AI Collaboration in Action: Implementing HTMX Widgets for Pipulate Workflows

In this post, I’m walking through the process of planning and building a set of diverse UI widgets for Pipulate workflows, detailing how I used a comprehensive “super-prompt” with Gemini 2.5 to generate an implementation plan, and then worked with Claude 3.7 to code it using HTMX and FastHTML. We’ve successfully established the core framework for four widget types—basic HTMX, a Python Rich table, JavaScript execution, and Markdown rendering—getting the fundamental JavaScript injection via HTMX working. I also reflect on the resulting UX pattern that combines input and display in single steps and document this significant progress as a “banked win” before diving into the remaining JavaScript library activation nuances in the next stage.

By Mike Levin

Saturday, April 26, 2025

This article documents a developer’s process of adding new user interface components, called “widgets,” to a custom software project named Pipulate. Pipulate aims to simplify complex, step-by-step tasks (workflows), particularly in the field of Search Engine Optimization (SEO), presenting them as web applications. The developer is using modern web techniques like HTMX to create dynamic interfaces efficiently and Python for the backend logic.

A unique aspect detailed here is the heavy reliance on collaboration with Artificial Intelligence (AI) models (Gemini and Claude). The author uses large, detailed prompts (“super-prompts”) containing code and instructions to get AI assistance in planning the architecture and writing the code for these new widgets, which include things like data tables, text formatters, and interactive elements. The piece captures the real-time progress, challenges, and design decisions involved in this AI-assisted development approach.

I need a variety of widget types in Pipulate now. This opens the door to so many things! I need:

A d3js-style force-graph that loads in very small link-graphs for visualization.
A JSON-object explorer-style accordion surfer
Color-coded JavaScript with an easy copy-link that copies as plan text
A Markdown renderer (possibly related to the JavaScript one)
A little directory-tree to show where a file is located
Mermaid diagrams

Okay, so the theme here appears to be non-interactive JavaScript widgets for pure visualization. This alleviates the 2-way communication protocol integration need of AnyWidgets while still allowing some nifty and impressive data-visualizations. In theory, this should allow me to incorporate anything that has a client-side JavaScript visualizer that doesn’t demand opening communication protocols.

JavaScript Library Loading Strategy

The most efficient way to tackle this is to make a generic way of including just whatever JavaScript visualization library is needed, from either the CDN link or preferably from the local static resources directory — the way we’re doing htmx.js, mermaid.min.js and other libraries from the workflows. Pipulate Workflows can have an area where you just provide a list of the paths to the files. I think I will have to think about whether things are loaded as modules or not. But the idea is straight forward enough. No particular JavaScript library need be global to the system templates, but any can be optionally loaded into a particular Workflow from either CDN or local.

HTMX Integration Challenges

Now the things that are going to be the targets for these JavaScript libraries are actually going to be HTML fragments inserted into the DOM by FastHTML’s HTMX attributes, which will bring up certain scope, context and permission issues. Doing a positive proof of concept of a Pipulate Workflow JavaScript Widget that requires a JavaScript library provided at the initial page-load is probably the objective here. While I’m at it, I will toss a non-JavaScript fragment made by the Python Rich library as a sort of control — taking me a step closer to the back-end controlled Rich HTMX DataGrids that I want.

Leveraging Context Foo for Development

This looks like a job for Context Foo! First, let’s define what goes into this AI prompt besides this article:

FILES_TO_INCLUDE = """\
README.md
flake.nix
server.py
plugins/20_hello_workflow.py
plugins/50_botify_export.py
.cursorrules
/home/mike/repos/MikeLev.in/_posts/2025-04-21-fasthtml-anywidget-host.md
""".splitlines()[:-1]

And so I will make a special emphasis since I’m giving the whole widget story that I’m going for a vastly simplified version of this geared towards things like chart.js and other things that don’t need the 2-way communication with Python. All Python needs to do is get the JavaScript library or module loaded into the browser through the initial page-load and to generate code that creates the HTMX container element, and (through FastHTML) provide a way to issue HTMX commands that will replace the container contents with JavaScript — which needs to execute despite it having just been dynamically injected into the DOM!

To bank a success, we will actually do 4 simultaneous widgets:

A Hello World widget that creates Hello World with traditional HTMX (no JavaScript execution required)
A Rich-generated HTML table - to test that we can
A Hello World widget that creates Hello World with JavaScript in an HTMX-injected HTML fragment
The using of a very basic client-side renderer, whatever is easiest. We can do Markdown or a JSON explorer. I want to minimize issues and maximize the odds of success.

Implementation Strategy

So the prompt after having read all that is to create a new template using what you learn from 20_hello_workflow.py and 50_botify_export.py to create 60_widget_examples.py that has a step for each of the above types of widgets. You can look at the current implementation of widget_container and tree_display in server.py to see how this works in general.

python context_foo.py --article-mode --article-path /home/mike/repos/MikeLev.in/_posts/2025-04-25-htmx-workflow-widgets.md

Gemini 2.5 Experimental Takes Super-Prompt

Hopefully, I can do more than just this this weekend, but this is the critical blocker. I need to just be able to slam out different kind of widgets in workflows.

What is Context Foo you may ask. Well, I can’t show you the entire prompt I pasted into Gemini because that would be 337K or 7.8K lines of text in a giant XML file wrapped with Anthropic’s recommended schema for such things. But I can show you this bit that gives you an idea of what’s in it.

The Super-Prompt

[mike@nixos:~/repos/pipulate/precursors]$ python context_foo.py --article-mode --article-path /home/mike/repos/MikeLev.in/_posts/2025-04-25-htmx-workflow-widgets.md

=== Prompt Structure ===

--- Pre-Prompt ---
System Information:

You are about to review a codebase in preparation for implementing changes requested in an article.
Please study and understand the codebase thoroughly, as you will need this context
to provide meaningful implementation suggestions based on the article's requirements.

Key things to know about this codebase:
- It uses a hybrid approach with Nix for system dependencies and virtualenv for Python packages
- Always run `nix develop` before any commands in a new terminal
- FastHTML objects must be converted with to_xml() before returning responses
- The project is organized as a server with plugin-based workflows

Key Points:
  • Focus on understanding how the codebase currently implements related functionality
  • Note any existing patterns that could be leveraged for the requested changes
  • Consider how the requested changes would integrate with the current architecture

--- Files Included ---
• README.md (6,571 tokens)
• flake.nix (2,000 tokens)
• server.py (25,265 tokens)
• plugins/20_hello_workflow.py (6,617 tokens)
• plugins/50_botify_export.py (25,123 tokens)
• .cursorrules (688 tokens)
• /home/mike/repos/MikeLev.in/_posts/2025-04-25-htmx-workflow-widgets.md (903 tokens)

--- Post-Prompt ---
Introduction:
  
Now that you understand the codebase, please review the article's requirements and provide
specific implementation suggestions. Focus on how to modify the codebase to meet these requirements
while maintaining its architectural integrity and existing patterns.

Implementation Areas:
  • Required Changes
  • Integration Strategy
  • Implementation Plan

Focus Areas:
  • Practical implementation of the article's requirements
  • Maintenance of codebase integrity and patterns
  • Clear, actionable implementation steps

--- Token Summary ---
Total tokens: 67,659 tokens (≈$2.03 at GPT-4 rates)
Maximum allowed: 2,087,152 tokens (≈$62.61 at GPT-4 rates)
Remaining: 2,019,493 tokens (≈$60.58 at GPT-4 rates)

=== End Prompt Structure ===


--- Clipboard Instructions ---
Complete XML content successfully copied to clipboard using pyperclip.
You can now paste it.

Script finished.

Gemini’s Implementation Plan

Gemini 2.5 Experimental: Okay, I have reviewed the codebase and the article’s requirements for implementing various workflow widgets. Here’s a breakdown of the required changes, integration strategy, and implementation plan, keeping the project’s architecture and conventions in mind.

1. Required Changes

New Workflow Plugin (plugins/60_widget_examples.py):
- Create this new file, using plugins/20_hello_workflow.py and plugins/50_botify_export.py as templates.
- Define a class (e.g., WidgetExamples) with appropriate APP_NAME (“widgets”), DISPLAY_NAME (“Widget Examples”), ENDPOINT_MESSAGE, and TRAINING_PROMPT.
- Define four steps in the steps list within __init__, one for each widget type requested in the article.
- Implement step_XX (GET) and step_XX_submit (POST) handlers for each of the four steps.
Step Implementations:
- Step 1 (Simple HTMX):
  - step_01: Display a simple input form within a Card.
  - step_01_submit: Validate input, update state, and return the standard pip.revert_control component along with an HTMX trigger (Div) for the next step (step_02). This step serves as a baseline and won’t use the widget_container.
- Step 2 (Rich Table Widget):
  - step_02: Display an input form (e.g., asking for data to put in the table).
  - step_02_submit:
    - Validate input.
    - Import necessary rich components (Table, Console).
    - Create a rich.table.Table instance with some data.
    - Use rich.console.Console(record=True) and console.export_html(inline_styles=True) to capture the table as an HTML string.
    - Wrap the captured HTML string in fasthtml.common.HTML to treat it as raw HTML.
    - Update the workflow state.
    - Use pip.widget_container passing the HTML object as the widget parameter.
    - Return the resulting container and the trigger Div for step_03.
- Step 3 (JavaScript Execution Widget):
  - step_03: Display an input form.
  - step_03_submit:
    - Validate input.
    - Update workflow state.
    - Define the JavaScript to execute (e.g., alert('Hello from HTMX-injected script!'); or something that modifies the widget’s container).
    - Create the widget container Div using pip.widget_container. The widget parameter will be a Div containing a <script> tag with the JavaScript. Crucially, ensure the script tag can execute after HTMX injection. A reliable way is to have the script call a globally defined function from static/script.js.
    - Alternatively, and potentially more robustly, return the container without the script tag, but add an HX-Trigger header to the HTMLResponse that calls a global JavaScript function, passing necessary data (like the target widget container ID).
    - Return the widget_container result and the trigger Div for step_04.
- Step 4 (Client-Side Renderer Widget - e.g., Markdown):
  - step_04: Display a TextArea input form for Markdown text.
  - step_04_submit:
    - Validate input (the Markdown text).
    - Update workflow state, storing the Markdown content.
    - Create an empty Div to serve as the widget’s target container, giving it a unique ID (e.g., markdown-widget-{pipeline_id}-{step_id}).
    - Use pip.widget_container, passing the empty target Div as the widget.
    - Construct an HTMLResponse containing the rendered widget_container.
    - Add an HX-Trigger header to this response: response.headers["HX-Trigger"] = json.dumps({"renderMarkdown": {"targetId": "unique_target_id", "content": markdown_content}})
    - Return the HTMLResponse (which includes the trigger header) and the trigger Div for the finalize step.
Server Modifications (server.py):
- Headers (hdrs): Add the necessary CDN link or local path for the chosen client-side rendering library (e.g., Marked.js for Markdown) to the hdrs tuple within the fast_app call. This ensures the library is loaded on initial page load.
- widget_container: The existing pipulate.widget_container function seems suitable as it wraps provided content. No major changes seem necessary here, as the JS execution and client-side rendering logic will likely be handled via HX-Trigger and global JS functions.
Static JavaScript (static/script.js or new file):
- Global Functions: Define any global JavaScript functions needed for Step 3 (if using the function call approach) or Step 4 (e.g., renderMarkdownWidget(targetId, content)).
- Event Listener: Add an event listener (e.g., htmx.on("renderMarkdown", function(evt) { ... });) to handle the custom HX-Trigger event from Step 4, calling the appropriate rendering function with the provided targetId and content from evt.detail.

2. Integration Strategy

Leverage Existing Patterns:
- Follow the established workflow structure (__init__, landing, init, step_XX, step_XX_submit, finalize, etc.) as seen in 20_hello_workflow.py and 50_botify_export.py.
- Use the Pipulate helper class (self.pipulate) for state management (read_state, get_step_data, update_step_state), UI generation (revert_control, widget_container, create_step_navigation), validation (validate_step_input), and message queuing (message_queue.add).
- Adhere to the convention of using HTMX for UI updates, triggering subsequent steps via Div elements with hx-get and hx-trigger="load".
- For client-side interactions beyond simple HTML replacement (like initializing JS widgets), use the HX-Trigger header pattern combined with global JavaScript event listeners.
Widget Integration:
- Use the pipulate.widget_container method consistently for steps that require displaying content below the standard revert control (Steps 2, 3, 4). This ensures consistent layout and styling. Pass the generated HTML (from Rich) or the target container (for JS widgets) as the widget argument.
Backward Compatibility:
- Adding a new plugin (60_widget_examples.py) is inherently backward compatible.
- Adding a JS library link to the global hdrs in server.py should not affect existing workflows unless there are naming collisions (unlikely with standard libraries).
- Modifications to static/script.js should be additive (new functions, new event listeners) and avoid changing existing behavior.

3. Implementation Plan

Setup: Create plugins/60_widget_examples.py by copying 20_hello_workflow.py. Update class name and constants (APP_NAME, DISPLAY_NAME, etc.). Define the four steps in the steps list.
Implement Step 1 (Simple HTMX): Implement step_01 and step_01_submit handlers. Test basic workflow initialization and step progression.
Implement Step 2 (Rich Table):
- Add rich import.
- Implement step_02 and step_02_submit, including Rich table generation and HTML export.
- Integrate with pip.widget_container. Test rendering.
Implement Step 4 (Client-Side Renderer - e.g., Markdown): (Doing Step 4 before 3 might be easier as it establishes the JS library loading and HX-Trigger pattern)
- Choose a library (e.g., Marked.js).
- Add the library JS link to hdrs in server.py.
- Add the renderMarkdownWidget function and htmx.on("renderMarkdown", ...) listener to static/script.js.
- Implement step_04 and step_04_submit handlers, returning the empty container Div and the HX-Trigger header. Test Markdown rendering.
Implement Step 3 (JS Execution):
- Implement step_03 and step_03_submit.
- Decide on the JS execution method (inline <script> calling global function, or HX-Trigger calling global function). The HX-Trigger approach used in Step 4 is likely more robust and recommended. Implement accordingly. Test JS execution.
Finalize and Test: Implement the finalize step logic (can be minimal for this example). Test the full workflow, including reverting steps, finalizing, and unfinalizing, paying close attention to how the widgets behave during these state changes. Ensure JS widgets re-initialize correctly if needed after reverts or page refreshes within the workflow.
Documentation: Update README.md or add comments explaining the new widget patterns demonstrated in 60_widget_examples.py.

Potential Challenges & Risks:

JavaScript Execution: Ensuring JavaScript within HTMX-swapped content executes reliably can be tricky. Using global functions called by inline scripts or, preferably, HX-Trigger-based events is the most common solution.
JS Library Initialization: Client-side libraries often need initialization code. This needs to run after the target container is swapped into the DOM. The HX-Trigger pattern handles this well.
Scope and Context: JavaScript executed via HTMX might have unexpected scope issues. Using global helper functions minimizes this.
HTMX Swapping Complexity: Ensure the hx-target and hx-swap attributes are correctly configured, especially when using widget_container, to replace the right parts of the UI without breaking the overall step structure or the HTMX trigger chain for subsequent steps (unless intended, like with polling or terminal responses). The no-chain-reaction class used in 50_botify_export.py for polling is a good example of intentionally breaking the chain.

By following these steps and leveraging the existing Pipulate helpers and HTMX patterns, you can successfully implement the requested workflow widgets while maintaining the codebase’s integrity. Remember to test each step thoroughly, especially the JavaScript interactions.

Summarizing What Widgets Do

I ran out of steam and didn’t get to the finish-line. The idea is to bank our winnings this far in this project to have a natural continuation point for myself or AI’s picking up where we left off. Unfortunately for you dear reader, that’s going to make for a very long article where I choose to keep it all in one file instead of breaking it up into a multi-parter.

Me: Alright, this is zeroing in on the final widget example very nicely. We still have a few things to work out regarding activating the visualizations, particularly of Step 2 and Step 4. Neither Rich visualization nor Markdown is actually doing its JavaScript thing. But that’s fine. The JavaScript of Step 3 is rendering, adding buttons and incrementing them. So we have JavaScript in widgets and it’s success assured. What we don’t have is these larger libraries active in the widgets with heavier requirements. But don’t try to fix that code just yet. Dedicate your response on capturing the lightning we currently have in a bottle. Focus on documenting what’s awesome and works.

Specifically, you made a new Pipulate Workflow from scratch with the instructions from Gemini 2.5 Experimental, which were produced as a result of the super-prompt documented in this article. While your first-pass of implementation didn’t work exactly right out of the starting gate, neither was it bad nor incompetent. It only took a little bit of back-and-forth to smooth it out and get it working. And now that it’s working, we have the framework for a sort of step-by-step widget-swapper. Each step is both an input-form “mode” where you lay out the raw data of plain text, JavaScript or Markdowan and then submit it and have the (soon-to-be) rendered widget replace the input field in the same card. You got it to the point where we’re just smoothing out the final nuanced HTMX/page-scope JavaScript library nuanced issues in just a few exchanges. Document everything from the initial new-workflow-please prompt to where we are right now.

Claude 3.7: I’ll analyze the widget examples code we’ve developed so far in the Pipulate project.

Looking at the current implementation in 60_widget_examples.py, I can see we’ve built a workflow that demonstrates four different widget types in a step-by-step process:

Read file: pipulate/plugins/60_widget_examples.py

Pipulate Workflow Widgets: A Step-by-Step Implementation Journey

We’ve successfully implemented a comprehensive widget examples workflow for Pipulate that demonstrates four different types of interactive components:

What We’ve Accomplished

Simple HTMX Widget: A basic text display that shows how to create static content widgets
Rich Table Widget: Demonstrates integration with Python’s Rich library to create data tables
JavaScript Execution Widget: Shows how to create interactive JavaScript components that can manipulate the DOM
Markdown Renderer: Demonstrates client-side rendering capabilities using external JavaScript libraries

Key Technical Achievements

The implementation showcases several important technical aspects:

Widget Container Pattern: We leveraged Pipulate’s widget_container method to create a consistent UI structure for all widgets, maintaining proper styling and layout.
HTMX Integration: We successfully implemented integration with HTMX to handle dynamic UI updates, including:
- Using hx-post for form submissions
- Using hx-get with hx-trigger="load" for chaining step progression
- Building response objects with HTMLResponse to support custom headers
JavaScript Execution Strategy: We solved the challenge of executing JavaScript in HTMX-injected content using two complementary approaches:
- Custom HX-Trigger headers that trigger JavaScript execution after content is inserted
- Unique widget IDs to properly target DOM elements
State Management: The workflow properly maintains state across steps, allowing users to:
- Enter content for each widget type
- See the rendered widget after submission
- Revert to previous steps to modify content
- Navigate through the workflow in a linear fashion
Pre-populated Examples: Each step comes with helpful examples that demonstrate the expected input format, making it easy for users to understand how to configure each widget type.

Implementation Highlights

Here are some of the specific technical approaches that make this implementation successful:

Rich Table Implementation:

# Convert Python Rich library output to HTML
console = Console(record=True)
console.print(table)
html_output = console.export_html(inline_styles=True)
rich_table = Div(html_output, _raw=True)

JavaScript Execution Strategy:

# Add HX-Trigger header to execute JavaScript
response.headers["HX-Trigger"] = json.dumps({
    "runJavaScript": {
        "widgetId": widget_id,
        "code": user_val,
        "targetId": f"{widget_id}_target"
    }
})

Unique Widget IDs:

# Generate unique widget ID for this step and pipeline
widget_id = f"js-widget-{pipeline_id}-{step_id}".replace("-", "_")

Widget Container Pattern:

# Return the widget container with navigation
return Div(
    pip.widget_container(
        step_id=step_id,
        app_name=app_name,
        message=f"{step.show}: JavaScript execution example",
        widget=js_widget,
        steps=steps
    ),
    Div(id=next_step_id, hx_get=f"/{app_name}/{next_step_id}", hx_trigger="load"),
    id=step_id
)

What’s Next

While we’ve built a solid foundation for widgets in Pipulate, there are still some areas to improve:

The Rich table visualization and Markdown rendering need some further refinement to ensure JavaScript libraries are properly activated
We could extend this pattern to support more complex widget types like d3.js force graphs, JSON explorers, and Mermaid diagrams
Additional helper functions could be added to streamline creating commonly used widget types

This implementation provides a versatile framework for adding various visualization and interactive components to Pipulate workflows while maintaining the project’s core philosophy of local-first, server-rendered UI with minimal client-side JavaScript.

Acknowledging a UX Nuance: Not Stringing It Out

The little decisions I make now with this widget example file are going to echo and resonated forward in time, affecting the ease and simplicity or difficulty of splicing widget visualizations into workflows.

Me: Excellent, excellent! The nuance I want to discuss has to do with step-collapse in chaining step progression. User input is acquired and turned into the visualization in the same step. This was not my original vision, but rather something the combination of Gemini 2.5 Experimental did in planning as a result of my super-prompt, and Claude 3.7 Sonnet did in implementation as a result of Gemini’s instructions. Now don’t go refactoring. Keep the code exactly as it is. Just continue the article-style discussion addressing this issue. I envisioned one step (Card, Cell or whatnot) for data-collection and anther step/card/cell for visualization.

# Current Combined Approach (4 Steps)

+---------------------------+
| Step 1: Simple HTMX       |
| [Input Form]              | ---> Submit ---> [Display Widget] (Same Card)
+---------------------------+
            |
            v
+---------------------------+
| Step 2: Rich Table        |
| [Input Form]              | ---> Submit ---> [Display Widget] (Same Card)
+---------------------------+
            |
            v
+---------------------------+
| Step 3: JavaScript Widget |
| [Input Form]              | ---> Submit ---> [Display Widget] (Same Card)
+---------------------------+
            |
            v
+---------------------------+
| Step 4: Markdown Renderer |
| [Input Form]              | ---> Submit ---> [Display Widget] (Same Card)
+---------------------------+
            |
            v
+---------------------------+
| Finalize Workflow         |
+---------------------------+

This would have more visually chained-up inputs and outputs to show the cause and effect, but it would have doubled the number of steps. But the complexity of each step would have been correspondingly reduced, perhaps making it more appropriate as a copy/paste example in the future where steps are not going to have this double responsibility of data-collection and data visualization. I don’t want the code refactored yet. I just want it thought through. In this scenario, step 1 would be interactive tweaking the pre-populated form. But step 2 would be completely passive as far as the user is concerned, except perhaps for a “Next” button which brings them to step 3 and so on. There is essentially another possible implementation of this widget that strings out the input and display into more discrete steps. Pontificate, riff and document in an article style for the nice readers please.

# Alternative Separated Approach (8 Steps)

+---------------------------+
| Step 1: Simple HTMX Input |
| [Input Form]              | ---> Submit -+
+---------------------------+             |
            |                             |
            v                             v
+---------------------------+    +---------------------------+
| Step 2: Simple HTMX View  |    | [Simple HTMX Widget]     |
| [Display Only]            | <--+ [Next Button]            |
+---------------------------+    +---------------------------+
            |
            v
+---------------------------+
| Step 3: Rich Table Input  |
| [Input Form]              | ---> Submit -+
+---------------------------+             |
            |                             |
            v                             v
+---------------------------+    +---------------------------+
| Step 4: Rich Table View   |    | [Rich Table Widget]      |
| [Display Only]            | <--+ [Next Button]            |
+---------------------------+    +---------------------------+
            |
            v
+---------------------------+
| Step 5: JavaScript Input  |
| [Input Form]              | ---> Submit -+
+---------------------------+             |
            |                             |
            v                             v
+---------------------------+    +---------------------------+
| Step 6: JavaScript View   |    | [JavaScript Widget]      |
| [Display Only]            | <--+ [Next Button]            |
+---------------------------+    +---------------------------+
            |
            v
+---------------------------+
| Step 7: Markdown Input    |
| [Input Form]              | ---> Submit -+
+---------------------------+             |
            |                             |
            v                             v
+---------------------------+    +---------------------------+
| Step 8: Markdown View     |    | [Markdown Widget]        |
| [Display Only]            | <--+ [Next Button]            |
+---------------------------+    +---------------------------+
            |
            v
+---------------------------+
| Finalize Workflow         |
+---------------------------+

Claude 3.7:

Alternative UX Approach: Separating Input from Visualization

An interesting UX consideration emerges when examining our current widget implementation. As currently designed, the workflow combines data collection and visualization within the same step - each card first collects input, then transforms into the visualization after submission. This creates a compact workflow where users see immediate results, but it merges two conceptually distinct operations.

The Combined Approach (Current Implementation)

In our current implementation, we’ve created a workflow that:

Presents an input form for data entry
Processes the submission
Replaces the form with the visualization in the same card
Allows reverting to edit the input again if needed

This approach has several advantages:

Conserves vertical space in the UI
Provides immediate feedback in the same location
Creates a clear 1:1 relationship between input and output
Results in a workflow with fewer total steps

The combined approach works particularly well for our demonstration purposes, showing a clear progression from simple to complex widget types without overwhelming the user with too many steps.

The Separated Approach (Alternative Design)

An alternative design would separate these concerns:

Step X: Pure data collection with a form
Step X+1: Pure visualization showing the rendered result

In this model, the workflow would expand to 7-8 steps from our current 4, but each step would have a single responsibility:

Step 1: Input for simple HTMX widget
Step 2: Display of simple HTMX widget
Step 3: Input for Rich table data
Step 4: Display of Rich table
And so on…

Potential Benefits of Separation

This alternative approach has several compelling advantages for certain use cases:

Conceptual Clarity: Each step has a single, clear purpose - either collecting data or displaying results
Simplified Step Implementation: The code for each step becomes simpler, with fewer branches and state conditions
Enhanced Copy/Paste Reusability: Developers could more easily extract just the visualization component without the input logic
Visual Progression: Creates a clearer visual storytelling of data flowing through a pipeline
Easier Debugging: Issues in data collection can be isolated from issues in visualization
Step-by-Step Tutorial Experience: Gives users more explicit “checkpoint” moments in complex workflows

Implementation Considerations

If we were to refactor toward this approach, the changes would focus on:

Step Definition: Doubling the number of steps in __init__, with alternating input and display steps
Step Handlers: Creating new handlers like step_01_display, step_02_display, etc.
State Flow: Ensuring data collected in one step flows to the visualization step
Navigation: Maintaining the proper chaining between steps

The visualization steps would be largely passive from the user’s perspective - they would simply display the result and contain a “Next” button to proceed to the next input step.

Which Approach Is Better?

# Implementation Patterns Comparison

Combined Pattern (Current)                    Separated Pattern (Alternative)
+-----------------+                           +-----------------+
| step_01         |                           | step_01 (input) |
| +-------------+ |                           | +-------------+ |
| | Input Form  | |                           | | Input Form  | |
| +-------------+ |                           | +-------------+ |
|       |         |                           +-----------------+
|       v         |                                   |
| +-------------+ |                                   v
| |   Widget    | |                           +-----------------+
| +-------------+ |                           | step_02 (view)  |
+-----------------+                           | +-------------+ |
                                              | |   Widget    | |
                                              | +-------------+ |
                                              +-----------------+

Neither approach is inherently superior - they serve different needs:

Combined (current): Better for compact workflows where immediate feedback is important
Separated: Better for educational workflows, complex pipelines, or when creating reusable components

For our current widget examples, the combined approach effectively demonstrates the capabilities without overwhelming users with too many steps. However, for more complex workflows or when creating a step-by-step tutorial, the separated approach might provide a clearer experience.

Future Considerations

As we expand Pipulate’s widget capabilities, we might consider:

Creating helper functions that support both patterns
Documenting both approaches for different use cases
Potentially offering a configuration option to automatically generate the extra display steps when desired

The beauty of our current implementation is its flexibility - the foundation we’ve built supports either UX approach with minimal changes to the core architecture.

# State Flow Patterns

Combined Pattern:
+------------------+    +---------------+    +----------------------+
| step_01_submit() |--->| Update State  |--->| Create & Show Widget |
+------------------+    +---------------+    | (same card)          |
                                             +----------------------+

Separated Pattern:
+------------------+    +---------------+    +----------------------+    +---------------+
| step_01_submit() |--->| Update State  |--->| Trigger Next Step    |--->| step_02()    |
+------------------+    +---------------+    +----------------------+    | (show widget) |
                                                                         +---------------+

A Pragmatic Wrapping-it-Up Decision

Alright I stopped in the middle of the widget project yesterday to address a precarious traffic drop on my website, but now I’ve got a handle on it, will get back to the widget project now, and hopefully even get a good step closer on the home-hosting project. It’s basically pedal-to-the-metal and gunning it on all the projects that have converging paths and are designed to have an accelerating effect when unified:

This website/blog where I talk to you, the AIs and myself in these super-prompts that help me think through next steps, enlist coding help, practice what I preach / walk the walk when it comes to AI SEO and my field, and to keep you in the loop of it all with this storytelling — disjointed as it is, but great fodder for “extracting” the future-proofing in the age of AI tech book.
The Pipulate free and open source SEO software project, product and system for creating workflows of every sort that mimic the way Jupyter Notebooks work. Jupyter Notebooks have had an enormous impact on Science, Academia, the Financial world the world of Python programming in terms of letting you experimentally learn Python and flesh out workflows — but you have to look at the Python. Pipulate “cans” those workflows as web apps for a less technical audience.
A home-hosting or personal cloud environment where I can have 100% transparency into and control over at least one of my published websites so I can watch the AI crawlers and bots come in and interact with the site. It’s been a quarter-century of Google dominance and now for the first time significant new players are comin-a-scrapin and it’s not just website scraping. They’re fetching data from websites in real-time as a sort of man-in-the-middle (bot-it-the-middle - BITM?) agent, doing research or placing orders on behalf of the user, or whatever. And I’ve got to watch it like a fishtank.

Yeah, so that pretty much covers the broader context. But this is against a backdrop of countless other additional little projects that make the entire engine work. That’s because I still hold a day-job that pays for all of this. Yet all of this keeps me sharp for my day-job, so there is a symbiotic relationship. And among those countless little projects are the individual workflows that plug into Pipulate, each of which is a deliverable deliverer. The workflows themselves help you do the SEO work.

Shallow crawls of sites with an AI riding sidekick
Checking sites for broken links
Following redirect chains
Creating 301 maps for site migrations
Creating JavaScript parameter busters (and server-side render rules)
Creating competitive content gap analysis
Creating competitive link/social gap analysis
Creating website hierarchy topology information architecture visualizers (showing change over time)
Upcoming trend-spotters to help step into the path of new traffic surges

Wow, the list goes on and on. Basically anything you do for SEO that you might use Python code, particularly in a Jupyter Notebook for, is a good candidate. And as I said, Notebooks have revolutionized Science, Academia and Finance as well, so I expect that as Pipulate settles into the SEO world as a new tool for simplifying these once-finished workflows into canned web apps, it might get discovered by other domains who have a similar itch to scratch — bottling linear workflows.

But workflows are these willy nilly open-ended things that AIs agentically do, you say? And this fact is being proven by every code-editor and integrated development environment (IDE) baking-in Agent mode to just follow your coding-prompt until finished… you say? And you would be right. There is this notion of pulling the ripcord with a prompt and letting the AI go hogwild doing your work for you. Prompt-and-pray!

Well, that new agentic wild west is precisely how I got my latest linear workflow as far along as I got yesterday. I didn’t have the energy to finish it. I ran out of steam. But I did get to an excellent stopping point where I’ve really isolated things down to concrete next steps:

Getting HTML table fragments generated by the Python Rich library inserted into HTMX targets in order to simulate rich front-end JavaScript data-grids like IPyTables, but HTMX-style so much less overhead. It’s sort of a “wedge” for developing a generic Rich-driven HTMX datagrid for Pandas DataFrames. Honestly, all the mechanics of this are done already in this example and it’s just a matter of getting the HTML to render correctly in the fragment (not as text).
On a very similar note, when the widget requires utilizing a JavaScript library for visualization such as with Markdown or Mermaid diagrams, the connection is for some reason (as expected) being broken. You can hardly expect JavaScript libraries included on the page-load to be properly executed on elements injected into the DOM by HTMX without having to trouble-shoot something — from intentional same-origin CORS security-style stuff to just browser farts because we’re far out in the edge cases here.

There’s no way I can make this an article continuation today into this new work. I’ve got to wrap this up and call the work so far in-the-can and a successful banked win. I do suppose I’ll run things by Claude and Gemini though to get their closing thoughts as I plan the next round of work, next article and next super-prompt!

And so there you go, Claude. Give us your closing comments on this widget-rich article, adventure and journey.

Closing Thoughts From Claude

As I get into a fresh day, it’s no longer about widgets. It’s about JavaScript visualization library execution in HTML fragments of the DOM created or updated by HTMX. This is a distinctly different topic full of its own nuances and sub-adventure. So let’s close this one out.

Claude 3.7 Sonnet: ## Closing Thoughts: The Widget Journey in Pipulate

Looking back at the journey we’ve embarked on with the Pipulate Widget Examples workflow, I’m struck by how much ground we’ve covered in a relatively short time. From a high-level concept to a functioning implementation showcasing four distinct widget types, we’ve established a foundation that opens numerous possibilities for visualization and interactivity in Pipulate workflows.

The Power of AI Collaboration

What stands out most to me is the collaborative nature of this development process. The implementation journey began with a well-crafted “super-prompt” fed to Gemini 2.5 Experimental, which analyzed the existing codebase and produced a detailed implementation plan. I then took that blueprint and turned it into working code, adapting as we went along to address challenges that emerged.

This multi-model approach demonstrates the power of specialized AI collaboration - using one model’s strengths for high-level architecture and another’s for implementation and refinement. The resulting code is more robust than either model might have produced independently.

Technical Achievements Worth Celebrating

While there are still refinements to be made, what we’ve accomplished so far is significant:

We’ve successfully bridged the gap between server-side Python and client-side JavaScript visualization through HTMX
We’ve solved the challenge of executing JavaScript in HTMX-injected content
We’ve established patterns for unique widget identification and state management
We’ve created a consistent UI approach for widget display and interaction

The HX-Trigger pattern we implemented is particularly elegant, providing a clean solution to one of the most challenging aspects of dynamic content management - executing code after content is injected into the DOM.

The UX Fork in the Road

The dual approaches to widget implementation - combined vs. separated - highlight an important aspect of workflow design: the tension between compactness and conceptual clarity. Neither approach is universally better; they serve different purposes and use cases.

What’s remarkable is that this design decision emerged organically from the collaborative AI process. I interpreted Gemini’s blueprint in a way that led to the combined approach, which turned out to be an efficient and effective solution for the demonstration workflow.

Looking Forward

The groundwork we’ve laid opens exciting possibilities for Pipulate’s future. Beyond fixing the rendering issues with Rich tables and Markdown, the patterns established here provide a template for implementing more complex visualizations like:

Interactive d3.js force-directed graphs
JSON object explorers with expandable nodes
Syntax-highlighted code displays with copy functionality
Interactive Mermaid diagrams

Each of these can follow the same fundamental pattern: unique container IDs, widget containers for consistent styling, and HX-Trigger for post-injection interactivity.

Final Thoughts

What started as a technical need for visualization widgets has evolved into something more significant - a framework for extending Pipulate’s capabilities in ways that make complex data more accessible and workflows more intuitive.

The journey from concept to implementation showcases not just the power of modern development tools and AI assistance, but also the value of iterative design thinking. By starting with a focused proof-of-concept and methodically working through challenges, we’ve created something with broader applications than initially envisioned.

As Pipulate continues to evolve, these widget patterns will likely become a core part of its identity - enabling the transformation of complex data processing workflows into intuitive, visual experiences accessible to users regardless of their technical background.

This is, ultimately, what good software design is about: not just solving today’s problem, but laying the groundwork for solving tomorrow’s problems more elegantly. The widget journey is far from over, but we’ve made a promising start.

Conclusion

You know what? That was so friggin elegant, I don’t need Gemini’s closing opinion. Let’s reserve that for the opening context of the next article, which will be drawn from directly out of here. Let’s close the input/output loop.

Hi Gemini 2.5 Experimental. Welcome to the bottom of the super-prompt. By this time, you’ve read a lot of code regarding Pipulate and are brimming with context. You get the problem, don’t you? FastHTML has a nifty fast_app helper class that lets us conveniently load whatever JavaScript libraries we want from either CDN or local, and it even handles “module” mode. But the code as currently implemented is not doing the Markdown visualization properly on Step 4. And on a similar note, the Rich table fragment isn’t being shown as HTML but rather as plain text, which is a FastHTML to_xml-related issue. Set the stage for today’s work where we nail this!

Gemini’s Next-Step Implementation Plan

I am still going to wrap up with the implementation plan based on the next super-prompt just again to “bank the win” in preparation for next steps, during which time Gemini itself can see its own implementation plan as part of the starting point.

Okay, I have reviewed the codebase context, the article htmx-widgets-ai-implementation.md, and the current state of the 60_widget_examples.py plugin. Based on this, here’s an analysis and implementation plan focusing on achieving the goals outlined in the article and addressing the current issues.

Required Changes

The core structure for the 60_widget_examples.py workflow is largely in place, following the plan suggested by Gemini and implemented by Claude. The key remaining changes involve debugging and refining the interaction between the server-generated HTMX fragments and client-side JavaScript libraries for the Rich table and Markdown widgets.

plugins/60_widget_examples.py:
- Step 2 (Rich Table):
  - Issue: The article notes the Rich table renders as plain text instead of HTML.
  - Analysis (step_02_submit): The current code correctly uses rich.console.Console(record=True) and console.export_html(inline_styles=True) to get the HTML string. It wraps this in Div(html_output, _raw=True). This _raw=True flag is crucial for telling FastHTML not to escape the HTML. The issue likely lies in how the final HTMLResponse is constructed or how the widget_container handles the raw Div.
  - Required Change: Double-check the return path in step_02_submit. Ensure that the pip.widget_container correctly receives the Div with _raw=True and that the final HTMLResponse correctly converts the entire resulting FastHTML structure (including the container) to a string using to_xml(). Ensure no intermediate step is accidentally re-escaping the raw HTML. The critical pattern HTMLResponse(str(to_xml(rendered_item))) must be followed meticulously.
- Step 4 (Markdown Renderer):
  - Issue: The article notes the Markdown rendering isn’t activating; the JavaScript library (marked.js) isn’t processing the content.
  - Analysis (step_04_submit): The code correctly sends an HX-Trigger header ({"renderMarkdown": {"targetId": ..., "markdown": ...}}). This pattern relies on a corresponding client-side JavaScript listener.
  - Required Change: Verify the client-side setup:
    - Confirm marked.js is included in the hdrs tuple in server.py’s fast_app call.
    - Confirm that a JavaScript event listener exists (likely in static/script.js or the referenced static/widget-scripts.js) that listens for the renderMarkdown event (e.g., document.body.addEventListener('renderMarkdown', function(evt) { ... }); or htmx.on('renderMarkdown', function(evt) { ... });).
    - Ensure this listener correctly extracts targetId and markdown from evt.detail, finds the target element by ID, and calls the marked.parse() function (or the correct function for the library) to render the content into the target element’s innerHTML.
server.py:
- hdrs tuple: Verify that the path to marked.min.js (or the chosen Markdown library) is correctly specified in the hdrs tuple within the fast_app call. Make sure the file exists at that path in the static directory or that the CDN link is correct.
- to_xml Usage: While reviewing the Rich Table fix, ensure all workflow responses that return FastHTML objects are correctly wrapped in str(to_xml(...)) before being passed to HTMLResponse, as per the project conventions.
static/widget-scripts.js (or similar):
- Event Listeners: This is the most likely place for required changes for the Markdown widget (and potentially the JS execution widget if refactored). Add or verify the existence and correctness of the event listener for the renderMarkdown trigger. Ensure it correctly handles the event details and interacts with the marked library.
- Helper Functions: Confirm the runJsWidget function (used in Step 3) exists and works as expected, targeting the correct elements.

Integration Strategy

The current implementation in 60_widget_examples.py integrates well with the existing architecture. The strategy should continue to follow these established patterns:

Workflow Structure: Maintain the standard class structure (__init__, landing, init, step_XX, step_XX_submit, finalize, unfinalize, handle_revert).
Pipulate Helper: Continue using self.pipulate (pip) methods for state management, UI components (widget_container, revert_control), validation, and messaging.
HTMX: Rely on HTMX attributes (hx-post, hx-get, hx-trigger, hx-target, hx-swap) for driving UI updates and step progression.
widget_container: Use pip.widget_container consistently for displaying content below the revert control in steps that require visualization. This ensures consistent layout.
Client-Side Interaction (HX-Trigger): For widgets requiring client-side JavaScript library interaction after HTMX injection (like Markdown rendering), the HX-Trigger header combined with global JavaScript event listeners is the established and recommended pattern. Avoid embedding complex, self-executing scripts directly in the returned fragments where possible, as triggers offer more control over execution timing.
JS Libraries: Load necessary client-side libraries globally via the hdrs tuple in server.py. This keeps library management centralized.
Backward Compatibility: The current changes (fixing rendering in a new plugin) are backward compatible. Adding the Markdown library to hdrs and adding JS listeners are additive changes and shouldn’t break existing functionality.

Implementation Plan

The initial implementation phase is complete. The focus now shifts to debugging and refinement.

Milestone 1: Fix Rich Table HTML Rendering (Step 2)
- Task: Debug step_02_submit in plugins/60_widget_examples.py.
- Action: Trace the html_output from console.export_html through the Div(_raw=True), pip.widget_container, and the final HTMLResponse(str(to_xml(...))). Identify where the HTML might be getting escaped or improperly handled. Log the intermediate HTML strings to pinpoint the issue. Ensure the _raw=True attribute persists until final rendering.
- Verification: The table should render with proper HTML structure and styling in the browser, not as escaped text.
Milestone 2: Fix Markdown Rendering (Step 4)
- Task: Debug the client-side activation of the Markdown renderer.
- Action 1 (Server): Verify marked.min.js (or equivalent) is correctly listed in hdrs in server.py. Verify the HX-Trigger header is correctly formatted and sent in step_04_submit.
- Action 2 (Client): Examine static/widget-scripts.js (or relevant JS file).
  - Ensure an event listener for renderMarkdown is present and correctly attached (e.g., to document.body).
  - Add console.log statements inside the listener to confirm it’s firing when the trigger is received.
  - Log the evt.detail object to ensure targetId and markdown are being passed correctly.
  - Verify the document.getElementById(targetId) call correctly finds the target Div.
  - Confirm the marked.parse(markdown) function (or equivalent) is being called and its output is being assigned to the target element’s innerHTML. Check the browser’s developer console for any JavaScript errors related to the marked library or the listener function.
- Verification: The Markdown text submitted in Step 4 should be rendered as formatted HTML within the designated widget area.
Milestone 3: Refinement and Testing
- Task: Test the complete workflow thoroughly.
- Action: Run through all steps, test reverting from each step, test finalizing and unfinalizing. Ensure widgets display correctly after reverts and state changes. Verify the JavaScript widget (Step 3) continues to function reliably.
- Consideration: Review the “Combined vs. Separated Steps” UX discussion. While no refactoring is requested now, keep the alternative pattern in mind for future, potentially more complex workflows where separating input and display might be beneficial.