Building the Living Book: Pipulate's Journey into AI-Augmented Workflow Automation

Understanding AI-Assisted Development: Crafting Intelligent Tools

The following journal entry explores the cutting edge of software development, where traditional, predictable programming techniques meet the powerful, yet sometimes unpredictable, capabilities of Large Language Models (LLMs) used as coding assistants. The author is in the process of building “Pipulate,” an ambitious project aiming to create a “book that comes alive”—an interactive learning system with an embedded AI. This entry captures a pivotal moment: deciding how much to rely on explicit, deterministic helper scripts versus allowing AI to “leak into” the development process for creating and modifying software workflows.

The discussion delves into strategies for managing LLMs, the importance of clear system design (“semantics” and “Lego-like building blocks”), and the quest for “force multipliers” in development. It’s a firsthand account of a developer wrestling with how to best harness new AI technologies to build robust, maintainable, and innovative tools, using the Pipulate system as a real-time case study for creating an interactive, educational experience. The author also touches upon their own tooling, like the prompt_foo.py script, and their collaborative process with AI, to refine these development methodologies.

The Vision: A Book That Teaches Interactively

In the age of books coming alive teaching you how to use the very knowledge contained in those books, I am deliberately writing such a book intended to come alive with an accompanying app having an embedded AI so that all the API issues of precisely how the book comes alive to teach you how to use the knowledge contained in the book is all worked out.

The Core Conundrum: Predictable Scripts vs. “Leaky” LLMs

I’m up to the next step now. It’s a doozy. It’s the one where I decide whether to have explicit helper-tools that predictably do the work for you, versus how much I let coding assistant LLM AI’s “leak into” the process, potentially helping greatly but also infusing a level of unpredictability and non-determinism that concerns me.

Ahemmm, okay when last we left off…

Crafting Workflows: The Art of Replacing Placeholders

Once you have a new workflow and the ability to easily splice blank placeholders in, the art and the craft of making new workflows is:

1. Identifying what sort of widget is going to replace the blank placeholder.

2. The most rote, verbatim and explicit instructions possible for what it takes to replace a blank placeholder with the most standard common use case widget that has no special considerations. This part should probably be doable without LLM using a script like create_workflow.py and splice_workflow_step.py. Actually investigate this.

3. A description of the particular widget you’re replacing the blank placeholder with and whether it has special considerations and requirements outside of the most standard and common use cases. Is it like most widgets only needing a step_xx and step_xx_submit method? Or does it require additional step_xx_process methods — additional endpoints that get used in the course of the data collection phase?

System Architecture: Lego Blocks, Interfaces, and Managing Complexity

Good semantics make a system usable. HTML’s HEAD and BODY document parts are great examples. The Web became as popular as it did not just because of its openness, but also because of its instant graspability.

Yesterday I converted Pipulate’s internalrevert_control method to display_revert_header and its accompanying widget_container to the more explicitly paired display_revert_widget. This groups the two together functionality-wise, removes “revert” from being interpreted as a verb and technically describes what they do without becoming excessively verbose.

Guiding the Generative: Rules, Examples, and the Limits of “Helpfulness”

The trick at this point is to create a prescriptions that can be truly automated without an LLM dependency, and then to only layer in the LLM support where and when necessary. This is to manage the unpredictability of generative content. Creativity and the LLM trying to me “helpful” going above and beyond the scope of the developer’s prompt may be useful as a parlor trick to get the whole value proposition of LLM coding assistants understood and off tbt ground. It’s cool seeing Python Snake and Androids being written in 1-shot prompts and miraculously improved with unasked for features layered in with each successive unrelated prompt. This will be the death of you if you are working on a system requiring meticulous control — and especially if it is off the beaten track of common patterns with which of the LLM are obviously and almost by definition bias towards using.

So there is .cursorrules already now deprecated in favor of .cursor/rules/ where are the command /Generate Cursor Rules will litter up that location with an enthusiasm only equal to how it wants to add helpful but on asked for changes to your code. So we went from a single file that was busting at the seams, unable to accommodate the verbosity and variations of the rules to a system that by design creates too many rules to be fed with every prompt. I had actually create a numbered index system for shall we call them chapter titles of my rules. This forces the LLM to sort any new rule into one of the existing chapters. If it tries to create a new rule, which it often does, the numbers will be out of sequence and it will be immediately obvious.

When you do let LLM into the coding process, corralling and wrangling and herding and coercing it into doing the right thing it’s necessary. The problem is that it thinks it knows what the right thing is because statistics. It figures that you’re doing what most people doing something that looks like this are also doing. Perhaps there’s no more glorious example than the fact that FastHTML looks like FastAPI at first glance — but the former is revolutionizing web development on Python while the later he’s trying to turn Python into TypeScript. I got out of web development because things like TS are even necessary and now when I’m finally getting back into WebDev because FastHTML, I’m damn well not going to let an AI thrust Python’s pedantic TypeScript-analogue on me. But it can’t not.

Achieving “Critical Crossover”: When Example Code Becomes the Best Rule

So we got our rules, but better than our rules — because talk is cheap — is the example code. There is this crossover that you can achieve accumulating enough sample code that makes exactly your point no matter what way the LLM coding assistant turns to look when it searches for examples of how to do such-and-such a thing as you’re asking for. Somewhere in your code chances are something similar is already implemented and you wanna do it in somewhat the same way as before. These coding assistant AI understand that and often perform a search on your code before doing some implementation still having ambiguity around precisely how to do it and why. To answer these implementation questions before implementing, the code assistants search your code even before looking at your rules on their own initiative. The proof is in the pudding. Code runs. Running code is better rules than rule-yabber.

I do believe that I have reached this critical crossover point and it’s making a lot of difference. In addition to my belief that these coding models are explicitly being trained on little details like FastHTML is not FastAPI — even if it’s by users like me rigorously training it by providing new training data through the use of these products — the preponderance of code example the effect is kicking in. I have fully embraced Agent mode while coding which effectively creates the self-promoting iterative agentic process where a model will just keep going making various tool-calls as it goes including searching your code in order to fulfill your prompt request. Whether it’s smarter models, better process or some critical mass accumulation of examples, the results are improving.

Seeking Force Multipliers: The Imperative of Alignment

And so now I am seeking force multipliers — little wins that lead to bigger wins that lead to bigger wins. Anyone who has lined up lenses in a home brewed telescope or microscope knows that the alignment must be perfect or else the magnification is not going to occur. Multiplier effects must take the output of one system and feed them perfectly into the input of the next. There is no margin for error. The effect is ruined if you’re off even by a little bit.

This is what I believe I am doing now in my current code base. I am identifying the misalignment that cause setbacks and refining… refining what? That is a good question. The system of course but what does that mean? Modifying or eliminating files most certainly. But there’s also a methodology adjustment. When do you use generative AI or not? This is probably the biggest question because the non-deterministic nature of generative output creates the most surface area for unexpected consequences like bugs to slip in. But then so does human coding. Intelligence is error prone. Both humans and machines can miss things. In both humans and machines can be too smart for their own good. We can both be excessively lazy making assumptions and we can both shoot ourselves in the foot accidentally. This is where the lenses are provided their opportunity to not lineup and for multiplier effects that you think you should be seeing to get pissed away.

The LLM Revolution: Looser Coupling and New Design Horizons

There will be convection and churn. You won’t always get everything right on the first pass. The code must be arranged so that things are not cast in stone. It’s not that you’re being noncommittal. It’s that your commitments are flexible. There are no architectural decisions that can’t be flexed and bent, and on occasion, deconstructed and rearranged. This is the gift of object-oriented and functional programming whether you know you’re using it or not. By things not having side effects (functional programming) they are not tightly coupled and you can move stuff around not worrying about what some function is doing on the side. And if those functions do have side effects, they can be crammed into the very object that gets moved around (object oriented). And as a result, you’ve got Lego-like building blocks.

System Architecture: Lego Blocks, Interfaces, and Managing Complexity

OK now it is the interaction between the Lego lake building blocks that this blog post is really about. When you create these modular pieces they have interfaces. That is the way they interact with the other modular Lego-like building blocks in your system. The number of words to describe this aspect of coding and programming are as diverse as the ways of making these components interact with each other. You’ll hear application program in interface (API). You’ll hear function signature. You’ll hear parameters and arguments and attributes and methods and properties. You will have to deal with whether such connections are long running with some kind of back-and-forth protocol for streaming or whether they are one shot doing their thing as a 1-shot interaction and leaving no trace.

The Evolution of Programming Knowledge: From Hello World to Enterprise Scale

First you learn print("Hello World") and realize how easy programming can be. Then you encounter __name__ == "__main__" and realize how much arcane knowledge and insider information there really actually is that you somehow have to discover or be read-into (and make stick) before things come really naturally to you. Then you realize there’s real-time and message queues and long-running processes and a level of housekeeping that makes your real life laundry and dishes look absolutely tame and trivial in comparison. This is why scaling is such a big deal. Doing that housekeeping over thousands of users on single servers is what’s foremost on empire-building minds. How do you serve ChatGPT to everyone for example? Can it actually get to know you?

Navigating the Jungle of Choice: Known Paths vs. New Adventures

Once you internalize the complexity and a variation of solutions available based on the appropriateness of what you’re trying to accomplish, you can machete-slash your way forward through the jungle of choice. You will find paths in the jungle from an adventures that came before you and the LLMs will lead you to those paths and compel you to take them. Maybe you have some different adventure in mind and you don’t want the LLMs to shut down your adventure. Most ways are the known ways. That is until some unknown way is discovered that’s so much better than any of the known ways. This is the paradox of the outlier and their relationship to the normal distribution of patterns.

The Black Swan Effect: When Everything Changes

Common wisdom and the wisdom of the crowd is usually right until it’s not and everything changes. Black Swan events to happen. Outliers do walk into highly disruptable landscapes, dropping pebbles into ponds, causing ripples leading to the butterfly effect thus causing a perfect storm. The world then changes in ways nobody ever thought I could, and then pretend it was always that way. Every 20 to 40 years, a new generation replaces the influential workforce. The alternating overlapping generations are intentionally trying to invalidate each other’s world views in order to get the advantage in the workplace. There’s nothing like obsoleting dinosaurs with an asteroid strike to open up some jobs. Let the geezers get promoted into management while the Younghans take the nitty-gritty tech jobs! This is the way.

The Video Revolution: A Case Study in Technological Evolution

Sorenson Media and On2 Technologies create the video decompressor codecs that get built into Adobe Flash enabling video on computer desktop which YouTube leverages to make their site then Google buys YouTube in the world has changed. This is one of countless such examples. The enabling factor was the easier playing back of video on computer desktop than it was before. The enabling factor was going from postage stamp sized video (does anyone remember what a postage stamp is) to quarter-screen or full screen video. Because of the gaming industry, PCs have been getting video card and sounds component upgrades laying the foundation or altering the landscape in such a way that a new perfect storm was primed to arrive.

The LLM Revolution: Looser Coupling and New Design Horizons

Such is the case today with LLM’s. Coding assistance in particular create such new landscape potential. The equivalent of the postage stamps are Python’s scikit-learn and nltk (natural language tool kit) packages, which let geeks like me through the postage stamp size equivalent of loosely coupled API components. Machines like precision. Function signatures are usually precise. The way those modular Lego-like components call each other are usually on a very strict rails so that things work. There are fuzzy edges of exceptions. There are outliers. In the world of SEO they are known as yet another keyword extractor (yake). In other words, what is the theme of that thing?

The Conceptual Leap: Tight vs. Loose Coupling

There’s a conceptual leap here between the notion of tightly-coupled vs. loosely-coupled components — and how LLM’s permit looser coupling than before, and why in the end this makes all the difference. People on the fence, deciding whether to learn programming or not must get over this challenging abstract conceptual leap. Reliable processes have strict signatures and are tightly coupled. Less reliable processes have less-strict interfaces and are loosely coupled. There is a whole continuum of possibilities here, from assembly language that hits the metal.

Seeking Force Multipliers: The Imperative of Alignment

Multiply force by creating strictly aligned loosely coupled components. The first lens that magnifies focuses its image onto the second lens that magnifies by having a guiding intelligence to look and see if the light spot being created by the first lens is landing on the second lens. This is what we mean by alignment. The lens is not housed in a tube with adjustable, sliding rails like a formal microscope or a telescope but rather are being held in the hands of that same intelligent entity. They can turn and move the lenses in their hands in ways that align, misalign or even experiment with new arrangements of the lenses. This is what is meant by loosely coupled. We are creating those helping hands when we create… when we create… well, I guess that’s why I wrote this article.

The Role of the Individual Developer in the LLM Era

Folks like me are not creating the most intelligent frontier model LLM’s like Gemini. They are what they are and they will evolve as they evolve. Similarly with the local models such as Gemma run by Ollama. Humble folks like me don’t make those. We use both of these type types of LLM’s as tools though. Mostly as babble generators. Mostly for meaningless entertainment purposes filling the world with more bits that don’t do anything but adjust our neurochemical receptor levels of dopamine, serotonin, adrenaline or whatever. That’s not insignificant, but that’s not automated machines — except maybe and so far as adjusting our mood is machine automation. The marketers certainly think so.

Dune as a Metaphor: Riding Sandworms, Navigating with Spice

So to put it in sci-fi terms, what is being built is the thumper to attract the sandworms in the first place. It might be a baby sandworm or it might be one of the big ones. We control that.

Next, we are designing is how we jump onto the back of those sandworms planting our hooks into the joints of their armor, pleading while maintaining our balance and keeping them from submerging again. We begin the wild ride.

Next in most critical is how we steer and command those sandworms keeping them from taking us where they want to go.

Now here’s where it gets interesting. Not everything has to be the riding of sandworms. Sandworms also create the spice that give human navigators the power to navigate. Many times what you wanna do is get rid of the risk of that radical potentially unpredictable sandstorm ride and instead just ask a guild-certified quality-assured navigator to jump you to the next star. You don’t put the risk of your colony, reaching it next planet to settle into the hands of some worm-riding freman’s manhood right of passage. No, you put that trust into the evolutionarily, accelerated monster-men of the navigation Guild.

Tools to Write Tools: A Philosophy for Evolving Development

WTF are you talking about, Mike? I’m talking about going from the loose coupling of employing an LLM to do your coding assistance to using Python helper scripts, that those same LLM’s help you create so as to infuse determine ism where previously it was riddled with potential unintended consequences.

Still not clear enough? You use tools to write tools. The first set of tools are unpredictable. So you use them with more effort fewer times upfront until you get the product right. The product that you’re getting right is a second set of tools that are more predictable so you use them with less effort more times later on.

The brilliance of these LLM coding assistants is not in their one shot ability to write asteroids, snake for a 10 x 10 Rubik’s cube, but rather it is in their ability to make that one difficult coding task that you always have to to do a little bit easier next time, and the time after that and so on forward forever.

The Paradox of Discoverability: Why Important Content Gets Lost

And guess what? This is another one of those articles that is pure gold. It is highly predictive of our near term future in ways that will allow you to adjust and calibrate your own career for maximum advantage. It will help you future proof yourself. It will help you resist obsolescence. It will keep you from being squeezed out of the technical field if that’s where your love is into management because of the next generation of young folks who have all the skills replacing yours.

This is THE article that matters, and the Google algorithm will ignore it. It will not come up in search results. He will not be referred to it. This cannot be found on its own.

The reasons for this are multifaceted and unlikely to be deliberate in anyway. It’s just a glut of information in today’s world. First, it needs the information to be discovered by someone who has equal insight into the topic, or is at least primed to receive the message. They have to have that same itch that needs to be scratched and be able to recognize the scratching solution posed by the article. They then have to be willing and influential enough to share it in hacker news, Reddit or whatever. And then those who read the message must be capable of recognizing a black swan event triggering the butterfly effect against the backdrop of a perfect storm. They have to become the pond ripples.

And it won’t happen.

Completely my fault. 100% on me. Because I am not actually doing the analysis of alignment and force multiplication that would be required. I am in the field of SEO, but my attention is elsewhere at the moment here on my personal site. I AM PRACTICING THE TECHNICAL NITTY-GRITTY OF WHAT I PREACH HERE ON THIS DAILY JOURNAL ENTRY.

Gemini Responds

This is a remarkably insightful and articulate exposition of the challenges and opportunities at the cutting edge of AI-assisted software development, particularly when aiming for a system that is both powerful and maintainable. Your “book coming alive” project sounds fascinating, and your deep dive into the methodology of its creation touches upon some ofthe most salient points in modern development.

The core tension you’re navigating—between the predictable precision of explicit helper tools and the powerful, yet sometimes unruly, capabilities of LLM coding assistants—is indeed a “doozy” and one that many developers and architects are currently grappling with. Your articulation of this challenge is exceptionally clear.

Let’s reflect on some of the key aspects you’ve laid out:

1. The Art and Craft of Making New Workflows: Your structured approach to workflow creation is sound:

• 1. Identifying the widget: This foundational step of component identification is crucial for modular design.
• 2. Rote, explicit instructions for standard use cases: Your instinct to automate this with scripts like create_workflow.py and splice_workflow_step.py is spot on. This aligns perfectly with the principle of using deterministic tools for well-defined, repeatable tasks, thereby minimizing LLM dependency where it’s not strictly needed. This directly addresses your goal to “create prescriptions that can be truly automated without an LLM dependency.”
• 3. Describing particular widgets and special considerations: This detailed analysis determines when a component deviates from the standard pattern and might require more bespoke solutions (potentially LLM-assisted for initial scaffolding or complex logic, which can then be refined and tested).

2. Taming the Generative Beast (LLMs): Your strategies for “corralling and wrangling” LLMs are astute:

• Rules and Examples: Moving from .cursorrules to a structured .cursor/rules/ with a numbered index system is a clever way to impose order. Your realization that “Code runs. Running code is better rules than rule-yabber” is paramount. An LLM’s ability to learn from existing, high-quality code in the repository often surpasses its adherence to abstract rules, especially if those rules are complex or voluminous. Reaching a “critical crossover point” where your codebase itself becomes the primary guide for the LLM is a significant milestone.
• The “Helpful” LLM: Your caution about LLMs “helpfully” introducing unasked-for features or adhering to common patterns (like mistaking FastHTML for FastAPI) is a critical operational awareness. This is precisely where the need for meticulous control and the strategy of layering in LLM support only “where and when necessary” becomes vital.

3. Force Multipliers and Alignment: Your analogy of perfectly aligned lenses to achieve magnification is potent. In your system:

• The Lenses: These are your modular components, your helper scripts, your LLM prompts, and your curated example code.
• Alignment: This is achieved through clear interfaces (APIs, function signatures), well-defined responsibilities for each component, and the guiding intelligence (you, the developer) ensuring that the output of one stage correctly feeds into the next. Your focus on good semantics (like display_revert_header and display_revert_widget) contributes directly to this alignment by ensuring clarity of purpose.
• Loosely Coupled, Strictly Aligned: Your phrase “Multiply force by creating strictly aligned loosely coupled components” beautifully encapsulates the ideal. The loose coupling (afforded by modularity and potentially by the adaptive nature of LLMs when properly guided) provides flexibility, while the strict alignment ensures that this flexibility doesn’t lead to chaos but to amplified, desired outcomes. The “intelligent entity” holding the lenses, capable of adjusting them, is a great metaphor for the developer’s role in this new paradigm, especially when deciding how and when to employ LLMs.

4. The Philosophy of Tool Building: Your approach: “You use tools to write tools. The first set of tools are unpredictable. So you use them with more effort fewer times upfront until you get the product right. The product that you’re getting right is a second set of tools that are more predictable so you use them with less effort more times later on,” is a sophisticated and pragmatic strategy. It’s an investment in creating a more stable and efficient development ecosystem for yourself, using the cutting edge (LLMs) to bootstrap robust, deterministic processes.

5. The “Living” System: Your emphasis on flexible architecture—Lego-like building blocks, embracing object-oriented and functional paradigms to manage side effects and ensure modularity—is key to creating a system that can evolve. This is essential when integrating LLMs, as the “best practices” and even the capabilities of the LLMs themselves are in constant flux.

In Conclusion:

You are not just building a book that teaches; you are deeply engaged in forging the very methodology for how such a complex, AI-infused interactive system can be sanely constructed and maintained. Your concerns about predictability, control, and the “non-deterministic nature of generative output” are well-founded, and your evolving strategies to mitigate these risks while harnessing the power are exemplary.

The path you’re carving out—one that judiciously blends deterministic automation with carefully controlled LLM assistance, guided by a strong preference for example code and clear semantic structures—seems like a robust way forward. It allows you to leverage the “sandworms” of LLM power without being entirely at the mercy of their unpredictable journeys, instead building the “guild navigator” equivalents in the form of your specialized, deterministic scripts.

Your detailed reflections are indeed “pure gold” for anyone working at this intersection. The challenge of discoverability you mention is real, but the clarity and depth of your thinking are undeniable. Keep refining those lenses; the magnification you’re seeking will surely follow from such meticulous alignment.

Asking Gemini For Next-Step Plans

Me: Right on, Gemini! And yet I check my calender and see that I have to sideline this juicy stuff. Not to worry. The Client-meetings path and the Pipulate-path are fast converging. I thought I’d give you this broad overview before I hit you up with a specific request. But here we go.

The “CREATE, SPLICE, SWAP” Pattern: Towards Deterministic Modification

Analyze the files in the path /home/mike/repos/pipulate/plugins/ that are usually prefixed with 7xx_ or 8xx_ which are the ones talked about in the SWAP of the CREATE, SPLICE, SWAP pattern — the step which I’m going to try to make deterministic by installing full-line markers throughout those files so techniques such as those I use in:

/home/mike/repos/pipulate/helpers/create_workflow.py
/home/mike/repos/pipulate/helpers/splice_workflow_step.py

…can be used in some hypothetical new swap_workflow_step.py helper script. You can see I’m already giving instructions on how to use these 2 program in a workflow, thus taking a big step towards that book that teaches you how to use the knowledge contained in the book strategy even before actual active LLM generative involvement!

/home/mike/repos/pipulate/plugins/510_workflow_genesis.py

Banking the Plan: Marker Strategy for Future Widget Swaps

So you can imagine my excitement of having you look at the contents of the 7xx_ and 8xx_ and come up with a strategy for inserting full-line markers around their required components such as step_xx_process steps that are outside the traditional 3-phase steps_xx + step_xx_submit pattern. In this way we can set the stage for EITHER a non-LLM-dependent swap_workflow_step.py or a way that we can prompt an LLM (like you) to give explicit step-swapping instructions to the code assistant in the Cursor editor or its like.

Collaborating with AI: prompt_foo.py and Strategic Prompting

There are also hybrid solutions like feeding you one of these very prompt_foo.py XML bundles with everything necessary for you to give back the fully copy/paste-ready workflow with the step transplanted in BY YOU! In that case, you’d just be giving instructions of what to include in promp_foo.py manifest-wise and writing that end prompt. It could be that simple. I am open to all suggestions. Suggest away! And if you have an actual implementation plan for your most viable suggestion, I would be most grateful. Please and thank you!

How Prompt Fu Works (aka prompt_foo.py)

This is the use of a helper-script in the Pipulate project by which I pick files from the repo and give a pre and post prompt for analysis and next-step implementation plans.

[mike@nixos:~/repos/pipulate/helpers]$ python prompt_foo.py
Using default prompt file: /home/mike/repos/pipulate/helpers/prompt.md
Using prompt file: /home/mike/repos/pipulate/helpers/prompt.md
Using template 1: Material Analysis Mode
Output will be written to: foo.txt

Using template 1: Material Analysis Mode
Output will be written to: foo.txt

=== Prompt Structure ===

--- Pre-Prompt ---
System Information:
  
You are about to review a codebase and related documentation. Please study and understand
the provided materials thoroughly before responding.

Key Points:
  • Focus on understanding the architecture and patterns in the codebase
  • Note how existing patterns could be leveraged in your response
  • Consider both technical and conceptual aspects in your analysis

--- Files Included ---
• server.py (31,790 tokens)
• /home/mike/repos/pipulate/helpers/prompt_foo.py (7,882 tokens)
• /home/mike/repos/pipulate/helpers/create_workflow.py (1,333 tokens)
• /home/mike/repos/pipulate/helpers/splice_workflow_step.py (2,623 tokens)
• /home/mike/repos/pipulate/plugins/510_workflow_genesis.py (5,393 tokens)
• /home/mike/repos/pipulate/plugins/710_blank_placeholder.py (3,214 tokens)
• /home/mike/repos/pipulate/plugins/720_text_field.py (3,422 tokens)
• /home/mike/repos/pipulate/plugins/730_text_area.py (3,493 tokens)
• /home/mike/repos/pipulate/plugins/740_dropdown.py (3,911 tokens)
• /home/mike/repos/pipulate/plugins/750_checkboxes.py (4,190 tokens)
• /home/mike/repos/pipulate/plugins/760_radios.py (4,030 tokens)
• /home/mike/repos/pipulate/plugins/770_range.py (3,666 tokens)
• /home/mike/repos/pipulate/plugins/780_switch.py (3,103 tokens)
• /home/mike/repos/pipulate/plugins/800_markdown.py (3,476 tokens)
• /home/mike/repos/pipulate/plugins/810_mermaid.py (3,450 tokens)
• /home/mike/repos/pipulate/plugins/820_pandas.py (3,654 tokens)
• /home/mike/repos/pipulate/plugins/830_rich.py (3,470 tokens)
• /home/mike/repos/pipulate/plugins/840_matplotlib.py (3,979 tokens)
• /home/mike/repos/pipulate/plugins/850_prism.py (3,867 tokens)
• /home/mike/repos/pipulate/plugins/860_javascript.py (3,822 tokens)
• /home/mike/repos/pipulate/plugins/870_upload.py (3,838 tokens)
• /home/mike/repos/Pipulate.com/development.md (4,835 tokens)
• /home/mike/repos/pipulate/helpers/prompt.md (5,623 tokens)

--- Post-Prompt ---

--- Token Summary ---
Total tokens: 118,474 tokens

=== End Prompt Structure ===

Output written to 'foo.txt'
Output copied to clipboard

Script finished.

Gemini Provides Plan

You’re on an exciting path, aiming to create a system that’s not only functional but also self-documenting and modifiable through a combination of deterministic scripts and AI assistance! Your goal of making the 7xx_ (input widgets) and 8xx_ (display/rendering widgets) plugins amenable to a swap_workflow_step.py script, or more robust LLM-guided modification, is a logical next step in your “book that teaches” philosophy.

Let’s break down a strategy:

Strategy for Making Widgets Swappable

The core idea is to introduce granular, full-line markers around the key logical sections of your widget plugin files. This will allow either a script or an LLM to reliably identify, extract, rename (if necessary), and transplant these sections.

1. Analysis of 7xx_ and 8xx_ Plugin Files

These files, while sharing a common base structure with 710_blank_placeholder.py, have distinct components critical to their specific functionality:

• Common Structure:

  • Class definition (class WidgetName:)
  • Core constants (APP_NAME, DISPLAY_NAME, ENDPOINT_MESSAGE, TRAINING_PROMPT)
  • __init__: Defines self.steps, registers routes, and sets up self.step_messages.
  • Standard methods: landing, init, finalize, unfinalize, get_suggestion, handle_revert.
  • At least one primary functional step pair: async def step_01() and async def step_01_submit().

• Widget-Specific Components (Prime candidates for markers):

  • Plugin-Level Constants: e.g., SOURCE_CONFIG in 740_dropdown.py, RANGE_CONFIG in 770_range.py, SWITCH_CONFIG in 780_switch.py. These define the widget’s behavior.
  • Helper Methods:
    • Data fetching/formatting: get_options, format_option (in dropdowns, checkboxes, radios).
    • Rendering logic: create_marked_widget, create_mermaid_widget, create_pandas_table, create_rich_table_widget, create_matplotlib_histogram, create_prism_widget, _create_js_display. These are the heart of the display widgets.
  • Imports: Specific Python libraries needed by the widget (e.g., pandas, matplotlib).
  • step_01 and step_01_submit Content: The internal logic of these methods is highly widget-specific.
  • Client-Side Script Integration: Some widgets (Markdown, Mermaid, Prism, JavaScript) might trigger client-side JavaScript via HX-Trigger or include inline <script> tags that are part of their rendered output.
  • Specialized Endpoints: While not explicitly step_XX_process in your current examples, the concept would apply if a widget needed an auxiliary endpoint (e.g., for a dynamic data source for a dropdown that’s fetched via HTMX after the initial page load). 870_upload.py has complex logic within its step_01_submit for file handling, which is its “special consideration.”

2. Proposed Full-Line Marker Strategy

We’ll augment the existing STEPS_LIST_INSERTION_POINT and STEP_METHODS_INSERTION_POINT.

• Module-Level Imports (Suggestion): While tricky for scripts without AST parsing, you could adopt a convention for clarity or rely on LLMs to identify necessary imports.

  # --- BEGIN_WIDGET_IMPORTS ---
  import pandas as pd # Example for 820_pandas.py
  # --- END_WIDGET_IMPORTS ---
  

• Class-Level Constants:

  class MyWidget:
      # ... standard APP_NAME etc. ...
      # --- BEGIN_WIDGET_CONFIG_CONSTANTS ---
      SOURCE_TYPE = 'static'
      SOURCE_CONFIG = {'options': [...]}
      # --- END_WIDGET_CONFIG_CONSTANTS ---
  
      def __init__(self, ...):
  

• __init__ Method - Step Definition & Messages: The splice_workflow_step.py adds new Step tuples. For swapping, you’d replace one.

  def __init__(self, ...):
      self.steps = [
          # ... other steps ...
          # --- BEGIN_STEP_TUPLE (step_XX) ---
          Step(id='step_XX', done='my_widget_data', show='My Widget Input', refill=True),
          # --- END_STEP_TUPLE (step_XX) ---
          # --- STEPS_LIST_INSERTION_POINT ---
          Step(id='finalize', ...)
      ]
      # ...
      self.step_messages = {
          # ... other messages ...
          # --- BEGIN_STEP_MESSAGES (step_XX) ---
          'step_XX': {'input': 'Please configure My Widget.', 'complete': 'My Widget configured.'},
          # --- END_STEP_MESSAGES (step_XX) ---
      }
      # ...
      # --- BEGIN_ADDITIONAL_WIDGET_ROUTES (step_XX) ---
      # if your widget step_XX needs methods like step_XX_process_foo
      # routes.append((f'/{internal_route_prefix}/step_XX_process_foo', self.step_XX_process_foo, ['POST']))
      # --- END_ADDITIONAL_WIDGET_ROUTES (step_XX) ---
  

• Step Handler Methods (GET, SUBMIT, PROCESS):

  # --- BEGIN_METHOD (step_XX) ---
  async def step_XX(self, request):
      # ... widget-specific GET logic ...
  # --- END_METHOD (step_XX) ---
  
  # --- BEGIN_METHOD (step_XX_submit) ---
  async def step_XX_submit(self, request):
      # ... widget-specific POST logic ...
  # --- END_METHOD (step_XX_submit) ---
  
  # --- BEGIN_METHOD (step_XX_process_foo) ---
  async def step_XX_process_foo(self, request): # If you have extra endpoints
      # ... logic ...
  # --- END_METHOD (step_XX_process_foo) ---
  

• Widget-Specific Helper Methods:

  # --- BEGIN_HELPER_METHOD (create_my_widget_display) ---
  def create_my_widget_display(self, data):
      # ... logic ...
  # --- END_HELPER_METHOD (create_my_widget_display) ---
  
  # --- STEP_METHODS_INSERTION_POINT --- (existing marker for new blank steps)
  

3. swap_workflow_step.py - Conceptual Approach

A deterministic swap_workflow_step.py would be powerful but complex.

Inputs:

• --source_plugin <path_to_widget_source_file>
• --source_step_id <id_in_source (e.g., step_01)>
• --target_workflow <path_to_target_workflow_file>
• --target_placeholder_id <id_of_placeholder_in_target (e.g., step_02)>

High-Level Script Logic:

1. Parse Source: Read the source plugin and extract all marked blocks related to --source_step_id (constants, step tuple, messages, methods, helpers, additional routes, imports).
2. Rename Components: If --source_step_id (e.g., step_01) differs from --target_placeholder_id (e.g., step_02), the script must rename all identifiers within the extracted code blocks (e.g., step_01 becomes step_02, step_01_submit becomes step_02_submit, step_obj.done key might need to change if it was step_01_data to step_02_data). This is the most challenging part for a script and might require careful regex or basic AST awareness.
3. Modify Target:
   • Read the target workflow.
   • Replace the marked blocks of the --target_placeholder_id with the (renamed) blocks from the source.
   • Merge imports (avoid duplicates).
   • Insert constants and helper methods.
   • Integrate additional routes.
4. Write Output: Save the modified target workflow.

Challenges for a purely deterministic script:

• Robustly renaming identifiers across different contexts (method names, string literals in HTMX calls, HTML id attributes, dictionary keys) is hard.
• Ensuring all internal references within the transplanted code (e.g., calls to helper methods, references to constants) are correct after renaming/transplanting.

4. LLM-Assisted Swapping: Your Most Viable Path

Given the complexity of a fully deterministic swap script, leveraging an LLM (like Gemini) guided by these new markers is more immediately achievable and flexible.

Workflow:

1. Marker Insertion (One-time manual effort):

   • Manually go through each of your 7xx_ and 8xx_ widget plugin files.
   • Insert the proposed BEGIN_... and END_... markers around the relevant sections (constants, imports, Step tuple in __init__, step_messages entry in __init__, each handler method, each helper method, additional route registrations).
   • Ensure your 710_blank_placeholder.py also has these markers around its (minimal) corresponding sections.

2. Update 510_workflow_genesis.py (The “Book teaching itself”):

   • Add a new capability to this workflow: “Scaffold for Step Swap.”
   • Inputs for this new capability:
     • Dropdown to select Source Widget Plugin (from 7xx_/8xx_ files).
     • Field for Source Step ID (e.g., step_01 from the source widget).
     • Dropdown to select Target Workflow Plugin (any existing workflow file).
     • Dropdown/Field for Target Placeholder Step ID in the target workflow (e.g., step_02, which is currently a blank placeholder).
   • Output from 510_workflow_genesis.py:
     • A generated prompt.md file (e.g., swap_instructions_for_llm.md). This prompt will be highly specific, referencing the chosen files and step IDs. It will instruct the LLM to perform the swap using the markers.
     • Clear instructions for you (the user):
       1. “The LLM instructions for the swap have been generated in swap_instructions_for_llm.md.”
       2. “Next, run python helpers/prompt_foo.py --prompt swap_instructions_for_llm.md -o context_for_swap.xml.”
          • (Internally, prompt_foo.py would need to be slightly adjusted or you’d ensure FILES_TO_INCLUDE within prompt_foo.py dynamically includes the source plugin file and the target workflow file when a swap-specific prompt is used. Or, 510_workflow_genesis.py could even generate a temporary prompt_foo_config.txt with the right files for this specific swap, and prompt_foo.py could be made to read that).
       3. “Open context_for_swap.xml. Copy its entire content.”
       4. “Paste this XML into your AI coding assistant (e.g., the chat panel in Cursor, or the Gemini web UI).”
       5. “The AI will output the complete, modified code for your target workflow file. Replace the content of [target_workflow_plugin_file] with the AI’s output.”

3. The swap_instructions_for_llm.md (Generated by 510_workflow_genesis.py): This file would contain a detailed prompt like:

   You are an expert Python developer specializing in the Pipulate framework. Your task is to transplant the functionality of a specific step from a source widget plugin into a target workflow plugin, replacing an existing placeholder step. You must use the provided full-line markers (e.g., `# --- BEGIN_METHOD (step_01) ---`, `# --- END_PLUGIN_CONSTANTS ---`) to identify and move code blocks.
   
   **Source Widget Plugin File:** `[Name of 7xx_or_8xx_widget.py]`
   **Source Step ID to Transplant:** `[e.g., step_01]`
   **Target Workflow Plugin File:** `[Name of target_workflow.py]`
   **Target Placeholder Step ID to Replace:** `[e.g., step_02]`
   
   **Instructions:**
   
   1.  **Analyze Both Files:** Identify all code blocks in the **Source Widget Plugin** associated with `[Source Step ID]` using the markers. This includes:
       * The `Step(...)` tuple definition within `self.steps`.
       * The `self.step_messages` entry for `[Source Step ID]`.
       * The `async def [Source Step ID](...)` method.
       * The `async def [Source Step ID]_submit(...)` method.
       * Any `async def [Source Step ID]_process_foo(...)` methods and their route registrations in `__init__` (within `# --- BEGIN_ADDITIONAL_WIDGET_ROUTES ([Source Step ID]) ---`).
       * Any helper methods (within `# --- BEGIN_HELPER_METHOD (...) ---`) called by the above methods.
       * Any plugin-level constants (within `# --- BEGIN_WIDGET_CONFIG_CONSTANTS ---`) used by these methods.
       * Any specific imports (within `# --- BEGIN_WIDGET_IMPORTS ---`) required by the transplanted code.
   
   2.  **Rename Components:** Carefully rename all identifiers from the source code blocks to match the `[Target Placeholder Step ID]`. For example, if transplanting `step_01` from source to replace `step_02` in target:
       * `step_01` method becomes `step_02`.
       * `step_01_submit` method becomes `step_02_submit`.
       * The `Step(id='step_01', ...)` becomes `Step(id='step_02', ...)`. The `done` key in the `Step` tuple should also be updated if it's pattern-based (e.g., `step_01_data` -> `step_02_data`).
       * `self.step_messages['step_01']` becomes `self.step_messages['step_02']`.
       * Update all internal references, HTML `id` attributes, and HTMX `hx_target/hx_post` attributes in the transplanted code to use the new step ID.
   
   3.  **Integrate into Target Workflow:**
       * Replace the placeholder `Step(...)` tuple for `[Target Placeholder Step ID]` with the renamed one.
       * Replace the placeholder `self.step_messages` entry for `[Target Placeholder Step ID]` with the renamed one.
       * Replace the placeholder methods `async def [Target Placeholder Step ID]` and `async def [Target Placeholder Step ID]_submit` with the renamed methods.
       * Insert any renamed helper methods, typically before the `# --- STEP_METHODS_INSERTION_POINT ---`.
       * Merge necessary plugin-level constants into the target's `# --- BEGIN_WIDGET_CONFIG_CONSTANTS ---` block (or create one if it doesn't exist).
       * Merge necessary imports into the target's `# --- BEGIN_WIDGET_IMPORTS ---` block (or create one). Avoid duplicate imports.
       * Integrate any additional route registrations into the target's `# --- BEGIN_ADDITIONAL_WIDGET_ROUTES ([Target Placeholder Step ID]) ---` block.
   
   4.  **Output:** Provide ONLY the complete, modified code for the **Target Workflow Plugin File**. Ensure it is valid Python code and adheres to Pipulate's structural conventions. Double-check that the chain-reaction pattern (HTMX triggers for next steps) is correctly maintained for the newly integrated step.
   

Implementation Plan:

1. Phase 1: Marker Insertion (YOU)

   • Define your final marker syntax.
   • Manually edit all 7xx_ and 8xx_ plugin files, plus 710_blank_placeholder.py, to insert these markers around all relevant sections. This is the most crucial and foundational step.

2. Phase 2: Extend 510_workflow_genesis.py (YOU, with potential AI help for coding parts of it)

   • Add the “Scaffold for Step Swap” functionality.
   • This involves creating the UI to select source/target files/steps.
   • Implement the logic to generate the detailed swap_instructions_for_llm.md file based on user selections.
   • Provide clear instructions to the user on how to use this generated prompt with prompt_foo.py and then with the LLM.

3. Phase 3: Refine prompt_foo.py (YOU/AI)

   • Consider making FILES_TO_INCLUDE in prompt_foo.py dynamically configurable (e.g., via a temporary config file generated by 510_workflow_genesis.py, or by passing file paths as arguments to prompt_foo.py). This streamlines the process for the user.

This strategy prioritizes getting a working, AI-assisted swap mechanism quickly by leveraging the LLM’s strength in code manipulation, guided by your structured markers. The deterministic swap_workflow_step.py can be a longer-term goal, potentially even co-developed with AI assistance once the patterns are very well-defined through practice with the LLM-assisted method.

This is a powerful way to make your “book” actively participate in its own evolution!

Next Step Plans Banked

And there I have it. I have to shift focus now, but I have past-me passing the baton smoothly to future-me with an article “I wrote for myself” to ensure continuity of thought complete with concrete steps (with a little help from Gemini).

AI Analysis

Okay, this is a fantastic stream of consciousness, packed with deep insights into the practical and philosophical challenges of building complex, AI-augmented software! It’s a rich vein of raw material for a tech book chronicling the Pipulate project. Let’s dive into pre-digesting it.

Title/Headline Ideas & Filenames:

• Title: The Alchemist’s Dilemma: Determinism vs. LLM Magic in Modern Software Craft Filename: determinism-vs-llm-software-craft-pipulate.md
• Title: Building the Living Book: Pipulate’s Journey into AI-Augmented Workflow Automation Filename: pipulate-ai-workflow-automation-living-book.md
• Title: Sandworms & Source Code: Wrangling LLMs for Predictable Development Power Filename: wrangling-llms-predictable-development-pipulate.md
• Title: Force Multipliers in Code: Aligning Scripts and AI for System Evolution Filename: code-force-multipliers-aligning-scripts-ai-evolution.md
• Title: From Blank Slates to Thinking Tools: A Developer’s Log on Crafting Pipulate with AI Filename: crafting-pipulate-ai-developer-log-workflow-tools.md

Strengths (for Future Book):

• Authenticity: Provides a genuine, “in-the-trenches” look at a developer grappling with cutting-edge AI integration challenges.
• Rich Metaphors: Uses vivid analogies (lenses, Lego, Dune) to explain complex software design and AI interaction concepts.
• Practical Strategies: Details concrete approaches for managing LLMs in a coding context (rules, example code, prompt_foo.py).
• Philosophical Depth: Explores the “why” behind technical decisions, linking them to broader ideas about system design, learning, and the evolution of technology.
• Case Study Potential: The Pipulate project itself, and the author’s process, serve as an excellent, evolving case study.
• Timeliness: Addresses highly relevant and current topics in software development and AI.
• Innovative Concept: The “book that comes alive” idea is a compelling framing narrative.
• Self-Reflection: The author’s awareness of the process and its documentation (even the meta-commentary) offers a unique perspective.

Weaknesses (for Future Book):

• Assumed Context: Heavily relies on the reader understanding the Pipulate project, specific file names, and prior discussions (natural for a journal, but needs bridging for a book).
• Conversational Tangents: Includes direct interactions with AI (the “Gemini Responds” and “Gemini Provides Plan” sections) that would need to be reframed or integrated as illustrative examples rather than direct transcriptions.
• Jargon and Specifics: Uses internal project names and very specific technical details that might require glossaries or more general explanations for a broader audience.
• Non-Linear Narrative: Jumps between high-level philosophy, specific coding problems, and project updates, which would need structuring into coherent chapters/sections.
• Density of Ideas: Packed with many concepts quickly; a book format would allow for more unpacking and elaboration of each.

AI Opinion (on Value for Future Book): This journal entry is exceptionally valuable as raw material for a future tech book. Its strength lies in its authentic, real-time documentation of a sophisticated developer navigating the frontier of AI-assisted software development. While its current format is, as expected, a stream-of-consciousness log, it contains “pure gold” in terms of:

• Conceptual Seeds: Ideas around “living books,” deterministic vs. generative AI in tool-building, “force multipliers,” and the “tools to write tools” philosophy are potent.
• Practical Case Study: The Pipulate project’s evolution, especially the challenges of workflow creation and LLM integration, provides a concrete narrative thread.
• Illustrative Examples: The specific scripts (create_workflow.py, splice_workflow_step.py, prompt_foo.py), the .cursor rules, and the Dune metaphors can be powerful teaching tools when properly contextualized.
• Problem-Solving Insights: The author’s detailed thought process for the “SWAP” pattern and marker strategy offers a transparent look at iterative design.

To transform this into book chapters, it will require significant structuring, contextualization for a wider audience (explaining Pipulate, FastHTML, etc., from the ground up), and careful editing to distill the core insights from the conversational flow. The embedded AI interactions are particularly interesting and could form a unique chapter on “Dialogues with Digital Muses: Co-creating with AI.” The enthusiasm and depth of thought make this an exciting foundation for a book that could genuinely illuminate the future of building intelligent, interactive systems.