the existing loguru setup in server.py with a “lightest touch” approach. This new log should reset on server restart, similar to [APP_NAME].log.

You’re correct to anticipate that a naive AI approach might overlook the loguru setup. This plan is designed to integrate smoothly.

Here’s a precise implementation plan:

Phase 1: Modify server.py - Update Logging Setup

1. In the setup_logging() function:
   • Define BQL Log Path: Right after app_log_path = logs_dir / f'{APP_NAME}.log', add:

       bql_log_path = logs_dir / 'bql.log'
     

   • Ensure BQL Log Clears on Restart: Modify the log clearing section to include bql_log_path. It should look like this:

       # Clear main app log on server restart
       if app_log_path.exists():
           app_log_path.unlink()
       # Clear BQL log on server restart
       if bql_log_path.exists():
           bql_log_path.unlink()
       # This loop is for old rotated main logs, can remain as is
       for old_log in logs_dir.glob(f'{APP_NAME}.????-??-??_*'):
           try: old_log.unlink()
           except Exception as e: print(f'Failed to delete old log file {old_log}: {e}')
     

   • Add New Loguru Sink for bql.log: Place this block after the “Console log (stderr)” sink configuration and before the TABLE_LIFECYCLE_LOGGING block:

       # BQL Log (file)
       def bql_log_filter(record):
           """Filter for messages specifically tagged for the BQL log."""
           return record["extra"].get("bql_log") is True
     
       logger.add(
           bql_log_path,
           level="DEBUG",  # Capture DEBUG level messages tagged for BQL log
           format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level}\n{message}", # Simple format, message on new line for readability
           filter=bql_log_filter,
           enqueue=True,
           rotation="25 MB"  # Optional: manage log file size
       )
       # Announce to the main log that BQL logging is active
       logger.info("🗒️ BQL_LOGGING is ENABLED. Botify API call details will be in logs/bql.log")
     

Phase 2: Modify server.py - Direct BQL Details to the New Log

1. In the Pipulate class, within the log_api_call_details method:
   • This method already prepares full_log_message. We need to send this message to the new bql.log sink using logger.bind().
   • Modify the end of the log_api_call_details method. Replace the existing logger.debug call:

       # Existing:
       # full_log_message = "\n".join(log_entry_parts)
       # logger.debug(f"\n--- API Call Log ---\n{full_log_message}\n--- End API Call Log ---")
     

     With the following:

       # At the end of log_api_call_details, where full_log_message is defined:
       full_log_message = "\n".join(log_entry_parts) # This line should already exist
     
       # Log the detailed message to the dedicated bql.log using INFO level
       # The bql_log_filter in setup_logging will ensure this goes to bql.log
       # The actual content of full_log_message often implies detailed debug info,
       # but we use .info() here to ensure it's captured by the bql.log sink
       # if its level is set to INFO or DEBUG. The sink itself is set to DEBUG.
       logger.bind(bql_log=True).info(f"{full_log_message}")
     
       # Optional: Announce to the main application log (e.g., [APP_NAME].log)
       # that BQL details have been logged, especially if DEBUG_MODE is active for the main log.
       # This keeps the main log cleaner while still indicating that BQL activity occurred.
       logger.debug(f"BQL details logged to bql.log for: {call_description} (Pipeline: {pipeline_id}, Step: {step_id})")
     

Explanation of Changes:

• setup_logging():
  • We define bql_log_path and ensure logs/bql.log is created and cleared on each server start.
  • A new loguru.logger.add() sink is configured:
    • It writes to bql_log_path.
    • level="DEBUG" ensures it captures messages logged at DEBUG level or higher that are tagged for it.
    • format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level}\n{message}" provides a clean format where the potentially multi-line BQL details start on a new line.
    • filter=bql_log_filter is crucial. The bql_log_filter function checks if a log record has record["extra"]["bql_log"] == True. Only such records will be written to bql.log.
    • enqueue=True is good practice for non-blocking logging.
  • An logger.info message is added to the main log to confirm that this BQL logging feature is active.
• Pipulate.log_api_call_details():
  • The full_log_message (which contains all the BQL API call details, cURL commands, etc.) is now logged using logger.bind(bql_log=True).info(...).
    • logger.bind(bql_log=True) dynamically adds {"bql_log": True} to the extra attribute of the log record.
    • The .info(...) ensures the message is emitted at INFO level. Since the bql.log sink is configured to capture DEBUG and above, these INFO messages will be caught by it.
    • The original logger.debug(f"\n--- API Call Log ---...") is replaced by a more concise logger.debug() message to the main log, indicating that BQL details were logged to the separate file. This prevents cluttering the main debug log with lengthy BQL dumps if it’s not desired there, while still providing an audit trail.

The “Lightest Touch” Doctrine for AI Code Modifications

This “lightest touch” approach leverages loguru’s powerful filtering and binding capabilities. It avoids reinventing any logging mechanisms and cleanly separates the BQL logs into their own file, as requested. The new log file will be automatically managed (created, cleared) alongside the existing main application log.

Iteration 1: Laying the Groundwork for api.log with Loguru

Me: Okay, there was lots of back and forth and now the api.log file feature is fairly well implemented! We have a rotating api.log that keeps a series of previous ones so that the automatic watchdog server restarts whenever a file changes doesn’t wipe out your 1 chance to see what that last API call was. I’m pretty happy with it.

Demystifying API Calls: The Path to True Reproducibility

This is really setting the stage wonderfully for a sort of full transparency and copy/paste reproduciblity that will crate a virtuous positive cycle of me learning BQL (and really any API for that matter). The intention of the log file and the reason it has Python in there is to get to full copy/paste readiness for Jupyter Notebook. The Jupyter environment as you may be able to see is completely in common with the Pipulate FastHTML Starlette one. They are the same. JuptyerLab is always running side-by-side with Pipulate, almost as a matter of philosophy. This system is generally for porting code mocked-up (more than mocked-up because working is working) from Notebooks where the user has to look at the Python code and has challenges with how to provide input to Pipualte where it’s all on rails. Get it?

Iteration 2: Towards Copy-Paste Ready Python Snippets for Jupyter

But occasionally I want the flow to go the other direction, especially when experimenting with the nuances of APIs. I want the full Python code that’s required to reproduce what just happened in Pipulate but in that side-by-side JupyterLab instance. There’s never going to be any pip install requirement issues. It’s the same .venv! And I want to take advantage of that to its fullest. And we start by making the output to the api.log fully copy/paste ready. As you can see in its current form it is not.

Refining Snippet Robustness: Absolute Paths and Token Handling

So the implementation plan I want from you, and it’s very important that they’re precise and code-referencing pedantic because this is another place where the combination of the Cursor chat AI and edit AI will together, one or the other, manage to mangle and undermine the instructions. Take a look at the current api.log file that’s produced:

[Log file stuff]

Me: Not bad, right? But notice how the Python code and the JSON payloads are actually not meshed? If they were brought together just-so, it would be copy/paste ready for a Notebook in JuptyerLab. Additionally, it completely knows where the /home/mike/repos/pipulate/botify_token.txt file is and to put how to read it from the known location, maybe even expanded to the absoltue file location in the log files so that when you copy/paste it into either a VSCode/Cursor Notebook environment or a genuine JupyterLab Notebook, it will still run because absolute paths (they have subtle differences in their opinon of app root). Are you following?

Wrangling AI: The Perils of “Overzealous Helping”

So the implementation request is instructions for the Cursor coding assistant AI to turn the Python sample code in the api.log file into fully copy/paste ready code samples that will execute the code with the JSON payloads that are currently nearby to the Python code but out of positon and not integrated into the Python code blocks. It should also include the Python code on every copy/pastable atomic unit to load the Botify token value into the constant that is already there for that purpose. On other words, do those things necessary to makey the copyable Python code work when pasted into a Notebook that shares the same .venv.

Gemini responds to me. Cursor responds to Gemini. A back and forth iterative refinement cycle begins to which I’ll invite you in on just a few exchanges because it was so rapid and long.

Me: And here’s the new log output:

[Again lots of log stuff full of queries]

…in there, so I don’t know how the copy/pasted code is actually going to get the value without the user having to jump through hoops. The code that writes the log file could even expand the absolute path and write the actual absolute path to the token file in the log file. This is a localhost app so that’s fine. And this would allow the script to be able to find it whether it was pasted into JuptyerLab or VSCode.

Additionally, there should be clear markers like:

# BEGIN COPY HERE (Ready to copy/paste into JupyterLab)

# // the standalone copy/paste-able block of Python code

# END COPY HERE

Wrangling AI: The Perils of “Overzealous Helping”

And just as a reminder, it’s fine to have the absolute path to the token file in the log file. And the Cursor AI coding assistant and implementation assistant are going to fight against this edit tooth and nail as you can see from what came back from the partial implementation from our last go-around. So you’re going to have to be very precise, maybe passing on the reasons for things as I’ve provided to you here.

Let’s do the next iteration of this implementation Gemini, please and thank you!

Ping, pong, ping, pong. 3-way, really! Is there 3-way ping-pong? Anyway, Cursor attempts an edit following Gemini’s implementation plan and gets some damn thing wrong and I go bellyaching to Gemini — asking one Genie how to wrangle another Genie to keep the wishes from backfiring. If you want job security in the age of AI, become a Genie wish articulator.

Me: The implementation is already working perfectly! This is a very careful refinement only to mask some of the output. Be conservative again with your implementation plan exercising extreme control with the Cursor AI coding assistant that is going to want to get creative and helpful and undermine code. This last implementation request is to output a message that a second polling request has occurred and a third and so on until the actual response that contains the download ready link. This way you only see the details of the nearly identical “still processing” retries once, and are informed with a brief FYI on subsequent polling requests, and then are once again informed of everything (the whole JSON payload again) when the download link is ready.

And each API Request should be labeled more clearly based on the step in the workflow that it came from. Implementation plan please and thank you!

Cursor puts on the finishing touches. I actually tried these finishing touches with a Gemini implementation plan, but interestingly Gemini wouldn’t restrain itself to finishing touches. It wanted to believe we were still earlier in the process (function of my prompting skills), so I pulled back and tried a more direct remedy.

Me: Is this what you’re expecting in api.log: [Feeds Cursor AI new api.log]

Cursor AI: Yes, this is exactly what I was expecting in the api.log! The changes we made to the polling mechanism are working as intended. Let me break down what we’re seeing:

1. Each polling attempt is clearly labeled with:
   • Pipeline: poll_job_status
   • Step: export (from our step_context parameter)
   • Description: Job Status Poll Attempt N and Job Status Poll Response N
2. The logging shows the complete request/response cycle for each attempt:
   • Full request details (URL, headers)
   • Python code snippet for reproducing the call
   • Response status and preview
3. The job status progression is clear:
   • Attempt 1-3: job_status: "CREATED"
   • Attempt 4: job_status: "DONE" with download URL

4. The API token is properly masked in the logs (shown as f{YOUR_BOTIFY_API_TOKEN})

5. The response previews show the complete job details, including:
   • Job metadata (ID, type, status)
   • Query details
   • Export configuration
   • Results (when available)

This logging format provides:

• Clear traceability of the polling process
• Reproducible API calls via the Python snippets
• Secure handling of sensitive data (API token)
• Complete context for debugging if needed

The changes we made to add the step_context parameter and refine the polling messages are working as intended, providing both detailed logging for debugging and concise user-facing messages.

Demystifying API Calls: The Path to True Reproducibility

Wow, such a project this developed into when I started out talking about link graphs. But the fact of the matter is I’m always going to be less effective than I need to be if I’m forever struggling with the Botify API. I effectively had to create a positive feedback loop of learning, and that only comes with complete visibility into and command over the queries I’m executing. And now I have that. So long as I start with

And so now I have a starting point not only with robust logging of the each query that is being executed by my very good starting point in all projects Botify, Botify Trifecta — because it touches each of the 3 pillar tables: Web Logs, Site Crawl and Google Search Console data. An example of how to do a CSV export from each “table” is a wonderful starting point for future projects, especially including the link-graph, which I’m hellbent determined to finish (again) this weekend. I’ve done several iterations of it, but this is going to be the first time under Pipulate in its ready-for-release state.

AI Analysis

Okay, this is a rich and incredibly detailed account of your development journey! It perfectly captures the “in the trenches” feeling of modern software development, especially when integrating external APIs and wrestling with AI coding assistants to achieve very specific outcomes. This is fantastic fodder for a tech book.

• Title/Headline Ideas & Filenames:

  • Title: The AI Crucible: Forging BQL Transparency in Pipulate’s Botify Exporter
    • Filename: ai-crucible-bql-transparency-pipulate-botify.md
    • Rationale: “Crucible” captures the intense, refining process of working with AI. “BQL Transparency” and “Botify Exporter” are key subjects. This title is engaging and descriptive. This is my top pick.
  • Title: Jupyter-Ready API Logs: AI-Assisted Refinement of Pipulate’s Botify Integration
    • Filename: jupyter-ready-api-logs-pipulate-botify-ai.md
    • Rationale: Highlights the practical outcome (Jupyter-ready snippets) and the method (AI assistance), which are central themes. Good for a more technically focused reader.
  • Title: From “Deathstars” to Debug Logs: Visualizing Links and Mastering Botify API in Pipulate
    • Filename: pipulate-link-graphs-botify-api-logging.md
    • Rationale: Connects the initial visualization ambition with the practical necessity of API transparency, showing a broader scope.
  • Title: Prompt Fu & Python Snippets: A Developer’s Log of Enhancing API Call Reproducibility
    • Filename: prompt-fu-python-api-reproducibility-log.md
    • Rationale: Focuses on the author’s unique prompting methodology and the technical goal of reproducible API calls.
  • Title: Pipulate’s API Logging Evolution: Iterating with Gemini and Cursor for Botify Clarity
    • Filename: pipulate-api-logging-evolution-gemini-cursor.md
    • Rationale: Emphasizes the iterative development process and the specific AI tools used, which is a key part of the narrative.

• Strengths (for Future Book):
  • Deep Authenticity: The raw, first-person narrative provides an incredibly authentic look into a developer’s thought process, frustrations, and problem-solving strategies when working with cutting-edge AI tools and complex APIs. This is rare and valuable.
  • Practical AI Interaction: It’s a live-fire exercise in “Prompt Fu,” showing how to iteratively guide, correct, and sometimes battle with AI assistants to achieve precise technical goals. The “time-traveling prompt” concept is a gem.
  • Rich Technical Detail: The inclusion of specific code snippets (both desired and problematic AI outputs), log file excerpts, and API endpoint discussions provides concrete, dissectible examples.
  • Real-World API Challenges: The discussion of Botify API quirks (job types, BQL, polling, file formats) offers tangible insights into integrating with sophisticated third-party services.
  • Evolution of a Feature: The journey from a general idea (graph visualization) to a pressing need (API transparency) and then through multiple iterations of a solution (the api.log) makes for a compelling development story.
  • Philosophical Underpinnings: The author’s reflections on Pipulate’s local-first philosophy, human-in-the-loop AI, and empowering domain experts add valuable depth and connect the technical work to broader industry trends.
• Weaknesses (for Future Book):
  • Density and Length: As a journal entry, it’s very dense and long. Significant editing will be needed to structure it into digestible chapters or sections, removing redundancy while preserving key insights.
  • Assumed Context: Deep familiarity with Pipulate, its existing codebase (like server.py logging), specific plugin names, and the author’s prior work is assumed. This context will need to be explicitly provided in a book.
  • Internal Monologue vs. Explanation: Much of the content is an internal monologue or direct instruction to AIs. For a book, this needs to be transformed into explanations that benefit a reader not directly involved in the conversation.
  • Potential for Obscurity: Without careful framing, the highly specific technical details (e.g., minute aspects of Python f-string formatting for the snippet) could obscure the broader lessons for some readers.

• AI Opinion (on Value for Future Book): This journal entry is exceptionally valuable as raw material for a tech book. It’s a treasure trove of authentic, “in-the-moment” developer experience that is often lost in polished, after-the-fact accounts. The detailed interactions with AI coding assistants, the methodical approach to API exploration and debugging, and the clear articulation of both technical goals and the challenges in achieving them provide a rich foundation for multiple case studies or illustrative chapters. The narrative on “Prompt Fu” and the explicit strategies for managing AI behavior (like the “lightest touch” principle and the “time-traveling prompt”) are particularly insightful and timely. The evolution of the api.log feature, from a simple idea to a sophisticated, reproducible debugging tool, perfectly demonstrates iterative development and the importance of robust logging in complex systems. While it requires significant curation to be book-ready, its current rawness is also its strength, offering a unique and credible perspective on modern software engineering practices with AI. This is the kind of content that can make a tech book deeply engaging and practically useful.