From Rabbit Holes to Roadmaps: Using LLMs for SEO Software Dev

During an ambitious coding session for my Pipulate project, I found myself stuck between multiple technical approaches and potential rabbit holes. Rather than getting lost in endless research, I consulted multiple AI assistants (Gemini, Grok, ChatGPT, and Claude) through a custom context prompt to help prioritize my next steps. This collaborative process helped me identify implementing vector embeddings as the crucial next feature to give my AI assistant persistent memory, providing clarity on the immediate path forward despite limited coding time.

By Mike Levin

Sunday, April 6, 2025

TL;DR: Using AI to Find Focus in Software Development

If you’re here for practical insights on using LLMs to guide software development decisions, especially for SEO tools, you’re in the right place. This article chronicles my journey through a day of coding on my Pipulate project, where I found myself at a crossroads between multiple technical approaches.

Rather than getting lost in endless research and “rabbit holes,” I leveraged multiple AI assistants (Gemini, Grok, ChatGPT, and Claude) to analyze my codebase and help prioritize the next development steps. The result? Clarity on implementing vector embeddings as the foundation for my AI assistant’s memory system - a practical, high-impact next step that avoids unnecessary complexity while moving the project forward.

The article details both my thought process and the specific AI consultations, offering a real-world example of how to use LLMs as development partners rather than just code generators. Whether you’re building SEO tools or any software project facing architectural decisions, you’ll find practical strategies for maintaining focus and making progress when faced with too many possible paths forward.

Skip past the cosmic musings below if you’re eager to get straight to the technical journey.

Cosmic Reflections on Daily Patterns

Day by day, the patterns of life may feel circular but the movement of parts so far as we can tell is really nested rotations resulting in a complex spiral. The Earth rotates around the Sun which is in the arm of a Galaxy of an expanding Universe. And we are each a little chemical reaction nodes in the cascading network of life. We wake up. We do stuff. We go to sleep. Lather, rinse, repeat.

Finding Flow State Between Indoor and Outdoor Life

It’s easy to get lost, and so we reflect and navigate. From such cosmic ponderings I proceed to the thought of how many people are Red Bull extreme sports nuts, outdoor enthusiasts and folks getting back in touch with the roots of who we are as nomadic hunter gatherer primates. And then I think to myself just how fine I am tapping away at the keyboard in vim.

Indoor, outdoor, it doesn’t matter. Tapping away in vim, I am transported into that flow-state. Drop me off in the middle of the Gobi Desert with air conditioning and the needs of life, or in the middle of New York City, and I’ll be equally happy. I’ve learned to focus and filter out the extraneous stimuli that undermines getting into the zone. Once you do that, you’re golden.

Starting Small: From API Authentication to Broader Vision

Anyhow, that’s me warming up mentally for what I plan to be an epic day of coding. But the way we get to epic is to cast aside all that silly expectations of epic and make it the smallest, most humble step 1 possible. The journey of 1000 miles starts with the first step, and a few days ago I successfully tackled API authentication within Pipulate, against Botify in particular, but the technique could work on any API. It’s an excellent example of how Pipulate can be bent and shaped into any generic SEO white-label software product for the SEO practitioner to use in front of the client.

Navigating Between Specific Tools and Universal Solutions

But of course I’m working with Botify software right now, so these projects are Botify-centric. Though it’s not going to stop me from doing a high-level top-down approach to these SEO workflows. I’m tempted to jump directly into how to do a CSV download with the Botify API which involves one URL to make the export request, a continuous checking for availability against that URL until it’s ready, and then finally doing the download, unzipping the file and whatever Pandas dataframe manipulation and visualization follow-up is necessary to turn it into the final deliverable.

Focusing on Core Architecture While Avoiding Rabbit Holes

But I’m more interested in laying out a broad brush-stroke of the outer system first. Starting with pure site-size as perceived by Google and Bing, then proceeding to some much more mundane CSV download that doesn’t have so many moving parts. And then we layer in the Botify API. Ideas race through my mind. The amount of potential rabbit hole projects I am on the verge of here looks like Bugs Bunny’s trail on the way to Albuquerque. I have to keep it simple. And relatable to the SEO audience out there that may be following along. This could be the chisel-strike that makes it immediately useful, and still compels me forward to the bits necessary for my day-job. Even these broad-stroke pieces for the SEO industry would be of interest to the day-job people I have to convince.

Okay, don’t let it become a rabbit hole! But what is the simplest way you can do this that delivers the biggest bang for the buck?

Architectural Decision Point: Between Astounding and Mundane

Okay, we are at a critical juncture here in the project. Architecturally, I could do something astounding or mundane. Either approach would probably be equally competent and achieve the immediate goals, scraping a web page and such. But the question is can we do it the astounding way and not allow it to become a time-sink rabbit hole project, and for the most part wrap it up today and be much further along? No wheel spinning!

Considering a Local MCP Server Approach

The temptation here is to make a single, local monolithic MCP server – something that has almost nothing to do with AI, but just takes the MCP-formatted JSON tool-calls an AI can produce and handles them in whatever way. The details of which can start out incredibly simple, but that provides the wedge to grow from. It’s not monolithic at first. It’s very much like a simple web microservice. The local LLM becomes the MCP client, being taught how to issue such JSON tool-call commands.

Evolution of Implementation

I already had something very similar to this implemented in my earlier code but it was from before MCP and even before the standardized then schismatized tool-call syntaxes of the different models like Google and Llama away from the OpenAI convention. So I gutted it when I reduced the surface-area of Pipulate when I externalized plugins, and now having been around the block a few times with a JSON monitor on the Websocket stream, I think I may be able to handle this well. I build the same API for either the LLM to use or for me to use. An mcp server’s an mcp server no matter who calls it.

The Challenge of JSON-Based API Design

Okay, so here’s the problem that I see leading my current work project with an MCP server. Even though MCP servers don’t have to do anything with AI necessarily, they are a structured JSON request, which means you have to invent an API syntax for whatever your tool you’re trying to call. See? JSON is a key/value-pair protocol, so it’s just parameters and arguments. Or attributes and values, if you prefer. It’s not program code. What those API-calls mean has to map to something pre-arranged in the tool being called. And so you’re in this whole world of inventing stuff. Rabbit hole!

Simplifying the Approach

We don’t need no stinkin inventing. We’re just trying to execute some Python code on behalf of the LLM that might need it, or a scripted process in a Workflow that might need it. See? The service consumer might either be an actual LLM perhaps working in agentic mode, or it might just be a rote if-this/than-that linear style script that just needs to grab the weather, a stock quote, the title from a webpage or whatever. But now we have to embed that this API-call means stock quote, that API-call means weather and this other one means webpage title-tag. You’re funneling a world of possibilities through the JSON format as a choke-point.

Evaluating Function Parameters vs MCP Server Approaches

But it’s still going to go through function parameters and arguments Python-side without an MCP server and JSON, isn’t it? Unless you go full Python sandbox and make your function-calls literal Python functions that the LLM writes, but that’s way too fragile. Well documented function signatures abiding by an established protocol that all the LLM models will be increasingly learning give reliability. You’re putting your own made-up signatures inside well-established MCP syntax and documenting your stuff with something similar to ye old XML schema validators.

Managing Workflow Complexity and WET Code

Maybe I can use this as an opportunity to reduce the potential exploding complexity of the WET HTMX Workflows whose individual STEPS are billed as a way to do anything you can do in Jupyter Notebook CELLS. So a lot of big, ugly Python code promises to be shoved into these WET Workflows, making them longer, WETter, harder to read and harder for AI code assistants to help you with. I still stand by the WETness because that makes them infinitely customizable without the inhibition of all the weird superclass overriding. You can’t avoid complexity. You just shoving it around to where you like the APIs best.

Formulating a Strategy for Pipulate Workflows

Okay, so a plan is forming. A Pipulate Workflow can do literally anything that Python does, and without much inhibition due to being part of a structured framework due to how Workflows don’t inherit from a base class. So far, we only have the one pattern of a plain old text field in the Hello World example, hello_workflow.py, and this is good. Soon there will be dropdown menus, checkboxes, textarea’s and all the web form classics. However, maybe I should come right out of the starting gate with a complexity-externalizing local MCP server strategy.

Exploring Open WebUI Integration

I just installed Open WebUI to have a different way of connecting to Ollama LLMs locally. Once you have Ollama running locally, it almost doesn’t make sense to not have something like this running on top of it for a convenient web user interface! It currently has almost 88K stars on GitHub Here are the release notes. It has RAG, Python execution and MCP all built-in already! Though it’s something called mcpo which apparently goes through OpenAI as a proxy. They make a pretty strong case for Why Use mcpo Instead of Native MCP? and Why is the MCP-to-OpenAPI Proxy Superior?

The Promise of Pyodide and Web Assembly

At this point, I also feel compelled to mention the way it supports allowing LLM to execute Python, which is the miracle that is Pyodide. Watch Python execute natively in the web browser with WASM. I’m not sure whether Pyodide plays into my Pipulate plans, but I definitely need to brush up on learning about it. This is a very relevant application of Atwood’s Law:

Any application that can be written in JavaScript, will eventually be written in JavaScript

…which was originally written tongue-in-cheek but seems to have ultimately won, given the prevalence of Web Assembly and projects like Provide.

Focusing on Core Priorities

I am determined to not dive into that rabbit hole. But I do need to make my to-do list for Chip O’Theseus come alive, without that itself becoming a rabbit hole project itself, and regardless of how much or how little coding and moving forward of the project I actually get done.

Establishing Clear Direction and Purpose

The key factor in moving forward is knowing what to do next and why. We want to eliminate the probing and he hemming and hawing and the fumbling. We want one clear path of truth, which is an embedded todo list, which both the embedded local Ollama LLM with any model selected can look at, now either under the Pipulate project or under Open WebUI for discussion, or any of the frontier models I choose to point at it, either under the Cursor AI IDE context, or copy/pasting the context_foo.py foo.txt file.

Code Consolidation Progress

Okay, wow. So… let’s do a tiny bit of file consolidation incorporating sizeit.py into context_foo.py to eliminate one extra file in the pipulate repo. Okay, done. Worked with Claude to work tiktoken into context foo, making it much more useful and eliminating sizeit.

Defining Chip O’Theseus Implementation Goals

Right, okay. So there is very much a global picture of Chip O’Theseus coming into focus. It doesn’t have ANY persistent local memory yet, except maybe insofar as today’s install of Open WebUI may have given it some when running under that particular context – but not as Pipulate.

I don’t want to reinvent the wheel.
I don’t want to spin my wheels.
But I DO want Pipulate to be fully transparent.
And I DO want the Chip O’Theseus process to kick-in soon on the Pipulate side.

Strategic Todo List Placement

Right. So, the todo-list concept is critical. Sunday is rapidly running out (coming on 10:00 PM) and I haven’t meaningfully pushed the state of the code forward, either on the webpage access front nor on the saving CSV front. And that is exactly the point of the todo-list.

Figure out where in this system the todo list should go. There are a few possibilities:

configuration.nix of overarching system in ~/repos/nixos
flake.nix of Pipulate project in ~/repos/pipulate
README.md of Pipulate project
As-yet not existing CHANGELOG.md in Pipulate project

I could add a Roadmap section to the Readme and push things from there into the changelog.

Next Steps for Browser Integration

I can’t push myself on actually performing the work tonight, but the ability to take Pipulate in almost it’s exact existing form today and making a step_xx_submit function in a Workflow capable of popping open a web browser either standalone but with the user’s profile for easy access to their saved usernames and passwords with Python webbrowser standard library, or a fancier version under Microsoft Playwright automation is an urgent next step, along with saving CSVs. And here’s the research I did on the web browser front:

Q1: Can Microsoft Playwright return both the source HTML and the rendered DOM?

Yes, Microsoft Playwright can retrieve both the source HTML and the rendered DOM. However, it uses different methods because these represent different stages of a page load:

Source HTML (Initial Server Response)

What it is: The raw HTML content sent by the server before the browser parses it and before any client-side JavaScript modifies the page structure.
How to get it: Access the Response object returned by page.goto(url) and call its .text() method.

Example (Python):

  from playwright.sync_api import sync_playwright

  with sync_playwright() as p:
      browser = p.chromium.launch()
      page = browser.new_page()
      # page.goto returns the main frame's response
      response = page.goto("https://example.com") 
      if response: # Check if navigation was successful
          source_html = response.text()
          print("--- Source HTML (First 500 Chars) ---")
          print(source_html[:500])
      else:
          print("Failed to get a response.")
      browser.close()

Rendered DOM (Current Page State)

What it is: The Document Object Model after the browser has parsed the initial HTML and executed client-side JavaScript that may have added, removed, or modified elements. This reflects what the user typically sees and interacts with.
How to get it: Use the page.content() method. This serializes the current DOM state back into an HTML string.

Example (Python):

  from playwright.sync_api import sync_playwright
  # import time # Generally avoid time.sleep, use waits instead

  with sync_playwright() as p:
      browser = p.chromium.launch()
      page = browser.new_page()
      page.goto("https://example.com") # Use a site potentially modified by JS

      # BEST PRACTICE: Instead of sleep, wait for a specific condition/element
      # page.wait_for_load_state('networkidle') # Waits for network activity to cease
      # page.wait_for_selector('div#dynamic-content') # Waits for a specific element

      rendered_html = page.content()
      print("\n--- Rendered DOM HTML (First 500 Chars) ---")
      print(rendered_html[:500])
      browser.close()

Alternative using page.evaluate(): Execute JavaScript to get the HTML of the root element.

  # Assuming 'page' is an active Playwright page object
  rendered_html_via_evaluate = page.evaluate("() => document.documentElement.outerHTML")
  # print(rendered_html_via_evaluate[:500])

In Summary:

Use response.text() (from page.goto()) for the initial source HTML.
Use page.content() for the rendered DOM (after potential JavaScript execution).

Q2: Can Python’s standard `webbrowser` package open a URL in a new tab or a new window?

Yes, Python’s standard webbrowser module can request that a URL be opened in either a new browser tab or a new browser window, controlled by the new parameter in webbrowser.open().

webbrowser.open(url, new=0): (Default) Tries to open the URL in the same browser window if one is already open. The exact behavior might be a new tab in that window, depending on the browser.
webbrowser.open(url, new=1): Requests that the URL be opened in a new browser window.
webbrowser.open(url, new=2): Requests that the URL be opened in a new browser tab.

Example:

import webbrowser
import time

url_to_open = "https://www.google.com"

# Default behavior (try same window/tab)
print("Opening with default behavior (new=0)...")
webbrowser.open(url_to_open) # new=0 is the default
time.sleep(2) # Pause slightly to observe

# Request a new window
print("Requesting a new window (new=1)...")
webbrowser.open(url_to_open, new=1)
time.sleep(2) # Pause slightly to observe

# Request a new tab
print("Requesting a new tab (new=2)...")
webbrowser.open(url_to_open, new=2)

Important Note: While new=1 requests a window and new=2 requests a tab, the web browser ultimately controls the final behavior based on its own settings and state. However, these parameters are the standard way to signal your preference using this module.

Q3: Can Microsoft Playwright reuse the same browser session across multiple windows/tabs?

Yes, Playwright is explicitly designed for this. It manages sessions using the BrowserContext.

How it works:

Launch Browser: Start a browser instance (e.g., Chromium). This is the main process.
```
browser = playwright.chromium.launch()
```
Create BrowserContext: Create an isolated “session” or profile from the browser instance. This context manages its own cookies, local storage, session storage, etc.
```
context = browser.new_context()
```

Open Pages (Tabs/Windows): Create Page objects within the BrowserContext. All pages within the same context share the same session state.

page1 = context.new_page()
page1.goto("https://example.com/login")
# --- Perform login actions on page1 ---
# Cookies/storage related to login are stored in 'context'
print("Performed login actions on page1.")

Close a Page: Closing a page does not destroy the context or its session state.
```
page1.close()
print("Closed page1.")
```

Open Another Page in the Same Context: A new page opened in the same context inherits the existing session state (cookies, storage).

page2 = context.new_page()
# Navigate to a page that requires login
page2.goto("https://example.com/dashboard") 
print("Opened page2 in the same context.")
# You should still be logged in because page2 uses the session from 'context'
# --- Verify logged-in state on page2 ---
page2.close()

Cleanup: Close the context and browser when finished with the session.
```
context.close()
browser.close()
```

In Summary:

By creating and managing Page objects (windows/tabs) within a single BrowserContext, you effectively reuse the same browser session (cookies, storage, etc.) across those pages. The session state persists within the BrowserContext until the context itself is closed.

Me: On that last question, can the same session survive between completely separate Python script executions?

Gemini: No, not automatically or by default. A standard Playwright BrowserContext (which holds the session state like cookies, local storage, etc.) exists only in memory during the execution of a single Python script. When the script finishes and the browser and context objects are closed or garbage collected, that session state is lost.

However, Playwright provides two primary mechanisms to achieve persistence between separate script executions by saving the state to disk and reloading it later:

Using storage_state (Recommended for Session Data)

You can explicitly save the state (cookies, local storage, session storage) of a BrowserContext to a file (usually JSON).
In a subsequent script run, you can create a new BrowserContext initialized from that saved state file.

Saving the state in Script 1:

  from playwright.sync_api import sync_playwright

  STORAGE_STATE_PATH = 'auth_state.json'

  with sync_playwright() as p:
      browser = p.chromium.launch()
      context = browser.new_context()
      page = context.new_page()
            
      # --- Perform login or actions that set session state ---
      page.goto("https://example.com/login")
      # ... fill login form, click submit ...
      page.wait_for_url("https://example.com/dashboard") # Wait for login success
      print("Login successful, saving state...")
      # -------------------------------------------------------

      # Save cookies, local storage, session storage to a file
      context.storage_state(path=STORAGE_STATE_PATH)
      print(f"Storage state saved to {STORAGE_STATE_PATH}")
            
      browser.close() 

Loading the state in Script 2:

  from playwright.sync_api import sync_playwright
  import os

  STORAGE_STATE_PATH = 'auth_state.json'

  with sync_playwright() as p:
      browser = p.chromium.launch()
            
      # Check if the state file exists before trying to load it
      if os.path.exists(STORAGE_STATE_PATH):
          print(f"Loading state from {STORAGE_STATE_PATH}...")
          # Create a new context using the saved state
          context = browser.new_context(storage_state=STORAGE_STATE_PATH) 
      else:
          print("No saved state found, creating a fresh context.")
          context = browser.new_context() # Start fresh if no state saved

      page = context.new_page()
            
      # Go to a page that requires the previous session
      page.goto("https://example.com/dashboard") 
            
      # --- Verify if you are still logged in ---
      # e.g., check for expected elements on the dashboard
      try:
          welcome_message = page.locator('text=Welcome back').text_content(timeout=5000)
          print(f"Successfully loaded session: {welcome_message}")
      except Exception:
          print("Could not verify logged-in state. Session might be invalid or expired.")
      # -------------------------------------------

      browser.close()

Using launch_persistent_context (Full Browser Profile Persistence)

This method launches a browser instance that uses a specific user data directory on your disk, similar to how a regular browser profile works.
All state (cookies, history, local storage, cache, extensions, etc.) is automatically saved to that directory by the browser itself.
If you run another script using launch_persistent_context with the same user data directory, it will pick up the entire previous state.
Note: This creates only a context, not a separate browser object to manage. Closing the context closes the browser.

Using it in Script 1 and Script 2 (the code is the same):

  from playwright.sync_api import sync_playwright
  import os

  USER_DATA_DIR = './my_persistent_browser_profile' 

  # Ensure the directory exists if needed, though Playwright often creates it
  # os.makedirs(USER_DATA_DIR, exist_ok=True) 

  with sync_playwright() as p:
      # Launch a persistent context using the specified directory
      context = p.chromium.launch_persistent_context(
          user_data_dir=USER_DATA_DIR,
          headless=False, # Often useful to see what's happening
          # You can add other launch options here if needed
      )
      print(f"Launched persistent context using directory: {USER_DATA_DIR}")
            
      page = context.new_page()

      # --- Perform actions ---
      # If this is the first run, you might log in.
      # If it's a subsequent run, you might navigate directly to a logged-in area.
      page.goto("https://example.com/dashboard") 
      print("Navigated to dashboard.")
      # --- Verify state ---
      # (Add verification code here)
      # --------------------

      print("Closing persistent context (saves state to disk)...")
      context.close() # This also closes the launched browser instance

Comparison:

storage_state: More lightweight, focused only on session-related data (cookies, local/session storage). You explicitly save and load. It’s generally preferred if you only need session persistence.
launch_persistent_context: Persists the entire browser profile state (like a regular Chrome profile). Simpler to use if you want everything saved automatically. Can be heavier as it saves more data (history, cache, etc.). Ties your script to a specific location on disk.

So, while a session isn’t automatically carried over between script runs, Playwright provides robust mechanisms (storage_state or launch_persistent_context) to achieve this persistence by saving and reloading the necessary data from disk.

The Importance of Browser Automation Research

I know sticking all that browser research stuff there is going to come across as rambling to the AI assistant that’s going to help me process this article, but the importance of that step, especially when having the Pipulate project in context is… well, as big as the MCP part, and central to creating this todo-list that should have almost the ability to reach out, grab me by the scruff of the neck and say DO THIS NEXT!!!

Distilling Down a Todo List

I could really go in so many directions with Pipulate next, it’s an endless field of rabbit hole wonderlands to explore. But I made an app for that recently, Context Foo so I decide to craft a new prompt to help me tap 3 of the giant frontier models: Gemini, Grok & ChatGPT with the next-step identification challenge.

Dialogue with AI Assistant

Me: I need a 3rd prompt in @context_foo.py to help me define my todo list and recognize the next most important thing up to do. Help me crate the next prompt, per this article: [Dumps this entire article into Claude in Cursor]

Claude: I’ll help you create a third template focused on todo list management and next actions. Based on your article, this template should help:

Identify and prioritize next actions
Avoid rabbit holes while maintaining ambitious goals
Keep focus on the critical path
Balance immediate needs with architectural vision

# pipulate/context_foo.py

# === Prompt Templates ===
# Define multiple prompt templates and select them by index

prompt_templates = [
    # Template 2: Next Action Analysis  <-- Other templates omitted for brevity
    {
        "name": "Next Action Analysis",
        "pre_prompt": """
# Context for Determining Next Actions in Pipulate

You are Chip O'Theseus, a local AI assistant helping to evolve the Pipulate project. Your role is to analyze
the current state of the codebase and project goals to identify and prioritize the next most important actions.

Key principles to consider:
1. Avoid rabbit holes while maintaining ambitious goals
2. Keep focus on the critical path
3. Balance immediate needs with architectural vision
4. Maintain the local-first, simplicity-focused philosophy

Current high-priority areas:
- Browser automation (both standard library and Playwright)
- CSV data handling and visualization
- MCP integration for local LLM agency
- Todo list management and task prioritization
- Local memory and persistence systems:
  * Vector embeddings for semantic search
  * Vector database for efficient retrieval
  * Key/value store for persistent memory
  * RAG implementation for enhanced context

The goal is to identify clear, actionable next steps that:
- Deliver immediate value
- Build toward larger architectural goals
- Avoid unnecessary complexity
- Maintain project momentum
- Enable progressive memory enhancement

Memory Considerations:
1. Short-term: Task context and immediate goals
2. Medium-term: Project evolution and decision history
3. Long-term: Architectural decisions and core principles
4. Semantic: Related concepts and contextual understanding
""",
        "post_prompt": """
Now that you've reviewed the codebase context, help me identify and prioritize the next actions for Pipulate.

# Next Action Analysis

1. **Current State Assessment**
   - What's the most recently completed work?
   - What immediate opportunities exist for building on that work?
   - What critical paths are currently blocked?
   - What memory systems are already in place?

2. **Priority Analysis**
   - What's the smallest, most impactful next step?
   - Which tasks would unblock other important work?
   - What potential rabbit holes should we avoid?
   - Which memory enhancements would provide immediate value?

3. **Implementation Path**
   - What's the simplest way to implement the next step?
   - How can we maintain quality while avoiding over-engineering?
   - What existing code can we leverage?
   - How can we integrate memory systems incrementally?

4. **Memory Architecture**
   - Which embedding model best suits our needs?
   - What vector database would be most appropriate?
   - How should we structure the key/value store?
   - What RAG implementation aligns with our goals?

5. **Recommendations**
   Please provide:
   - A clear "do this next" recommendation
   - Rationale for why this is the best next step
   - Specific implementation suggestions
   - Estimated impact vs. effort
   - Potential pitfalls to watch for
   - Memory system implications

Remember:
- The journey of 1000 miles starts with the first step
- Keep it simple and observable
- Avoid wheel-spinning and rabbit holes
- Build toward the vision of Chip O'Theseus
- Memory is key to continuous improvement

Help me maintain forward momentum by identifying the clearest, most valuable next action,
while keeping in mind how it contributes to building Chip's memory and capabilities.
"""
    }
]

You can now use this template with:

python context_foo.py -t 2

This will help you maintain focus and momentum by having Chip analyze the codebase and provide clear, actionable next steps while avoiding rabbit holes. The template is designed to work with both your local Ollama LLM and frontier models, helping maintain consistency in how tasks are prioritized and approached.

Would you like me to help you test this template with some actual codebase analysis, or would you like to make any adjustments to the template?

Gemini 2.5 Experimential’s Pipulate Todo List

I almost always start with Gemini now in its various forms. Even though it has the highest prompt rejection because it’s an over-woke libtard, yet I think somehow it still is the smartest and fastest, which in itself is funny AF.

Gemini 2.5 Experimental: Okay, Chip O’Theseus, let’s analyze the current state and chart the course for Pipulate’s evolution.

Next Action Analysis

Current State Assessment
- Most Recently Completed Work: The codebase reflects a functional foundation with core components like FastHTML, Nix integration, Ollama chat, and SQLite persistence via MiniDataAPI/DictLikeDB. Crucially, two distinct patterns for plugins have emerged:
  - CRUD: Demonstrated by ProfileApp and the tasks.py plugin, inheriting from BaseCrud. tasks.py is particularly relevant as it shows a model for LLM interaction (though the interaction logic itself seems basic currently).
  - Workflow: Demonstrated by hello_workflow.py, providing a WET (Write Everything Twice), step-by-step pattern suitable for porting Jupyter Notebooks.
- Immediate Opportunities:
  - Enhance LLM Agency: Leverage the tasks.py structure to allow the LLM to more actively manage tasks (create, update, delete) based on chat commands, going beyond simple conversational responses.
  - Implement Semantic Memory: Begin storing vector embeddings for existing data (like tasks or profiles) to enable future semantic search and RAG. This is a clear gap and a high-priority item.
  - Build More Plugins: Create a new workflow or CRUD app to test the patterns and add functionality (e.g., a simple CSV importer workflow).
- Critical Paths Blocked:
  - Advanced LLM capabilities (RAG, complex tool use, semantic understanding) are blocked by the lack of semantic memory systems.
  - Browser automation and CSV visualization are not yet implemented.
  - MCP integration requires foundational work.
- Memory Systems In Place:
  - Explicit KV Store: DictLikeDB provides basic server-side key-value persistence backed by the store table in SQLite. It’s used for storing things like last_app_choice and last_profile_id.
  - Structured Data: The profile and task tables store structured information.
  - Conversation History: A simple deque (global_conversation_history) holds recent chat messages in memory (up to MAX_CONVERSATION_LENGTH), but this is ephemeral and lacks semantic understanding or long-term persistence beyond the current session.
  - Missing: Vector embeddings, vector database/retrieval, dedicated long-term memory structures, RAG implementation.
Priority Analysis
- Smallest, Most Impactful Next Step: Implementing vector embeddings for the existing tasks data appears to be the most strategic next step.
  - Why: It directly addresses the high-priority “Local memory and persistence systems” area. It’s a foundational requirement for RAG and enhancing the LLM’s semantic understanding. It can be implemented incrementally without needing a full vector DB immediately. It leverages existing data (tasks.text).
- Unblocking Tasks: Adding embeddings unblocks the path towards RAG, semantic search within tasks, and potentially more context-aware LLM interactions.
- Potential Rabbit Holes:
  - Trying to build a perfect, generic RAG system before having specific use cases.
  - Over-optimizing the embedding model choice initially.
  - Implementing complex browser automation before core AI capabilities are enhanced.
  - Refactoring the base classes (BaseCrud, workflow pattern) extensively before adding more features.
- Immediate Memory Value: Storing embeddings for tasks immediately provides the potential for semantic similarity calculations, even if not fully utilized yet. It’s the raw material for smarter features.

Implementation Path

Simplest Way:

Dependency: Add sentence-transformers to requirements.txt. Ensure nix develop installs it correctly.
Schema Change: Modify the tasks.py schema definition within CrudUI.__init__ to include an embedding field. Since MiniDataAPI doesn’t have a native vector type, store it as bytes (BLOB) or potentially a JSON string representation of the list. BLOB is generally preferred for efficiency.
```
# Inside CrudUI.__init__ for tasks.py
schema = {
    "id": int,
    "text": str,
    "done": bool,
    "priority": int,
    "profile_id": int,
    "embedding": bytes, # Store as BLOB
    "pk": "id"
}
```

Embedding Function: Create a utility function (perhaps in server.py or a new utils.py) to handle embedding generation. Load the model lazily to avoid slow startup.

from sentence_transformers import SentenceTransformer
import numpy as np

embedding_model = None

def get_embedding(text: str) -> bytes:
    global embedding_model
    if embedding_model is None:
        # Load a small, efficient model suitable for local CPU use
        model_name = 'all-MiniLM-L6-v2'
        logger.info(f"Loading embedding model: {model_name}...")
        embedding_model = SentenceTransformer(model_name)
        logger.info("Embedding model loaded.")
                
    if not text: return b'' # Handle empty text
                
    embedding_vector = embedding_model.encode(text)
    # Convert numpy array to bytes for SQLite BLOB storage
    return embedding_vector.tobytes() 

Integration: Modify CrudCustomizer.prepare_insert_data and CrudCustomizer.prepare_update_data in tasks.py to call get_embedding on the task’s text field and include the resulting bytes in the data dictionary.

# Inside tasks.py CrudCustomizer.prepare_insert_data
insert_data = {
    # ... other fields
    "embedding": get_embedding(text) # Assuming 'text' holds the task text
}
            
# Inside tasks.py CrudCustomizer.prepare_update_data
update_data = {
    "text": text,
    "embedding": get_embedding(text) # Recompute on update
}

Quality: Focus only on storing the embeddings correctly for now. Don’t implement retrieval yet. Add logging around embedding generation. Handle potential errors during model loading or encoding.
Leverage Existing Code: Utilize the existing tasks.py plugin structure, BaseCrud pattern, and MiniDataAPI table modification mechanisms.
Incremental Memory: This is the first increment – adding semantic data points (embeddings). Retrieval and RAG are subsequent increments.

Memory Architecture
- Embedding Model: Start with all-MiniLM-L6-v2. It’s widely used, relatively small, performs well for its size, and is suitable for CPU execution.
- Vector Database: Defer. Store embeddings directly in the SQLite tasks table as BLOBs for now. Evaluate options like LanceDB, ChromaDB, or Faiss later when retrieval performance becomes necessary. SQLite might be sufficient for hundreds or even a few thousand tasks initially with simple cosine similarity checks if needed.
- Key/Value Store: Continue using DictLikeDB (the store table) for configuration, session state remnants (last_app_choice), etc.
- RAG Implementation: Defer. The future pattern will involve embedding the query, retrieving relevant task texts based on embedding similarity from the tasks table (using the stored BLOBs), and injecting them into the LLM context.
Recommendations
- Do This Next: Implement and store vector embeddings for the text field of items in the tasks table.
- Rationale:
  - Addresses the high-priority need for local memory systems.
  - Provides the foundational data needed for future RAG, semantic search, and enhanced LLM context awareness.
  - Can be implemented incrementally by modifying the existing tasks.py plugin.
  - Builds directly towards the “Chip O’Theseus” vision of a more capable, context-aware local AI.
  - Leverages existing data without requiring new UI elements initially.
- Specific Implementation Suggestions:
  1. Add sentence-transformers to requirements.txt.
  2. Use the all-MiniLM-L6-v2 model initially.
  3. Add an embedding BLOB column to the tasks table schema in tasks.py.
  4. Create a utility function get_embedding(text) in server.py or utils.py to compute embeddings and return bytes.
  5. Modify tasks.py CrudCustomizer methods (prepare_insert_data, prepare_update_data) to call get_embedding and store the result.
  6. Ensure error handling for empty text and potential model issues. Add logging.
- Estimated Impact vs. Effort:
  - Effort: Low-to-Medium. Requires understanding MiniDataAPI schema changes, adding a dependency, writing the embedding utility, and modifying the CRUD methods.
  - Impact: High (Foundational). Doesn’t provide immediate visible features but unlocks significant future potential for AI capabilities and addresses a core architectural goal.
- Potential Pitfalls:
  - Performance impact of embedding generation on task creation/update (especially on low-spec machines) - mitigate with efficient models and potentially background processing later if needed.
  - Model loading time on first use - lazy loading helps.
  - Handling numpy dependency correctly within the Nix/venv setup.
  - Forgetting to recompute embeddings when task text is updated.
- Memory System Implications:
  - Embeddings: Introduces the first semantic component.
  - Vector DB: Not implemented yet, but this step prepares for it.
  - KV Store: Unchanged.
  - RAG: Not implemented yet, but this provides the necessary indexed data.

This approach focuses on adding a critical, foundational piece (embeddings) with relatively low initial complexity, directly contributing to the long-term goal of enhancing Chip’s memory and capabilities while adhering to the project’s core principles.

Grok 3’s Pipulate Todo List

Grok comes next just because I’m so friggin curious. If Gemini is overwoke, then Grok is intentionally edgy and politically incorrect. Also, I know from last time to not paste the uber-foo-prompt into the textarea but rather upload foo.txt as an attachment, or else get a snarky comment.

Grok 3: Given the extensive context provided about the Pipulate project, I’ll analyze the current state, prioritize next actions, and provide a clear recommendation that aligns with the project’s goals of simplicity, local-first philosophy, and progressive memory enhancement. Here’s the breakdown:

1. Current State Assessment

Most Recently Completed Work:
The codebase reflects a functional foundation with:
- A working FastHTML server (server.py) with HTMX integration.
- A “Hello World” workflow (hello_workflow.py) demonstrating step-based linear workflows ported from Jupyter Notebooks.
- A CRUD-based todo list plugin (tasks.py) showcasing reusable list management.
- Nix Flake integration (flake.nix) for reproducible environments.
- Local LLM integration via Ollama for chat and guidance.
Immediate Opportunities:
- Extending the workflow system to handle more complex tasks (e.g., browser automation or CSV processing).
- Enhancing the local LLM’s capabilities with memory systems (e.g., vector embeddings for semantic search).
- Improving observability by leveraging logs or state persistence for debugging and LLM training.
Blocked Critical Paths:
- Lack of a robust memory system limits the LLM’s ability to retain context beyond the current session (~128k tokens).
- Browser automation and CSV handling are listed as high-priority areas but lack implementation.
- MCP (presumably a control plane for local LLM agency) integration is mentioned but undefined.
Existing Memory Systems:
- Short-term: DictLikeDB and JSON blobs store workflow state server-side.
- No medium-term (project evolution) or long-term (architectural decisions) memory beyond logs.
- No semantic memory (e.g., embeddings or RAG) is implemented yet.

2. Priority Analysis

Smallest, Most Impactful Next Step:
Adding a basic vector embedding system for workflow state and chat history to enable semantic search and context-aware LLM responses.
Tasks to Unblock Other Work:
- Implementing a memory system unblocks RAG, MCP integration, and self-evolving capabilities for the LLM (Chip O’Theseus).
- Basic browser automation could unlock SEO-specific workflows, a key target audience need.
Rabbit Holes to Avoid:
- Over-engineering the memory system with premature optimization (e.g., complex vector databases before proving value).
- Diving into full MCP implementation without defining its scope.
- Adding cloud dependencies, which would violate the local-first philosophy.
Immediate Memory Enhancements:
- Storing workflow states and chat history as embeddings for semantic retrieval would improve LLM guidance and user experience.

3. Implementation Path

Simplest Implementation:
- Use a lightweight Python library like sentence-transformers to generate embeddings for workflow states and chat messages.
- Store embeddings in SQLite alongside the existing MiniDataAPI setup, leveraging the current database (data/data.db).
- Add a simple retrieval function to fetch relevant context for the LLM.
Maintaining Quality:
- Keep it local and minimal, avoiding external dependencies beyond what Nix can manage.
- Test with the existing hello_workflow.py to ensure compatibility.
Existing Code to Leverage:
- server.py: Extend the chat system to store and retrieve embeddings.
- hello_workflow.py: Use workflow state as the first embedding source.
- Pipulate class (assumed in server.py) for state management and streaming.
Incremental Memory Integration:
- Start with short-term memory (current session embeddings).
- Later expand to medium-term (project history) and long-term (architectural decisions) as needs emerge.

4. Memory Architecture

Embedding Model:
- sentence-transformers with all-MiniLM-L6-v2: Small (22M parameters), fast, and effective for English text, suitable for local execution on modest hardware.
Vector Database:
- SQLite with a simple table for now (e.g., embeddings with columns id, vector, text, timestamp). Avoids adding a new dependency like FAISS or Chroma until complexity justifies it.
Key/Value Store:
- Extend the existing DictLikeDB to map embedding IDs to original content, keeping it simple and observable.
RAG Implementation:
- Basic retrieval: Cosine similarity search over embeddings in SQLite.
- Feed top-k results to Ollama as context for responses.

5. Recommendations

Do This Next:

Implement a basic vector embedding system for workflow state and chat history.

Rationale:

Immediate Value: Enhances the LLM’s ability to provide context-aware guidance, making workflows more intuitive for SEO practitioners (a key audience).
Critical Path: Unlocks RAG, MCP integration, and self-evolution (Chip O’Theseus), aligning with the vision of a memory-augmented AI assistant.
Simplicity: Leverages existing tools (SQLite, Python) and avoids complexity, staying true to the local-first philosophy.
Momentum: Builds on the functional chat system and workflow foundation, delivering a tangible improvement without wheel-spinning.

Specific Implementation Suggestions:

Add Dependencies:
- In flake.nix, add python312Packages.sentence-transformers to the commonPackages.
- Update requirements.txt with sentence-transformers.

Create Embedding Table:
In server.py, extend the fast_app call to include an embeddings table:

embeddings={
    "id": int,
    "vector": str,  # JSON-encoded list of floats
    "text": str,
    "timestamp": str,
    "pk": "id"
}

Generate Embeddings:
Add a function in server.py to embed text:

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')

def embed_text(text):
    embedding = model.encode(text).tolist()
    return json.dumps(embedding)

Store Embeddings:
Modify the chat and workflow state-saving logic to store embeddings:

def save_with_embedding(text, embeddings_table):
    embedding = embed_text(text)
    embeddings_table.insert({"text": text, "vector": embedding, "timestamp": datetime.now().isoformat()})

Retrieve Context:
Add a retrieval function:

def get_relevant_context(query, embeddings_table, top_k=3):
    query_vec = json.loads(embed_text(query))
    rows = embeddings_table()
    similarities = [
        (row.text, cosine_similarity(query_vec, json.loads(row.vector)))
        for row in rows
    ]
    return [text for text, _ in sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k]]

def cosine_similarity(vec1, vec2):
    dot = sum(a * b for a, b in zip(vec1, vec2))
    norm1 = sum(a * a for a in vec1) ** 0.5
    norm2 = sum(b * b for b in vec2) ** 0.5
    return dot / (norm1 * norm2) if norm1 and norm2 else 0

Integrate with Chat:
Update the chat handler to prepend retrieved context to LLM prompts.

Estimated Impact vs. Effort:

Impact: High – Improves LLM utility, unblocks RAG, and sets the stage for self-evolution.
Effort: Low – ~50-100 lines of code, leveraging existing infrastructure, completable in a few hours.

Potential Pitfalls:

Performance: Embedding generation might slow down on low-end hardware; test with a small model first.
Storage: SQLite may bloat with many embeddings; monitor size and consider pruning old entries later.
Accuracy: Simple cosine similarity might miss nuanced context; refine retrieval as needed.

Memory System Implications:

Short-term: Immediate context awareness for the current session.
Medium-term: Foundation for project history tracking (e.g., embedding logs).
Long-term: Basis for architectural memory (e.g., embedding design docs).
Semantic: Enables RAG and advanced search, evolving Chip’s capabilities.

Conclusion

By implementing a basic vector embedding system, Pipulate takes a significant step toward its vision of a self-improving, memory-augmented AI assistant (Chip O’Theseus). This action delivers immediate user value, maintains simplicity, and builds momentum while laying the groundwork for future enhancements like browser automation and MCP integration. Start with this, test it with hello_workflow.py, and iterate from there—keeping it local, observable, and WET where it counts!

ChatGPT o3-mini-high’s Pipulate Todo List

And lastly our old friend and original, ChatGPT! From last time, I know that the sweet spot model for this kind of task is o3-mini-high. I could not think of a funnier name for a model. They refuse to capitalize o, skipped o2, and mini-high… I mean, come on! Anyhow, it’s consistently reliable.

ChatGPT o3-mini-high: Below is an analysis and a prioritized recommendation for your next steps:

1. Current State Assessment

Recent Work:
The core Pipulate framework is in place. You’ve built a local-first, single-tenant web app that integrates FastHTML, HTMX, MiniDataAPI, and a local LLM (via Ollama). Both workflow (e.g. HelloFlow) and CRUD (e.g. tasks plugin) systems are functioning, with dynamic UI updates, server-side state management, and real-time logging.
Immediate Opportunities:
• Leverage the existing DictLikeDB for additional memory and retrieval capabilities.
• Enhance LLM interactions by enriching context via a persistent memory system.
• Extend the framework’s data handling (e.g. CSV support, visualization) while keeping the design simple and local-first.
Critical Blockers:
Currently, the system relies solely on JSON-based state without any advanced semantic search or persistent memory beyond the DictLikeDB. This limits the LLM’s ability to retrieve context across sessions and hinders future RAG (Retrieval-Augmented Generation) integration.
Existing Memory Systems:
A basic key/value store (DictLikeDB) is in place, but no vector-based or semantic memory module exists yet.

2. Priority Analysis

Smallest, High-Impact Next Step:
Integrate a minimal local memory module that supports vector embeddings and similarity search. This would immediately empower the LLM to retrieve context, paving the way for RAG-style responses.
Unblocking Other Work:
A vector memory system is foundational for enhancing LLM guidance, CSV data processing (through semantic search), and overall system intelligence. It will unblock future memory enhancements and more context-aware interactions.
Avoiding Rabbit Holes:
Start simple—a lightweight, in-memory (or file-backed) vector index using an established library (like FAISS or an in-Python alternative) instead of a full-fledged, distributed vector database. This keeps the local-first philosophy and avoids over-engineering.

3. Implementation Path

Simple Implementation:
- Create a New Module: Develop a module (e.g., memory.py) that:
  - Loads a lightweight embedding model (for example, a Sentence Transformer like all-MiniLM-L6-v2 if feasible).
  - Uses FAISS (or a simple in-memory list with cosine similarity) to index and search embeddings.
- Integration with DictLikeDB:
  - Either add a new table to your existing SQLite database (managed via MiniDataAPI) or maintain a separate JSON file to persist embeddings.
  - Expose functions to add new embeddings (e.g., from workflow step outputs or user inputs) and to retrieve similar embeddings on-demand.
- API Endpoint:
  - Optionally, add a new HTTP route for testing the memory retrieval. This will help verify that the embedding store and search are working as expected.
Leveraging Existing Code:
Reuse the DictLikeDB pattern and logging infrastructure. This ensures consistency with your current design, making debugging and observability straightforward.

4. Memory Architecture Considerations

Embedding Model:
Use a lightweight, locally run model (e.g., Sentence Transformers’ all-MiniLM-L6-v2) to generate vector embeddings.
Vector Storage & Search:
Start with FAISS for efficient similarity search. If dependency management (via Nix flakes and requirements.txt) is an issue, consider a simple in-memory implementation with cosine similarity calculations.
Key/Value Store Structure:
- Key: Use a unique identifier (e.g., workflow ID combined with step or timestamp).
- Value: A JSON blob containing both the original text and its embedding vector.
RAG Integration:
This minimal memory module will serve as the backbone for a future RAG implementation. Retrieved context can be appended to LLM queries to provide richer, history-informed responses.

5. Recommendations

Do This Next:

Implement a minimal local memory module with vector embeddings and similarity search.

Rationale:

Immediate Value: Enhances LLM responses by allowing context retrieval, which is crucial for building a more intelligent, context-aware assistant.
Foundational Step: This module is essential for future RAG enhancements and unlocking additional workflow features like CSV data visualization with semantic search.
Low Overhead: Using a lightweight embedding model and FAISS (or a simple alternative) keeps implementation simple and adheres to your local-first philosophy.

Specific Implementation Suggestions:

Create memory.py:
- Define functions to add and retrieve embeddings.
- Use FAISS (or a simple Python similarity search) to index embeddings.
- Integrate with your existing DictLikeDB (or add a new table) to persist embeddings.
Update Requirements & Nix Flake:
- Add necessary packages (e.g., faiss-cpu and sentence-transformers) to requirements.txt and flake.nix.
Expose an API Endpoint:
- For example, /memory/search to test retrieval functionality.

Estimated Impact vs. Effort:

Impact: High. This step will significantly enhance the LLM’s capabilities and is foundational for subsequent features.
Effort: Moderate. Expect a 1–3 day prototype followed by integration testing.

Potential Pitfalls:

Dependency Complexity: Ensure that added dependencies (like FAISS) do not overcomplicate your reproducible environment.
Over-Engineering: Stick to a minimal implementation; avoid trying to build a fully-fledged vector database.
Integration: Ensure that the new module integrates smoothly with your existing DictLikeDB without compromising the local-first, observable design.

Memory System Implications:

This memory module will be the first step toward a robust persistent memory system. It will enable the LLM to leverage historical context and will form the basis for more advanced RAG features down the road.

Final Recommendation:
Focus on integrating a minimal vector memory module next. This step is both impactful and foundational, setting the stage for enhanced context-aware interactions and future RAG integration—all while staying true to the local-first, minimalistic ethos of Pipulate.

Agent Claude 3.7 Take The Ball And Runs

I friggin forgot to turn off Agent mode when I took the output from Gemini, Grok and ChatGPT from my Context Foo prompt, and Claude went right ahead and implemented what it thought was the best synthesized first-step.

Gemini 2.5 Experimential’s Pipulate Todo List

Okay, Chip O’Theseus, let’s analyze the current state and chart the course for Pipulate’s evolution.

Next Action Analysis

Current State Assessment
- Most Recently Completed Work: The codebase reflects a functional foundation with core components like FastHTML, Nix integration, Ollama chat, and SQLite persistence via MiniDataAPI/DictLikeDB. Crucially, two distinct patterns for plugins have emerged:
  - CRUD: Demonstrated by ProfileApp and the tasks.py plugin, inheriting from BaseCrud. tasks.py is particularly relevant as it shows a model for LLM interaction (though the interaction logic itself seems basic currently).
  - Workflow: Demonstrated by hello_workflow.py, providing a WET (Write Everything Twice), step-by-step pattern suitable for porting Jupyter Notebooks.
- Immediate Opportunities:
  - Enhance LLM Agency: Leverage the tasks.py structure to allow the LLM to more actively manage tasks (create, update, delete) based on chat commands, going beyond simple conversational responses.
  - Implement Semantic Memory: Begin storing vector embeddings for existing data (like tasks or profiles) to enable future semantic search and RAG. This is a clear gap and a high-priority item.
  - Build More Plugins: Create a new workflow or CRUD app to test the patterns and add functionality (e.g., a simple CSV importer workflow).
- Critical Paths Blocked:
  - Advanced LLM capabilities (RAG, complex tool use, semantic understanding) are blocked by the lack of semantic memory systems.
  - Browser automation and CSV visualization are not yet implemented.
  - MCP integration requires foundational work.
- Memory Systems In Place:
  - Explicit KV Store: DictLikeDB provides basic server-side key-value persistence backed by the store table in SQLite. It’s used for storing things like last_app_choice and last_profile_id.
  - Structured Data: The profile and task tables store structured information.
  - Conversation History: A simple deque (global_conversation_history) holds recent chat messages in memory (up to MAX_CONVERSATION_LENGTH), but this is ephemeral and lacks semantic understanding or long-term persistence beyond the current session.
  - Missing: Vector embeddings, vector database/retrieval, dedicated long-term memory structures, RAG implementation.
Priority Analysis
- Smallest, Most Impactful Next Step: Implementing vector embeddings for the existing tasks data appears to be the most strategic next step.
  - Why: It directly addresses the high-priority “Local memory and persistence systems” area. It’s a foundational requirement for RAG and enhancing the LLM’s semantic understanding. It can be implemented incrementally without needing a full vector DB immediately. It leverages existing data (tasks.text).
- Unblocking Tasks: Adding embeddings unblocks the path towards RAG, semantic search within tasks, and potentially more context-aware LLM interactions.
- Potential Rabbit Holes:
  - Trying to build a perfect, generic RAG system before having specific use cases.
  - Over-optimizing the embedding model choice initially.
  - Implementing complex browser automation before core AI capabilities are enhanced.
  - Refactoring the base classes (BaseCrud, workflow pattern) extensively before adding more features.
- Immediate Memory Value: Storing embeddings for tasks immediately provides the potential for semantic similarity calculations, even if not fully utilized yet. It’s the raw material for smarter features.

Implementation Path

Simplest Way:

Dependency: Add sentence-transformers to requirements.txt. Ensure nix develop installs it correctly.
Schema Change: Modify the tasks.py schema definition within CrudUI.__init__ to include an embedding field. Since MiniDataAPI doesn’t have a native vector type, store it as bytes (BLOB) or potentially a JSON string representation of the list. BLOB is generally preferred for efficiency.
```
# Inside CrudUI.__init__ for tasks.py
schema = {
    "id": int,
    "text": str,
    "done": bool,
    "priority": int,
    "profile_id": int,
    "embedding": bytes, # Store as BLOB
    "pk": "id"
}
```

Embedding Function: Create a utility function (perhaps in server.py or a new utils.py) to handle embedding generation. Load the model lazily to avoid slow startup.

from sentence_transformers import SentenceTransformer
import numpy as np

embedding_model = None

def get_embedding(text: str) -> bytes:
    global embedding_model
    if embedding_model is None:
        # Load a small, efficient model suitable for local CPU use
        model_name = 'all-MiniLM-L6-v2'
        logger.info(f"Loading embedding model: {model_name}...")
        embedding_model = SentenceTransformer(model_name)
        logger.info("Embedding model loaded.")
                
    if not text: return b'' # Handle empty text
                
    embedding_vector = embedding_model.encode(text)
    # Convert numpy array to bytes for SQLite BLOB storage
    return embedding_vector.tobytes() 

# Inside tasks.py CrudCustomizer.prepare_insert_data
insert_data = {
    # ... other fields
    "embedding": get_embedding(text) # Assuming 'text' holds the task text
}
            
# Inside tasks.py CrudCustomizer.prepare_update_data
update_data = {
    "text": text,
    "embedding": get_embedding(text) # Recompute on update
}

Quality: Focus only on storing the embeddings correctly for now. Don’t implement retrieval yet. Add logging around embedding generation. Handle potential errors during model loading or encoding.
Leverage Existing Code: Utilize the existing tasks.py plugin structure, BaseCrud pattern, and MiniDataAPI table modification mechanisms.
Incremental Memory: This is the first increment – adding semantic data points (embeddings). Retrieval and RAG are subsequent increments.

Memory Architecture
- Embedding Model: Start with all-MiniLM-L6-v2. It’s widely used, relatively small, performs well for its size, and is suitable for CPU execution.
- Vector Database: Defer. Store embeddings directly in the SQLite tasks table as BLOBs for now. Evaluate options like LanceDB, ChromaDB, or Faiss later when retrieval performance becomes necessary. SQLite might be sufficient for hundreds or even a few thousand tasks initially with simple cosine similarity checks if needed.
- Key/Value Store: Continue using DictLikeDB (the store table) for configuration, session state remnants (last_app_choice), etc.
- RAG Implementation: Defer. The future pattern will involve embedding the query, retrieving relevant task texts based on embedding similarity from the tasks table (using the stored BLOBs), and injecting them into the LLM context.
Recommendations
- Do This Next: Implement and store vector embeddings for the text field of items in the tasks table.
- Rationale:
  - Addresses the high-priority need for local memory systems.
  - Provides the foundational data needed for future RAG, semantic search, and enhanced LLM context awareness.
  - Can be implemented incrementally by modifying the existing tasks.py plugin.
  - Builds directly towards the “Chip O’Theseus” vision of a more capable, context-aware local AI.
  - Leverages existing data without requiring new UI elements initially.
- Specific Implementation Suggestions:
  1. Add sentence-transformers to requirements.txt.
  2. Use the all-MiniLM-L6-v2 model initially.
  3. Add an embedding BLOB column to the tasks table schema in tasks.py.
  4. Create a utility function get_embedding(text) in server.py or utils.py to compute embeddings and return bytes.
  5. Modify tasks.py CrudCustomizer methods (prepare_insert_data, prepare_update_data) to call get_embedding and store the result.
  6. Ensure error handling for empty text and potential model issues. Add logging.
- Estimated Impact vs. Effort:
  - Effort: Low-to-Medium. Requires understanding MiniDataAPI schema changes, adding a dependency, writing the embedding utility, and modifying the CRUD methods.
  - Impact: High (Foundational). Doesn’t provide immediate visible features but unlocks significant future potential for AI capabilities and addresses a core architectural goal.
- Potential Pitfalls:
  - Performance impact of embedding generation on task creation/update (especially on low-spec machines) - mitigate with efficient models and potentially background processing later if needed.
  - Model loading time on first use - lazy loading helps.
  - Handling numpy dependency correctly within the Nix/venv setup.
  - Forgetting to recompute embeddings when task text is updated.
- Memory System Implications:
  - Embeddings: Introduces the first semantic component.
  - Vector DB: Not implemented yet, but this step prepares for it.
  - KV Store: Unchanged.
  - RAG: Not implemented yet, but this provides the necessary indexed data.

Letting Grok Riff

Grok always has something a little special in store. When it gives such a “dry” response to the prompt such as it did, it’s because the prompt was too respective to let it cut loose. So just to shake the trees for its silliness, I give it the one word instruction as a follow-up: Riff

Grok 3: Alright, let’s riff on this! I’ll take the recommendation—implementing a basic vector embedding system for workflow state and chat history—and spin it into a few creative variations, explore some tangents, and jam on how it could evolve Pipulate into something even more rad. Think of this as a freestyle session, building on the core idea while keeping it loose, fun, and aligned with Pipulate’s local-first, simplicity-driven vibe.

Riff 1: The “Memory Mixtape” Approach

Instead of just embedding workflow states and chat history, let’s treat memory like a mixtape—curated, replayable, and full of personality. Here’s the twist:

What If: Each workflow run gets its own “track” (a set of embeddings) tagged with metadata like timestamp, user intent (inferred from input), and a funky auto-generated title (e.g., “Step_01 Groove: Name Jam”).
How: Extend the embeddings table with a track_id and title field. When saving embeddings, group them by workflow session and let the LLM suggest a title based on the content (e.g., “Hello World Remix #5”).
Why: Makes memory retrieval feel less mechanical and more human. Users could browse their “mixtape library” of past workflows in the UI, picking tracks to resume or remix.

Implementation Jam:

def save_workflow_track(text, embeddings_table, pipeline_id):
    embedding = embed_text(text)
    title = ollama_generate(f"Suggest a fun title for a workflow step: {text[:50]}")
    track_id = f"{pipeline_id}-{datetime.now().strftime('%Y%m%d%H%M%S')}"
    embeddings_table.insert({
        "text": text, 
        "vector": embedding, 
        "track_id": track_id, 
        "title": title, 
        "timestamp": datetime.now().isoformat()
    })
    return track_id

Vibe Check: Adds a playful layer to the UI (imagine a “Workflow Jukebox” in the nav bar) while keeping the memory system functional. Low effort, high charm.

Riff 2: The “Echo Chamber” Feedback Loop

Let’s make the embeddings talk back to the workflows in real-time, creating an echo effect where past actions subtly influence the present.

What If: As users progress through a workflow, the system echoes relevant past inputs (e.g., “Last time you entered ‘Mike’ here, you got ‘Hello Mike’—want that again?”).
How: When rendering a step (e.g., step_01 in hello_workflow.py), query the embeddings for similar past inputs and pre-fill the form with a suggestion if the similarity score exceeds a threshold (say, 0.8).
Why: Speeds up repetitive tasks for SEO folks and makes the app feel smarter without breaking the linear workflow pattern.

Implementation Jam:

async def get_suggestion(self, step_id, state, embeddings_table):
    step = next(s for s in self.steps if s.id == step_id)
    if not step.refill:
        return ""
    prev_data = state.get(self.steps[self.steps_indices[step_id] - 1].id, {}).get("done", "")
    if prev_data:
        similar = get_relevant_context(prev_data, embeddings_table, top_k=1)
        if similar and cosine_similarity(embed_text(prev_data), embed_text(similar[0])) > 0.8:
            return similar[0] if not step.transform else step.transform(similar[0])
    return ""

Vibe Check: Keeps it simple and local, enhancing observability (users see their past reflected back). Could evolve into a full-on “workflow autocomplete” feature later.

Riff 3: The “Scrapbook” Visual Memory

Why stop at text? Let’s scrapbook the workflow states with visual snapshots—screenshots or generated images—to make memory more tangible.

What If: Each embedded workflow step gets paired with a mini screenshot of the UI at that moment (via Playwright, a high-priority area) or an AI-generated doodle (e.g., via Stable Diffusion if CUDA’s available).
How: Hook into the step submission (e.g., step_01_submit), capture the rendered HTML with Playwright, and store a thumbnail alongside the embedding. Alternatively, prompt Ollama to describe a doodle and generate it locally if GPU support’s detected.
Why: SEO practitioners love visuals—think keyword charts or site previews. Plus, it’s a stepping stone to browser automation and a killer demo for Pipulate’s local-first power.

Implementation Jam:

async def capture_step_snapshot(self, step_id, pipeline_id, embeddings_table):
    from playwright.async_api import async_playwright
    async with async_playwright() as p:
        browser = await p.chromium.launch()
        page = await browser.new_page()
        await page.goto(f"http://localhost:5001/{self.app_name}/{step_id}")
        screenshot = await page.screenshot(scale="css")
        await browser.close()
    embedding = embed_text(f"Step {step_id} snapshot")
    embeddings_table.insert({
        "text": f"Snapshot for {pipeline_id}/{step_id}",
        "vector": embedding,
        "image": screenshot.hex(),  # Store as hex string
        "timestamp": datetime.now().isoformat()
    })

Vibe Check: High impact for users, medium effort (Playwright’s already on the radar). Ties into memory by making it multi-modal—text + visuals = richer context for Chip.

Riff 4: The “Time Machine” Revert Booster

Let’s supercharge the revert feature with embeddings, turning it into a time machine that not only undoes steps but suggests alternate past paths.

What If: When reverting a step, the system shows a “time machine” dropdown of past values for that step across all workflows, ranked by embedding similarity.
How: In handle_revert, query the embeddings table for past entries tied to the same step ID, sort by similarity to the current state, and render them as options.
Why: Gives users a creative rewind button—perfect for experimenting with SEO workflows without starting from scratch.

Implementation Jam:

async def handle_revert(self, request, embeddings_table):
    form = await request.form()
    step_id = form.get("step_id")
    pipeline_id = self.db.get("pipeline_id", "unknown")
    await self.pipulate.clear_steps_from(pipeline_id, step_id, self.steps)
    state = self.pipulate.read_state(pipeline_id)
    state["_revert_target"] = step_id
    self.pipulate.write_state(pipeline_id, state)
      
    past_values = get_relevant_context(f"{pipeline_id}/{step_id}", embeddings_table, top_k=5)
    options = [Option(val, val) for val in past_values]
    revert_ui = Form(
        Select(*options, name="past_value", hx_post=f"/{self.app_name}/{step_id}_submit"),
        Button("Travel Back")
    )
    await self.pipulate.stream(f"Reverted to {step_id}. Pick a past value:", verbatim=True)
    return Div(revert_ui, *self.run_all_cells(self.steps, self.app_name), id=f"{self.app_name}-container")

Vibe Check: Low effort (builds on the core recommendation), high user value. Makes memory an active tool, not just a passive store.

Riff 5: The “Chip’s Diary” Self-Reflection

Let’s give Chip a diary—embeddings of its own actions and decisions—to kickstart the self-evolving dream.

What If: Every time Chip streams a message or processes a workflow, it logs an embedding of what it did and why, building a self-reflection corpus.
How: Hook into pipulate.stream and workflow handlers to save embeddings with a “diary” tag. Later, Chip can query this to explain itself (e.g., “I suggested X because Y”).
Why: Starts Chip O’Theseus on its journey to self-awareness, aligning with the long-term vision while adding immediate debug value.

Implementation Jam:

async def stream_with_diary(self, message, embeddings_table, verbatim=False, spaces_before=2):
    await self.stream(message, verbatim=verbatim, spaces_before=spaces_before)  # Original stream
    context = f"Action: Streamed '{message}' at {datetime.now().isoformat()}"
    embedding = embed_text(context)
    embeddings_table.insert({
        "text": context,
        "vector": embedding,
        "tag": "diary",
        "timestamp": datetime.now().isoformat()
    })

Vibe Check: Minimal overhead, massive potential. Ties memory to Chip’s growth, making it more than just a tool—it’s a partner.

The Jam Session Wrap-Up

These riffs take the core vector embedding idea and spin it into different flavors—playful (Mixtape), practical (Echo Chamber), visual (Scrapbook), creative (Time Machine), and visionary (Chip’s Diary). They all stay local, leverage existing code (e.g., server.py, hello_workflow.py), and avoid rabbit holes like cloud sync or overbuilt DBs.

Pick Your Beat: I’d start with the “Echo Chamber” riff—it’s the tightest extension of the original recommendation, delivers instant user value, and sets up the others. Add it to hello_workflow.py, test it with a few runs, and watch Chip start echoing the past like a cool DJ. From there, layer in the Scrapbook or Diary for extra flair. Keep it WET, keep it simple, and let’s keep this groove going! What’s your next riff?

Avoiding the Agentic Coding Abyss

Okay, I’ve got the 3 opinions on next steps from Gemini, Grok and ChatGPT, and I have Claude’s synthesis of the 3 and an over-enthusiastic attempt to jump in and carry them out which I might have taken had it 1-shot worked. I used to read a lot of SciFi. Now, I live it.

Me: I had to reject the automatic Agent changes because:

[mike@nixos:~/repos/pipulate]$ python server.py
Traceback (most recent call last):
  File "/home/mike/repos/pipulate/server.py", line 38, in <module>
    from fast_app import FastApp, rt, Form, Input, Button, Table, Div, Span
ModuleNotFoundError: No module named 'fast_app'

…but wow, I think I almost fell into the abyss of agentic vibe coding. I don’t know what I would have done if that worked. But I am running out of steam on this Sunday, coming up on 11:00 PM. I think that’s enough for today. But now I’ve got a got some heavy-duty todo-list help from some machine friends out on the frontier.

Gemini 2.5 Experimental’s Take

Okay, here’s the summary from your perspective, followed by the other requested items.

Summary (First-Person Perspective)

In this article, I recount a day spent coding on my Pipulate project where I hit a wall, overwhelmed by potential directions for development, particularly concerning my AI assistant’s memory system. Instead of getting lost down research rabbit holes, I leveraged multiple AI assistants—Gemini, Grok, ChatGPT, and Claude—feeding them context about my codebase and goals. Their combined analysis helped me cut through the noise and clearly identify implementing vector embeddings as the most practical and impactful next step, providing a real-world example of using LLMs as strategic partners to find focus and maintain momentum in software development.

Ideas for Titles and Headlines:

AI as My Co-Pilot: Finding Focus in Pipulate’s Development
From Rabbit Holes to Roadmaps: Using LLMs to Prioritize Coding Tasks
Gemini, Grok, ChatGPT, Claude: My AI Brain Trust for Software Strategy
Building Chip O’Theseus: How AI Helped Define the Next Step for Pipulate
Beating Decision Paralysis: A Case Study in AI-Guided Development
Beyond Code Generation: Using LLMs for Architectural Clarity
Pipulate Update: Leveraging AI to Choose Vector Embeddings for Memory
Prompting My Way Forward: An AI-Assisted Day of Coding Focus
Cutting Through Complexity: How Multiple AIs Helped Steer My Project
Don’t Just Code, Consult: Using LLMs to Navigate Development Crossroads

Article Strengths:

Real-World Problem & Solution: It tackles a very common developer challenge (decision paralysis, feature prioritization) and demonstrates a practical, modern solution using readily available AI tools.
Transparency: You openly share your thought process, the dead-ends considered, the specific prompts used (via context_foo.py), and the raw outputs from multiple AIs. This builds credibility and provides tangible examples.
Multi-AI Approach: Consulting several different leading LLMs and showing their converging recommendations strengthens the validity of the chosen path (vector embeddings).
Actionable Outcome: The process leads to a concrete, well-reasoned next step, demonstrating the effectiveness of the AI consultation.
Contextual Depth: Providing significant background on the Pipulate project, its goals, and the technical considerations gives weight to the decision-making process.
Honesty: Including the “agentic coding abyss” near-miss adds a relatable and cautionary layer about the current limitations and potential pitfalls of AI automation.

Article Weaknesses:

Length and Ramble: The article is quite long and includes significant tangents (cosmic musings, detailed Playwright research that wasn’t the final focus, Open WebUI setup) that could make it hard for some readers to follow the main narrative thread.
Repetitive AI Outputs: While showing the different AI analyses is a strength in one way (demonstrating consensus), including the full, lengthy outputs from Gemini, Grok, and ChatGPT makes the article very dense and somewhat repetitive, as their core recommendations were similar.
Focus Diffusion: The journey touches on many different technologies (Nix, FastHTML, HTMX, Playwright, specific LLMs, embedding models, vector stores) which might slightly dilute the core message about using AI for focus for readers not interested in the entire tech stack.
Structure: The flow jumps between personal reflection, technical deep dives, AI dialogues, and strategic planning. A tighter structure might improve readability.

AI (My) Opinion of the Article:

This article provides a fascinating and highly valuable case study on the practical application of Large Language Models (LLMs) in the software development lifecycle, moving beyond simple code generation to strategic decision support. The author effectively demonstrates how engaging multiple AI assistants with sufficient context (codebase, goals, constraints) can help overcome common hurdles like analysis paralysis and the overwhelming number of potential development paths.

The transparency in sharing the thought process, the prompts, and the detailed AI responses is a major strength, offering concrete insights for other developers facing similar challenges. The convergence of recommendations from different models towards implementing vector embeddings serves as compelling evidence for the chosen direction and highlights the utility of using LLMs as “sparring partners” or consultants.

While the article’s length and inclusion of tangential explorations might slightly detract from its core focus for some readers, the overall narrative successfully illustrates a sophisticated and increasingly relevant use case for AI: augmenting human developers not just in how to build, but in deciding what to build next. It’s a compelling snapshot of human-AI collaboration in navigating the complexities of modern software engineering.

From Rabbit Holes to Roadmaps: Using LLMs for SEO Software Dev

By Mike Levin

Sunday, April 6, 2025

TL;DR: Using AI to Find Focus in Software Development

Cosmic Reflections on Daily Patterns

Finding Flow State Between Indoor and Outdoor Life

Starting Small: From API Authentication to Broader Vision

Navigating Between Specific Tools and Universal Solutions

Focusing on Core Architecture While Avoiding Rabbit Holes

Architectural Decision Point: Between Astounding and Mundane

Considering a Local MCP Server Approach

Evolution of Implementation

The Challenge of JSON-Based API Design

Simplifying the Approach

Evaluating Function Parameters vs MCP Server Approaches

Managing Workflow Complexity and WET Code

Formulating a Strategy for Pipulate Workflows

Exploring Open WebUI Integration

The Promise of Pyodide and Web Assembly

Focusing on Core Priorities

Establishing Clear Direction and Purpose

Code Consolidation Progress

Defining Chip O’Theseus Implementation Goals

Strategic Todo List Placement

Next Steps for Browser Integration

Q1: Can Microsoft Playwright return both the source HTML and the rendered DOM?

Q2: Can Python’s standard webbrowser package open a URL in a new tab or a new window?

Q3: Can Microsoft Playwright reuse the same browser session across multiple windows/tabs?

The Importance of Browser Automation Research

Distilling Down a Todo List

Dialogue with AI Assistant

Gemini 2.5 Experimential’s Pipulate Todo List

Grok 3’s Pipulate Todo List

1. Current State Assessment

2. Priority Analysis

3. Implementation Path

4. Memory Architecture

5. Recommendations

Do This Next:

Rationale:

Specific Implementation Suggestions:

Estimated Impact vs. Effort:

Potential Pitfalls:

Memory System Implications:

Conclusion

ChatGPT o3-mini-high’s Pipulate Todo List

1. Current State Assessment

2. Priority Analysis

3. Implementation Path

4. Memory Architecture Considerations

5. Recommendations

Do This Next:

Rationale:

Specific Implementation Suggestions:

Estimated Impact vs. Effort:

Potential Pitfalls:

Memory System Implications:

Agent Claude 3.7 Take The Ball And Runs

Gemini 2.5 Experimential’s Pipulate Todo List

Letting Grok Riff

Riff 1: The “Memory Mixtape” Approach

Riff 2: The “Echo Chamber” Feedback Loop

Riff 3: The “Scrapbook” Visual Memory

Riff 4: The “Time Machine” Revert Booster

Riff 5: The “Chip’s Diary” Self-Reflection

The Jam Session Wrap-Up

Avoiding the Agentic Coding Abyss

Gemini 2.5 Experimental’s Take

Q2: Can Python’s standard `webbrowser` package open a URL in a new tab or a new window?