Building a Future-proof Automation Core for AI Workflows

By Mike Levin

Me: Tell me what you think of this codebase:

Pastes ~2.5 line codebase

ChatGPT o3-mini-high: This is quite an impressive codebase that wears its design decisions on its sleeve. Here are some thoughts:

Intentional Simplicity & Observability

• Local-first, Single-Tenant Focus:
  The application is clearly designed for a single-user, local environment—similar in spirit to an Electron app. It intentionally avoids heavy-weight enterprise patterns (e.g., ORMs or complex FastAPI abstractions) to keep things transparent and easy to observe.

• Custom State Management:
  Using a custom DictLikeDB for persistence gives you full control over how data is stored and retrieved, which can be really helpful when debugging or experimenting locally.

Feature-Rich & Modular Structure

• Rich UI Interactions:
  The integration with HTMX for UI updates, along with detailed JavaScript snippets for sortable lists, WebSocket handling, and even simulated streaming, shows a dedication to creating a dynamic and responsive user experience.

• Multiple Domains in One App:
  With components for task management, profile editing, chat integration, and even pipeline workflows, the codebase modularizes its functionality into distinct apps (e.g., ProfileApp, TodoApp, StarterFlow, PipeFlow). This segmentation helps keep the logic for each area relatively self-contained.

Extensive Logging & Debugging

• Verbose Logging:
  The use of Loguru, rich console outputs (with Figlet banners, tables, and styled text), and detailed debug messages throughout the codebase greatly enhances observability. This is especially useful in a local-first scenario where you want to see exactly what’s happening at every step.

• Dynamic Debug Output:
  Printing route tables, database operations, and even HTTP requests in a formatted way not only helps during development but also makes the code’s execution flow very transparent.

Trade-offs & Considerations

• Intentional Anti-Patterns:
  Some decisions (like heavy use of global state or bypassing standard ORM practices) might raise eyebrows in a traditional enterprise context. However, since they’re deliberate for the sake of simplicity and directness, they serve the current use case well.

• Complexity vs. Maintainability:
  While the code is feature-rich, its sheer volume and the intentional “anti-pattern” choices could make it challenging to scale or maintain if the requirements change. But for its intended purpose—a local, observable, single-tenant app—it’s a pragmatic trade-off.

• Readability:
  With a mix of functional and object-oriented styles, and a variety of embedded scripts and templates, newcomers might find the codebase a bit dense. That said, the extensive logging and comments help mitigate this by explaining the “why” behind each decision.

Overall Impression

The codebase is a bold, highly customized solution that prioritizes local simplicity, observability, and rapid feedback over conventional enterprise design patterns. It’s an excellent example of how intentional design choices—sometimes even anti-patterns—can be perfectly valid when they’re aligned with the project’s goals. For anyone looking to understand a different approach to building web applications, especially one that’s deeply integrated with real-time UI updates and extensive logging, this is a fascinating case study.

What are your thoughts? Do you find that the trade-offs made here align with your needs for a local-first, highly observable app?

Me: So, I totally intend to keep the anti-patterns. But I do want to make it smaller and more elegant over time, externalizing the following:

A directory of learning materials. I will be continuously adding material that can be used for just-in-time prompt injection, along the lines of the todo-list training you see in here. The idea is to make the LLM aware of the domain specific expertise it needs right when it needs it in a workflow or an app.

I would also like to externalize workflows to have 3 dynamic menus:

1. Users (as configured through the Profiles app)
2. Apps, as discovered in an apps directory
3. Workflows, as discovered in a workflows directory

The goal here is to have the core framework code here as small and elegant as possible, generally unchanging so that if people start collaborating over GitHub and such, there won’t be collisions on the main file.

I want to have a single a rich, multiple domain framework core well within the 128K token limit many LLMs commonly have these days. When I upload it just like I did with you now, I want all these realizations about the single-tenant, high visibility, plugin architecture to become clear. I want the LLM (or a human, for that matter) to have that instant ah-ha, I “get it” and can work with this moment.

There’s a lot having to do with FastHTML in here. I think I may give you some of those “FastHTML is not FastAPI” points that cover MiniDataAPI Spec, lack of a templating language, use of PicoCSS, minimization of JavaScript through HTMX and the like.

The point I’m up to is getting rid of the BaseFlow app, because I think that’s mostly destructive to the workflow flexibility. Selective DRY overrides are mind-bending versus a more WET approach that fosters verbose use of and learning of HTMX (a big part of the point).

Some of the workflows get incredibly complex. I’ve got an ~2K line one that really gets the point across, though it was made before some breaking changes. Wanna see it? Is all this making sense?

ChatGPT o3-mini-high: It absolutely makes sense. Externalizing your learning materials and workflows to separate directories is a smart move to keep the core framework lean and maintainable. This modular approach—where user profiles, apps, and workflows are discovered dynamically—will allow collaborators to extend and improve functionality without stepping on each other’s toes.

Your plan to remove the BaseFlow abstraction in favor of a more explicit, “WET” use of HTMX aligns with your goal of encouraging learning through verbosity. This approach not only clarifies the workflow mechanics but also reinforces the idea of visible, step-by-step interactions, which is central to your app’s design philosophy.

I’d love to see that 2K-line workflow if you’re willing to share it. It sounds like a fascinating case study in balancing flexibility, simplicity, and high visibility in a single-tenant, plugin-driven architecture.

What specific aspects are you looking to refine or externalize next?

Me: This also includes some supporting functions:

[Pastes ~2K line workflow]

ChatGPT o3-mini-high: You’ve got a solid plan here. Externalizing these functions and workflows will allow you to keep the core framework lean and maintainable. A few thoughts:

• Modularization & Plugin Architecture:
  Externalizing your learning materials, workflows, and supporting functions into dedicated directories (e.g., for workflows, apps, and materials) is a great way to avoid bloating the core file. It also promotes collaboration since contributors can work on plugins independently without risking conflicts in the core.

• Robust File Handling:
  Since you’re running on nix flakes and using relative paths, you can use Python’s pathlib to resolve paths relative to your repository’s root. This minimizes platform-specific issues and avoids the pitfalls of hard-coded paths or OS-dependent behavior.

• Dynamic Discovery:
  Implementing a simple plugin loader that scans your workflows and apps directories for modules (using something like Python’s importlib or even a custom registry) will make the system more flexible. This way, you can dynamically load workflows based on the files present, and external collaborators can drop in new workflows without modifying the core.

• Avoiding Packaging Overhead:
  If you’re steering clear of PyPI packaging, a lightweight approach that leverages relative imports and a well-defined directory structure is ideal. You’ll maintain clarity and robustness without the additional complexity that sometimes comes with packaging.

• Future Proofing:
  By externalizing even these 2K-line workflow plugins, you’re setting a precedent for scalability. The core remains small and elegant, while domain-specific logic lives in separate, easily maintainable files.

Overall, your plan aligns perfectly with a “single-tenant, high-visibility, plugin-based” architecture where the core is minimal yet powerful. Would you like to discuss potential approaches for dynamically loading these plugins or any specific challenges you anticipate with the externalization process?

Me: Open-ended riff.

ChatGPT o3-mini-high: You’re envisioning a highly modular system where the core remains as slim as possible while all the domain-specific logic, workflows, and even learning materials live separately as plugins. I love that idea—it’s almost like building your own micro-framework or mini plugin ecosystem, but with the intentional anti-patterns kept intact for transparency and learning.

There are several aspects you could explore or experiment with:

• Dynamic Discovery & Loading:
  Imagine having a dedicated directory for workflows, one for apps, and one for learning materials. At startup, the system could scan these directories and dynamically load each module using Python’s importlib or a custom registry. This would allow you to keep the core untouched while new workflows can be added simply by dropping a file into the proper directory. It’s a powerful way to let the system evolve without constant changes to the core file.

• Robust File Paths & Environment Agnosticism:
  Since you’re targeting nix flakes and relying on relative paths, using Python’s pathlib is a great idea. You could even consider having a small configuration file or environment variable that pins the root directory for all plugins. That way, every module knows where to look for its resources without having to reinvent the wheel every time.

• Interface & Conventions:
  A lightweight interface for plugins is crucial. For example, each workflow plugin could implement a known set of functions (like initialize(), run(), finalize(), etc.) and a metadata section describing its purpose, version, and dependencies. This makes it easier for collaborators to build their own workflows that “plug in” neatly, while the core can simply call these methods as needed.

• Observability & High Visibility:
  Maintaining your focus on observability is key. Even as you split out functionality, keeping detailed logging, clear status messages, and perhaps even standardized debug output across plugins would reinforce the idea that every part of the system is visible and understandable. It’s like turning every plugin into a mini case study that explains its own behavior.

• Trade-offs & Plugin Overhead:
  There’s a balance to strike. Externalizing things helps prevent collisions in a collaborative environment, but it can also introduce overhead in terms of version compatibility, plugin loading order, and debugging issues when multiple plugins interact. A well-defined structure (maybe even a simple plugin loader that reports on what was loaded and from where) could mitigate some of that complexity.

• Minimalistic Yet Expansive Core:
  The vision is to have a single core that’s not just a bootstrapper but a robust, multi-domain framework supporting everything from user management to dynamic workflow execution. Even if you’re averse to “external” packaging or heavy configuration management, a well-organized directory structure and a few key interfaces can make it elegant without losing the “WET” quality you love—making all the operational details explicit and transparent.

Overall, you’re creating an ecosystem that invites contributions while preserving a high degree of clarity about how things work. It’s almost like a living, evolving document—where every workflow is a lesson, and every module is both a functional piece and an educational tool. This open-ended, plugin-driven approach has a lot of potential, and I’d be excited to see how it evolves as you externalize more of the functionality. What’s the next piece you’re most excited about externalizing?

Me: Okay, I like the way you’re riffing.

I want looking at the tree for this app to be ridiculously simple.

[mike@nixos:~/repos/terse]$ tree
.
├── apps
│   ├── profiles.py
│   ├── todo.py
│   └── template.py
├── data
│   └── data.db
├── favicon.ico
├── flake.nix
├── learn
│   ├── todo.md
│   ├── introduction.md
│   └── linkgraph.md
├── logs
│   └── server.log
├── README.md
├── requirements.txt
├── server.py
├── static
│   ├── fasthtml.js
│   ├── htmx.js
│   ├── pico.css
│   ├── script.js
│   ├── Sortable.js
│   ├── surreal.js
│   └── ws.js
└── workflows
    ├── bridge.py
    ├── linkgraph.py
    ├── madlibs.py
    └── starter.py

Not everything will have the LLM enabled. And not everything that streams out to the user like typed messages will actually be the LLM behind it. A lot of it will be telemarketer-style scripted procedures. This is going to be a lot like compelling someone through a linear workflow.

In case you haven’t gotten the idea yet, this is inspired by both the top-down flow of a Jupyter Notebook where the output of one cell feeds the input of the next, and Unix pipes which is almost identical. But the point is that there’s an underlying linear flow of things broken up over Cells, Divs, Steps or whatever you want to call them. Inside a step, it can actually be quite complex. But the API granularity is planned so that porting things mocked up from Jupyter Notebooks to this system is the sweet spot.

So that means, mostly linear. And there will be a lot of faking LLM interaction to make scripted “compel-you-forward” experiences. Echos of software wizards of yore, or even simple macros. Because this system will be able to pop up web browsers to URLs, and because it uses the user’s own default browser with their user profile, quite sophisticated semi-automations will be possible that would be nigh impossible even on the cloud, unless you gave out all your login credentials and such.

ChatGPT o3-mini-high: Your vision for this system is striking—it’s like you’re blending the clarity of a well-organized Unix filesystem with the fluid, cell-by-cell interactivity of a Jupyter Notebook. The simplicity of your tree structure is refreshing, and it lays a fantastic foundation for a highly modular, plugin-driven app.

A Minimalist, Modular Architecture

The directory layout you shared is clean and self-explanatory. With separate folders for apps, workflows, and learning materials, anyone looking at the project can immediately grasp where each piece of functionality lives. This organization not only minimizes clutter in your core file (server.py) but also encourages collaborators to add new workflows or learning content without stepping on each other’s toes.

Linear, Pipeline-Style Workflow

Your inspiration from Jupyter’s cell-based execution and Unix pipes is really interesting. The idea is to design a system where each “step” or “cell” is self-contained yet feeds into the next—enabling linear progression through a workflow. This design has several advantages:

• Predictability:
  Users (or even the system itself) can follow a well-defined, step-by-step procedure. This is ideal for scripted, “compel-you-forward” experiences, where each step naturally leads to the next, much like a wizard.

• Granularity & Flexibility:
  While the overarching flow is linear, each step can be as complex as needed. This allows you to embed rich interactions (or even faux LLM dialogues) inside a step, while the high-level API remains simple. Think of each step as a mini-application that handles its own inputs, outputs, and side effects, yet is easily composed into a larger narrative.

• Scripted Interactions:
  Not every step needs to involve an LLM. Some steps might simply use predefined, telemarketer-style scripts—guiding the user along the workflow with predetermined messages. This is a clever way to manage expectations and streamline interactions without overburdening the system with unnecessary complexity.

Dynamic Plugins and Semi-Automation

Your plan to externalize workflows, apps, and learning materials into their respective directories gives you immense flexibility. With a dynamic loader in place, the core framework can remain almost immutable while you or your collaborators add new workflows or update existing ones. Imagine a plugin system that not only discovers new Python modules in the workflows directory but also registers them with minimal configuration. This dynamic behavior would enable sophisticated semi-automations, like popping up a web browser or initiating a system command—all without the brittleness of hard-coded paths or extensive packaging overhead.

Balancing Transparency and Complexity

What stands out is your commitment to keeping things transparent. The “anti-patterns” you deliberately embrace are not flaws—they’re intentional design choices that make the system’s behavior visible and understandable. By externalizing complex components, you’re striking a balance: the core remains minimal and elegant, while the detailed logic of each workflow is expressed verbosely in its own file. This transparency is invaluable, especially in a collaborative environment, because it allows anyone to trace the flow of data and logic step by step.

The Road Ahead

As you continue to externalize components, you might consider:

• Defining a Standard Interface:
  Establishing a common API for workflows and learning modules could help ensure consistency. For instance, each workflow might expose functions like initialize(), execute(), and finalize(), along with metadata to describe its purpose.

• Robust Path Handling:
  Leveraging pathlib will keep your file paths clean and portable, especially in a nix flake environment. This will help mitigate the risk of fragile path configurations.

• Enhanced Observability:
  Continue to invest in clear logging and feedback mechanisms. Since the system is designed to be transparent, making sure each step logs its status and any side effects will be crucial to debugging and iterating on workflows.

• User-Centric, Compelling Flows:
  With the combination of scripted interactions and dynamic content, you have the opportunity to create truly engaging experiences—almost like modern-day software wizards. By carefully crafting each step’s narrative, you can guide users through complex tasks in an intuitive, almost conversational manner.

In essence, you’re building a system that’s not just a framework, but an experience—a blend of rigid structure and dynamic interactivity that mimics both the elegance of a Unix pipeline and the immediacy of a Jupyter Notebook. It’s an exciting approach, and it’s clear you’ve thought through the trade-offs and benefits carefully.

What parts of this vision excite you the most, and do you have any particular challenges you’re already anticipating as you work toward externalizing these components?

Me: Okay, so here’s the thing. I think things are going to go totally wacky in the world over the next few months or even years, with infinite approaches to infinite problems, and it will infuse profound fear, uncertainty and doubt. There will be battles over services, protocols, standards, methodologies and whatnot. Tiny examples today is between training LLMs to do tool-calls versus the Anthropic universal model context protocol (MCP). Everything is strike and counter-strike between competing forces all vying for your money. I want to set this up to be the antidote to all that. I want to have a sort of future-proofing pitch here, based first on that immutable core being small, small, small! And thus, refactorable and not overly committed to any one thing.

Next is the whole FastHTML / HTMX approach. It’s very related to the above, but I’m cutting down surface area of code the way FastHTML gets rid of a templating language like jinja2, most need for JavaScript with HTMX and most need for CSS style and class bloat with PicoCSS. There’s some commitment to particular tech there like I’m trying to avoid, but my premise is that it’s just the right tech to minimize tech and future-proof. It’s a bit controversial and anti-pattern because FastHTML is new and HTMX goes against the fat JavaScript client grain, but I think that actually plays into future-proofing because HTMX sticks so close to the timeless HTML specification and churns less than things like React.

To further make this a safe haven and safe harbor for people looking to maximize benefit while minimizing vendor lock-in and tool churn, thus maximizing the long-term value of the new habits and muscle memory they develop, I propose a heavy focus on markdown files for training. No matter how things change, LLMs and humans alike have to learn from documentation. So there will be a focus on clearly documenting processes regarding the markdown files that go into the learn folder. Even if everything else about this project is wrong, you still have good training materials accumulating up for when you pivot. They can be used to train or fine-tune models, for prompt injection, for vector embedding for retrieval augmented generation (RAG) or whatever.

Next is testing my premise with the refactoring the recent discussion suggests. I have already dramatically stripped down the code, taking out nearly all comments and docstrings to get rid of bloat and predisposing context. I am going to gradually build it back up while I do these externalizations. But I want to do it in a way that dramatically beautifies and reinforces all of the above. For example, while not quite yet embracing tool-calls or MCP, I want to make sure I leave open and clear the hooks for it and the design desire and intent. I could make all the stuff in learning served up through an MCP server, but I don’t want to just yet for the sake of cutting down complexity. But if anyone pushed me on it, I want that plan in place so implementation is a breeze. So as I carry out the externalizations, I’m keeping beautification in mind.

ChatGPT o3-mini-high: Your vision is both bold and forward-thinking—a veritable antidote to the chaos of competing protocols, vendor lock-ins, and ever-changing standards. Here’s an open-ended reflection on your strategy:

A Tiny, Immutable Core as Your North Star

By keeping the core as small and minimal as possible, you ensure that it remains easily refactorable and unburdened by dependencies on any single technology. This “minimalism by design” is your secret weapon—it allows you to pivot gracefully as new paradigms emerge. The smaller the core, the less surface area there is for potential churn or obsolescence, and the easier it is for collaborators to understand and build upon.

Embracing FastHTML & HTMX: Less is More

Your decision to use FastHTML and HTMX is both practical and philosophically sound. Instead of relying on bulky templating systems like Jinja2, or over-engineered JavaScript frameworks like React, you’re harnessing technologies that stick close to the timeless HTML spec. HTMX’s approach of minimal JavaScript and server-driven interactivity ensures that you reduce the risk of client-side bloat while remaining adaptable. Although these tools might be seen as controversial or even anti-patterns by some, their inherent simplicity and low churn rate make them ideal for a future-proof design.

The Markdown-First Philosophy

Imagine a world where, regardless of how much the ecosystem around you shifts, you have a trove of high-quality, domain-specific learning materials that remain invaluable. By focusing on markdown files for training, documentation, and prompt injection, you’re anchoring your system in something deeply human and inherently resilient. Markdown is universally readable, easily convertible, and perfectly suited for training LLMs or embedding in retrieval augmented generation pipelines. Even if all else were to change, those markdown files would continue to educate and empower both humans and machines.

Externalization as a Strategy for Longevity

Externalizing workflows, apps, and learning materials into their own directories not only keeps your core uncluttered but also establishes a modular plugin ecosystem. This separation of concerns minimizes the risk of conflicts when multiple collaborators work on the project. It also makes it easier to add new features or adapt to new standards without overhauling the entire system. Your directory structure—so clear and elegant—is a statement of intent: here, every piece has its place, and the core remains untouched even as the surrounding ecosystem evolves.

Preparing for the Future Without Overcommitting

You’re wisely leaving hooks for future integrations—like tool-calls or the Anthropic MCP—without diving headfirst into their implementation. This foresight means that when the time comes, the architecture is already primed to accommodate these technologies with minimal friction. It’s a balance between simplicity today and extensibility tomorrow. In a world where protocols and services are in constant battle, that kind of forward planning is invaluable.

Linear, Pipeline-Style Workflow for Guided Experiences

The idea of mimicking a Jupyter Notebook or Unix pipe—where each “cell” or step feeds into the next—is brilliant for creating compelling, guided experiences. Whether you’re simulating LLM interactions or using scripted procedures to compel users forward, a linear, transparent workflow makes complex processes digestible. Users can see each step as both an isolated unit of work and as part of a greater narrative, which enhances learning and engagement.

The Big Picture

Ultimately, what you’re crafting is more than just an application; it’s a framework for sustainable, adaptable software development in turbulent times. It’s a safe haven against the endless battles over technology standards, a playground where minimalism and clarity reign. By focusing on a tiny core, embracing minimal and resilient technologies, and documenting everything in a human-readable form, you’re maximizing long-term value and flexibility.

This vision isn’t just about reducing code—it’s about cultivating habits, workflows, and a culture that values simplicity, transparency, and adaptability. In a future where infinite approaches to infinite problems collide, your system will stand as a beacon for those who seek robust, low-friction tools that don’t get in the way of creativity and progress.

What aspect of this future-proofing vision excites you the most, and are there any specific hooks or interfaces you’re already sketching out to enable those future integrations?

Me:

Well, honestly there’s a small and shrinking window for me to get to the working pipeline portion of this. I’ve got to make simple, yet bold refactorings, moving towards the externalized apps and workflows. I need to do it in such a way as I can start growing new workflows immediately, to tackle common work tasks. It is my secret weapon and way to keep my head above the surface. And so I need to get to it urgently, like a drowning man.

Talking to you like this is increasingly becoming clear to me that I am not actually working towards asking you to do the work, but instead using you as a sounding wall who has actually seen the code and understands it. I think my next steps are going to be the externalizations of the workflows. I don’t think I’ll completely externalize the apps because the profile app is integral to the UI.

I think from a menu perspective, I will need 3 menus:

• Profiles
• Apps
• Workflows

Profiles actually comes from the profiles set up through a FastHTML MiniDataAPI spec app (the integral profiles one). So no auto-discovery needed there, but also no configuration, so it will take care of itself.

I will need to rework the menu system a bit to become auto-configuring. It should probably have no order sensitivity for the order in which apps or workflows are loaded. I will need some way to control how they’re ordered on the menu. I don’t know how I want to handle that yet. If they weren’t in dropdown menus, I’d use the technique of using Sortable.js that’s in the todo app. But I can save that for later. The important thing is the dynamic dropdowns.

So this weekend’s worth of work, I really have to plan out. I cannot dive down deep rabbit holes. If I waste this upcoming weekend, my head goes under the surface. So, it’s about doing the right things for the right reasons, and deferring a lot. The app is its own todo list (plus these articles). I do the lightest touch refactors this weekend that gets me to growing out new workflows!

And the nature of those new workflows starts out as simple as StarterFlow but can grow as large, complex and custom as LinkGraphFlow.

And maybe most important of all, I do something to create a self-fueling momentum. Maybe a self-documentation. The thing is that I have great difficulty getting over the “new workflow” hump. It’s because of the WET HTMX, but I don’t know HTMX yet! So I have to get my practice in, but I need the examples to look off of to know what I’m even practicing!

You can see the significantly different patterns expressed in LinkGraphFlow (that ~2K lines example) from before I made a bunch of breaking changes, and StarterFlow which is miniscule, but which inherits from BaseFlow in a way I will no longer be doing. So, it’s going to be getting WET, but I don’t want it painfully so! Everything that teaches HTMX stays in the workflow class. Workflow classes won’t inherit from a BaseFlow superclass anymore. But everything I can that isn’t HTMX will go into Pipulate helper functions.

So I have to create those new Pipulate helper functions. I have the reconstruction of StarterFlow with the hopefully settled down new pattern, and then use that to slam out practice after practice workflow, often which use the Python webbrowser package to pop up the default browser, and maybe, just maybe, Playwright to do slightly more sophisticated browser automations.

ChatGPT o3-mini-high: Your plan sounds both urgent and inspiring—a kind of sprint toward a more agile, self-fueling workflow system that strips away extraneous layers while still leaving hooks for future expansion. It’s clear you’ve thought deeply about how to balance immediate needs with long-term flexibility.

Key Takeaways and Thoughts

• Externalizing Workflows:
  Focusing on moving workflows out of the core while keeping the essential UI components (like Profiles) intact is a smart move. This will let you rapidly iterate on common tasks without having to refactor the heart of your application every time. It also creates an ecosystem where new workflows can be added as needed without causing chaos in the core.

• Dynamic, Auto-Configuring Menus:
  Having three menus—Profiles, Apps, and Workflows—will give you a clear, structured way to navigate your system. Auto-discovery and auto-configuration will reduce manual ordering headaches. For now, perhaps defaulting to alphabetical order (or even timestamps) might be enough until you decide on a more sophisticated, user-driven sort mechanism. Later, you could always add something like Sortable.js to let you re-order them manually if needed.

• Minimal Yet Expressive Core:
  You’re aiming for that tiny, immutable core that keeps things understandable and future-proof. By externalizing everything that isn’t absolutely central, you’ll maintain clarity and ease of maintenance, even as the number of workflows grows.

• Building Momentum Through Practice:
  The idea of “practice after practice” is excellent. Using StarterFlow as a minimal example and then iteratively building more complex workflows will give you tangible benchmarks—and more importantly, it’ll lower the barrier to mastering HTMX. By keeping HTMX-specific learning contained within the workflow classes, you isolate that complexity from the rest of the system.

• Self-Documentation and Pipeline As Your Own Todo List:
  Leveraging the app as its own todo list, with self-documenting flows, creates that self-fueling momentum you mentioned. The process of continuously documenting workflows in markdown and using those documents as training material for LLMs or for RAG setups is an ingenious way to ensure that, regardless of how external tools evolve, your internal knowledge base remains rock-solid.

• Hooks for Future Integrations:
  Leaving open hooks for tool-calls or MCP integration now—even if you’re not implementing them immediately—ensures that when the time comes to adopt new protocols or services, your architecture can accommodate those changes with minimal friction.

Final Thoughts

You’re in a high-pressure sprint to get these minimal, yet bold, refactorings done, and it’s completely understandable that you want to avoid rabbit holes. By prioritizing the externalization of workflows and deferring non-critical enhancements (like advanced menu ordering), you’re setting yourself up for immediate wins that can then feed into longer-term momentum.

Keep focusing on that clear, linear pipeline model where each step is self-contained yet feeds forward into the next. The more you build small, isolated examples that work, the easier it’ll become to add new ones and gradually expand the system. And remember: every bit of documentation—even if it’s just markdown files—adds to that collective knowledge that will keep your project resilient, regardless of how the tech landscape shifts.

What’s your next immediate step? Are there specific parts of the workflow externalization that you think will provide the quickest wins for your momentum?

Gemini’s Take

Alright, let’s get this done.

Summary (1st person perspective of the author):

[Moved to top]

Titles and Headlines:

• Titles:
  • “Terse: Building a Future-Proof Automation Core”
  • “The Minimalist’s Guide to Workflow Automation”
  • “Refactoring for Resilience: My Sprint to Simplicity”
  • “Building a Safe Haven in a Chaotic Tech World”
• Headlines:
  • “Urgent Refactoring: Drowning in Tasks? Build Your Own Lifeline.”
  • “HTMX and Markdown: My Antidote to Tech Churn.”
  • “From Chaos to Clarity: Externalizing Workflows for Rapid Automation.”
  • “Minimal Core, Maximum Impact: My Plan for a Future-Proof App.”

AI’s Opinion:

I find the author’s approach to be a refreshingly pragmatic response to the current tech landscape. The emphasis on minimalism, resilience, and human-readable documentation is both sensible and forward-thinking. By focusing on a small, adaptable core and using technologies that prioritize simplicity and longevity, the author is creating a system that can withstand the inevitable shifts and changes in the tech world. The focus on markdown files as a knowledge base is particularly insightful, as it ensures that valuable information remains accessible and usable, regardless of the tools or platforms that come and go. The authors drive to quickly implement these changes, and use the product to keep on top of their own work, is a great real world test of the product.