Pipeline Dreams: Finding Focus and Flow in the Age of AI

By Mike Levin

The Power of Pipelines

The whole point of the pipeline is to focus. It promotes linear procedures and prescribed workflows. We bottle-up best-practices and grab you by the scruff of the neck and lead you along the righteous path to carry them out, no muss no fuss, no decision fatigue, no cognitive overload or paralysis through analysis. Just Step 1, Step 2, Step 3… Continue until done.

Switching Contexts

But now I’m back in service the client mode, and I’m a bit shaky. What’s my linear process here?

Go look at your calender to see what your timeframe is.

https://calendar.google.com/calendar/

I’ve got a good 2 solid hours between now and the meeting, but I opted out of an optional meeting to clear that block. My weakness is agendas. No, my weakness is getting out of that system-building developer mode and back into client servicing mode. It’s only Tuesday in the first full week after New Years, so a lot of other people are going to be in this same mode. But don’t rely on that. Push things forward. Create a great sense of progress from meeting to meeting. Create things that the account management people on our side and the stakeholders on the client side can run up the flagpole and show as meaningful accomplishments, or at least continuous forward progress.

Balancing Multiple Roles

And so the pipeline is just in the back of my mind as I switch hats to being the type of domain expert whose domain expertise is deconstructed and reconstructed into those pipeline workflow processes.

Because they’re designed to be easy peasy and musical to compose, it will get into my fingers over time, so long as I actually engage and practice as I go, without interfering with the actual work. Or is that correct? Is that part of putting my domain expertise hat on, or is that unnecessarily mixing concerns? Ugh, that’s the vibrating edge I talked about in the previous article. That’s my burden to bear: doing both!

My Workspace Organization

And now for the commitment and consistency piece. Articles like this one I’m writing right now starts out on Screen 1 of my 7-screen configuration with Screen 7 being the Daily Tech Journal where stuff ends up public. I have the freeform journal “to the left” on a 7 virtual desktop array, the public tech journal “to the right” and the Client Notes in the middle.

• Freeform Journal, Screen 1
• [work]
• [work]
• Client Notes, Screen 4
• [work]
• [work]
• Tech Journal (this), Screen 7

The Writing Process

Things start out in the freeform journal so I feel uninhibited and can dredge up whatever needs dredging. But then I let the process guide me, and I get into different zones or flow-states, depending. In this case I am deliberately guiding myself to get into the Drucker/Deming flow state. The mission of any company or organization is to get and keep customers, and you do that through an ongoing process of total quality management from the trenches up.

Managing Personal Tendencies

And I’m certainly an in-the-trenches sort of guy. The fact that I hate the management side of this so much is one of the total quality management (TQM) things about myself I have to manage. I’d rather be tweaking bits all day and become the sort of architecture astronaut Joel Spolsky warns about. That is my predisposition, so I’ve got to use this journal to continuously rein myself back in and stay on rails — those pipeline rails!

The Power of Public Commitment

And that gets to the Robert Cialdini principle of (public) commitment and consistency. There’s no surer way than to sigh a petition you know your neighbors are going to see your name on to psychologically life-hack yourself into staying consistent with that publicly expressed intent. And so we move this very entry over to the public side. Working article name? Staying on track!

Okay, I’m working over there now. But it doesn’t get publicly published until I commit the file to the repo and push it. So I still have a degree of inhibition until then. So where was I? A pipeline workflow process for getting into the right mindset to be a high-quality client work producing machine of a human being? Don’t just satisfy the client’s need. Visualize and target being some sort of inhuman at-one-with-Botify-software robot, exceeding what even the existing gurus can do.

Lofty goals, but start humble. Where was I? Oh yeah, the linear process!

• Bring up calender, know time-window https://calendar.google.com/calendar/
• Organize browser bookmarks how they should show during call (no leaks)
  • This is the story-telling as you go left-to-right on the browser
  • The general story goes:
    • Agenda
    • Slides Deck (if applicable)
    • GSC (source of truth, RealKeywords cross-validation brand vs. non-brand)
    • Botify Tabs
    • Client page open (truly look at/use site)
• Display client’s bookmark folder (no other) with Agenda link at top
• Open Agenda and start making updates (starting doing becomes done) MAKE TAB 1
• Make GSC the next bookmark. Pull it up. Get “from the horse’s mouth” view
• Poke around GSC. Go to 16-mo view. Keep seasonality in mind. MAKE TAB 2
• Go to Overviews for each:
  • Action Board, Review priorities & correlate with PageWorkers
  • Log Analyzer (if applicable) Crawls / Unique URLs / Cumulated / 6 Months
  • Site Crawler
  • Real Keywords (if applicable)
  • PageWorkers (if applicable)
    • What was recently deployed / effect?
    • What is the next optimization up? Stage
• Make a Google Slide Deck and find something noteworthy from each
  • GSC
  • Action Board
  • Log Analyzer
  • Site Crawler
  • Real Keywords
  • PageWorkers
• Go through each item, get the holistic view
• Find items worthy of Google Slide deck pages
  • Screenshot and make deck slides when applicable
  • Get to know, craft, internalize the story
    • Is there an evolving story from last sessions?
    • Are there noteworthy new things?
• Clean up tabs
  • Close the clutter
  • Reset existing tabs to good start-points for call
  • Order them to reflect the story-telling

Preparing for Client Meetings

Okay, this is really good. My weakness is agency small-talk. I like when I have account management people on this to take care of humanizing things a bit. It’s fine for me to be me. I do rub some people the wrong way I know, so really always be concessionary. Always step back. Try to under-speak but be over-prepared.

Managing Browser State

Okay that state with open tabs in the browser is pretty precious. Check Chrome features: yep! There’s bookmark all tabs. Use it. Done! Haha, the meeting was just moved to next week. Okay, I’m over-prepared on this one. Onto the next and see if the same template applies.

Funny thing, I can now close all those tabs freely without any fear of loss of that special state of being prepared for the meeting by virtue of the open tabs. There’s something immensely important there to capture.

Linear Workflows and Storytelling

Okay, so this article is about staying on track. It’s about making that track linear. It’s about grabbing some one by the scruff of the neck… there ought to be a better expression… take control of the situation, decisively guide, steer firmly… yeah, okay. I like steer firmly.

These are workflows that firmly steer the user along linear paths. And there’s a lot of stuff that has to do with popping up the web browser given data filled into a form… namely, a list!

Client Call Reflections

Okay, yeah. My client calls are done for the day. The first big one that I lead was cancelled and the second one where I’m just a contributing voice went really well. But I had some good realizations.

Every Client call being a show as it is, has a set of tabs that should be opened left-to-right that tell the story of the call, so that it’s rigged for success before you even begin screen-sharing and so that there’s no fumbling around during the call for that particular link you need.

Pipeline System Implementation

And this is a perfect application for the pipeline system.

I am thinking ahead to implementing it based on the outline above. At some point, there’s going to be an agenda. But I am going to be sorely tempted to allow going back in the pipeline to change values without wanting to erase everything that comes after it. However, that’s not how these are designed, and I am going to resist that. The agenda should be a final output item.

Key Design Considerations

This can start out much like the LinkGraphFlow, where you put in a project URL. But I don’t want to hit those primary key issues! Ugh, okay think! 80/20-rule! Key systems that simulate composite keys using a single field? Conventional patterns that get used per-workflow whose rules can live in the workflow itself and be part of the music?

Client-YYYYMMDD slug? Forget about the project URL as a key. That can be a subsequent card. Hmmm, I’m tempted to change the key field from url to key or id, but that’s potentially a rabbit hole and could be very difficult to do without introducing bugs. But I would love that to be part of the 80/20-rule solution. It would make the simulated composite key per workflow feel that much better. But the user never actually sees the URL field, and URL does stand for uniform resource locator, which it technically is!

Okay, see how lightly you can implement… hmmm… why even have date slugs? Why not just put an incremented digit with the client name… well, because you want to be able to pull it up easily. But these are not permanent things. These pipeline workflows are designed to be temporary, with only their side effects persisting. But their use is part of the show in front of the client. When they see it in use, what should it look like?

Giving Fresh o1 Pro The Codebase

o1 Pro: Below is a concise review of how this code aligns with the FastHTML + HTMX + MiniDataAPI local-first principles laid out in your .cursorrules file, followed by a few recommended tweaks.

1. Single-Tenant Local-First

• The code embraces local data storage (MiniDataAPI tables: tasks, clients, pipeline, etc.) and in-memory or file-based state (store), matching the single-tenant spirit.
• No external session managers or enterprise auth—just a simple db and a straightforward approach for storing user selections (last_profile_id, etc.).
• The entire system is structured for a single user or small single-tenant environment.

Recommendation: Keep the local file-based DB usage exactly as-is. It’s well-aligned with the local-first pattern.

2. FastHTML + HTMX Patterns

• Route definitions use @rt(...) or @app.route(...) for returning HTMX-friendly HTML partials (Div, Card, Form, etc.).
• HTMX incremental updates: hx_get, hx_post, and hx_swap appear in forms and anchors, returning partial UI.
• Large multi-step flows (e.g., BridgeFlow, StarterFlow, OneCardFlow) demonstrate the recommended placeholder chain technique: each step returns a Div(id="step_X") with hx_trigger="load" to auto-fetch the next step.
• Revert controls are used to let the user “jump back” via minimal HTMX forms. This matches the clear-forward-on-submit principle.

Recommendation:

• The step flows strictly call clear_steps_from(pipeline_id, current_step, self.STEPS) before saving new data. This is perfect. Continue to always do that in every step’s *_submit() route.

3. MiniDataAPI Usage

• You correctly define tables at startup: (tasks, Task) = db.create(...) and rely on insert, update, delete, or () for listing.
• No ORM or Pydantic overhead—only simple typed fields.
• For record filtering, you use .xtra(profile_id=...) and order_by='priority'.
• The pipeline storage is also a single record in the pipeline table with a JSON blob. That is the “One record to track the entire workflow” approach recommended in the guidelines.

Recommendation:

• If you foresee more advanced queries, you can chain multiple .xtra(...) calls (like xtra() with different fields). But keep the spirit minimal—no multi-join or big filters.

4. Server-Driven UI

• All complex flows (like link_graph_flow) return server-rendered HTML fragments.
• No large JS frameworks—just HTMX for partial updates and SSE for streaming.
• This keeps the client side minimal and fully “server-driven.”

Recommendation:

• The SSE example in StreamSimulator is good. If more advanced streaming is needed, keep the same pattern of yield f"data: ...". Don’t layer extra client-side libraries.

5. Multi-Step Pipeline Patterns

• Classes like BridgeFlow, OneCardFlow, and StarterFlow illustrate the recommended pattern:
  1. A landing() method sets the pipeline_id
  2. Each step checks whether data exists -> “completed” or “input form.”
  3. Submits call clear_steps_from(...), then store new step data, then show next step.
• They handle finalization by checking for "finalized" in the last step’s JSON. This matches the “finalization lock” pattern exactly.

Recommendation:

• Each flow clearly demonstrates the difference between ephemeral steps (always blank form) and preserve steps (prefill with old data). This is correct usage.
• Keep each step short, returning a minimal Div(...).

6. Simplicity & Anti-Patterns

• No signs of big classes or ORMs.
• No large front-end frameworks.
• Code is obviously “Localhost software—server-side state.”

Possible Tweaks:

1. Module Organization: If the single-file approach grows too large, consider splitting each flow into its own file (e.g. bridge_flow.py, one_card_flow.py) while still returning FT components. It’s not mandatory—just a code clarity option.
2. Testing: Adding some small route-level tests or flows that confirm steps are cleared properly can further ensure no “leaked” state.

Summary

Overall, your code aligns very well with the FastHTML + HTMX + MiniDataAPI local-first approach:

• Single-file or single-tenant usage: Good.
• HTMX partials: Correct usage of hx-get, hx-post, placeholders, and Div(id=...).
• MiniDataAPI: Minimal overhead, direct table usage.
• Multi-step pipeline: Perfect example of “clear forward on submit,” “revert control,” and finalization states.

Keep it up—the system demonstrates the recommended patterns from .cursorrules almost exactly. The main improvements are organizational: if it keeps growing, break it into smaller modules for clarity, but maintain the same local, minimal approach.

Asking o1 Pro to Riff

Me: Riff

o1 Pro: Imagine you’re seated at the terminal, keyboard at your fingertips, the entire local environment humming. Here comes the riff:

• We spin up mini pipelines where each step is just another DIV.
• We chain them with HTMX like the world’s simplest jam session, no front-end library hogging the show.
• Our data? A single table record stuffed with JSON—like a scribbled lead sheet: each chord progression (step data) waiting to be played.
• If the user wants a quick revert? We hand them a minimal form, no drama—just a neat little clear_steps_from(...) call, scrubbing future steps and setting the stage for a new solo.
• And in the end, everything goes back to that local, single-tenant vibe: no swirling chaos of multi-user complexity or hidden runtime surprises.

All local, all minimal, all in tune—like a jam band that respects the hush. That’s the FastHTML + HTMX groove: short, sweet, and improvised in perfect pipeline harmony.

Pipeline Instantiation

Ah, so you can see that spinning up a new pipeline is a thing, then?! That was immediately obvious to you, good! And there’s 2 notions of spinning up a new pipeline. One is designing a new one by say copy/pasting StarterFlow with a new class name, which is my intent now. And the other way is for the user who is not a developer at all, which is a huge part of the point, to actually spin up a new instance of using a pipeline. So there’s designing new pipeline classes, we call workflows and by convention end with Flow in CamelCaseFlow. And then there’s using one of these workflows by hitting the endpoint, which by convention instantiates a snake_case_flow factory instance of the CamelCaseFlow class. And then we link it into the menu and layout, and it’s live and non-developers can start using it as a sort of Jupyter Notebook on rails. Easy peasy, simple as that.

Single File Philosophy

Now comes the tricky part, creating those new classes as a developer. You already noted several times the one key thing, which is how unwieldy one long file could potentially get. But not really. Not to me. I’m a vim/NeoVim user so hopping around a text file millions of lines long if it’s tagged well for jumping around with search is infinitely preferable to me than directory diving and having to use fuzzy search features to figure out what’s where. What’s further, I love the idea of just copy/pasting the whole friggin framework into one of you super-big-token-window models so there’s never any question of whether you’re looking at the whole thing. You always are! (but for external libraries, which you’re probably trained on anyway).

Building GapAnalysisFlow

So what I want to do is to make GapAnalysisFlow, and start building it up a little at a time. First, I want to use a textarea field just like OneCardFlow, but as the first step of a copy of StarterFlow. Now the thing is this is not our first rodeo, and you drop features left and right when you try to synthesize something new from old. For example, you will almost always drop those things you think are cosmetic and which are unique affectations of the system, like the 2 different style emoji keys. You also drop things like the clearing-forward the overall pipeline state when persistence is being kept for a single field when you revert to that step. We are going against a lot of patterns you have been trained on, and you try to be overly helpful reverting to those patterns and regressing functionality instead of sticking to near-perfect abiding by the conventions that are in place. This is the reason for much of what you find in the .cursorrules file.

Implementation Request

So my request to you is to take this code of OneCardFlow:

[pasted]

…and to transpose the persistent textarea behavior into a new class called GapAnalysisFlow while staying meticulously and pedantically close to StarterFlow in all regards except for those that are required to turn card 1 into a textarea with persistence exactly as in OneCardFlow, except that in this case there will be another card (the existing card 2 from StarterFlow) coming after it. Here is StarterFlow:

[pasted]

I wish for it to be copy/paste-ready into the framework. I will proactively give you an example of where you are going to go wrong. You are going to try to insert a request object into the new landing method, but I’m going to tell you to look at the current implementations and how the landing method very deliberately leaves it out for maximum ease in linking it into the page layout. I encourage you to keep this lesson in mind when you are tempted to carry out other divergences from the StarterFlow reference specification.

Please and thank you!

o1 Pro Does Its Best 1st Try Implementation Yet

o1 Pro: Below is a copy/paste-ready GapAnalysisFlow class that follows StarterFlow conventions (two cards plus a final step) while making Card 1 a persistent textarea (like OneCardFlow). The second card (step_02) remains ephemeral, just like StarterFlow’s second card. Only the minimal necessary changes have been made:

class GapAnalysisFlow:
    """
    Two-card pipeline with a final step, nearly identical to StarterFlow.
    Differences:
      - Card 1 uses a persistent textarea (like OneCardFlow) storing "text".
      - Card 2 remains ephemeral, storing "mykey2".
      - The rest follows StarterFlow's structure and route definitions.

    PIPELINE CORE PRINCIPLES:
    1. Clear forward on submit
    2. Preserve affects UI only
    3. Submit handlers are state reset points
    4. State flows forward only
    5. Use pipulate helpers
    """

    def __init__(self, app, pipulate, prefix="/gapanalysis", llm_enabled=False):
        self.app = app
        self.pipulate = pipulate
        self.prefix = prefix
        self.llm_enabled = llm_enabled

        # We have 2 step keys: "text" (Card 1) and "mykey2" (Card 2),
        # then a final step for "finalized".
        self.STEPS = [
            ("text",      "step_01",   "Card 1"),   # Persistent textarea
            ("mykey2",    "step_02",   "Card 2"),   # Ephemeral
            ("finalized", "finalize",  "Finalize")
        ]

        # For user-facing messages, we lightly adapt from StarterFlow
        self.STEP_MESSAGES = {
            "new": "Step 1: Enter text for Card 1 (persistent).",
            "step_01": {
                "input": "Step 1: Type something in the persistent textarea.",
                "complete": "Step 1 Done. You entered '{}'. Now type something for Card 2."
            },
            "step_02": {
                "input": "Step 2: Type something ephemeral for Card 2.",
                "complete": "Step 2 Done. You entered '{}'. Click Finalize."
            },
            "finalize": {
                "ready": "Lock in your workflow or revert to any step.",
                "complete": "Workflow finalized. Use Unfinalize to make changes, or enter a new ID.",
                "unlocked": "Click a step to revert it."
            }
        }

        # Route definitions closely follow StarterFlow
        routes = [
            (f"{prefix}",                 self.landing),
            (f"{prefix}/init",            self.init, ["POST"]),
            (f"{prefix}/step_01",         self.step_01),
            (f"{prefix}/step_01_submit",  self.step_01_submit, ["POST"]),
            (f"{prefix}/step_02",         self.step_02),
            (f"{prefix}/step_02_submit",  self.step_02_submit, ["POST"]),
            (f"{prefix}/finalize",        self.finalize),
            (f"{prefix}/finalize_submit", self.finalize_submit, ["POST"]),
            (f"{prefix}/unfinalize",      self.unfinalize, ["POST"]),
            (f"{prefix}/jump_to_step",    self.jump_to_step, ["POST"])
        ]
        for path, handler, *methods in routes:
            method_list = methods[0] if methods else ["GET"]
            self.app.route(path, methods=method_list)(handler)

    # ---------------------------------------------------------------------
    # STATE MESSAGES
    # (Same pattern as StarterFlow, but references "text" for step_01)
    # ---------------------------------------------------------------------
    async def get_state_message(self, pipeline_id: str) -> str:
        state = self.pipulate.get_state(pipeline_id)
        logger.debug(f"\nDEBUG [{pipeline_id}] State Check:")
        logger.debug(json.dumps(state, indent=2))

        # Check finalize first
        if "finalize" in state:
            if "finalized" in state["finalize"]:
                msg = self.STEP_MESSAGES["finalize"]["complete"]
                logger.debug(f"State: finalized, Message: {msg}")
                return msg
            elif state.get("step_02", {}).get("mykey2"):
                msg = self.STEP_MESSAGES["finalize"]["ready"]
                logger.debug(f"State: ready to finalize, Message: {msg}")
                return msg

        # Check step_02
        if "step_02" in state:
            val2 = state["step_02"].get("mykey2")
            if val2:
                msg = self.STEP_MESSAGES["step_02"]["complete"].format(val2)
                logger.debug(f"State: step_02 complete ({val2}), Message: {msg}")
                return msg
            msg = self.STEP_MESSAGES["step_02"]["input"]
            logger.debug(f"State: step_02 input needed, Message: {msg}")
            return msg

        # Check step_01 (now storing "text")
        if "step_01" in state:
            val1 = state["step_01"].get("text")
            if val1:
                msg = self.STEP_MESSAGES["step_01"]["complete"].format(val1)
                logger.debug(f"State: step_01 complete ({val1}), Message: {msg}")
                return msg
            msg = self.STEP_MESSAGES["step_01"]["input"]
            logger.debug(f"State: step_01 input needed, Message: {msg}")
            return msg

        # If none found, it's new
        msg = self.STEP_MESSAGES["new"]
        logger.debug(f"State: new pipeline, Message: {msg}")
        return msg

    # ---------------------------------------------------------------------
    # LANDING (No request param => keep inline with StarterFlow)
    # ---------------------------------------------------------------------
    async def landing(self):
        """
        GET /gapanalysis
        Quick landing page to set or resume pipeline_id, calls /init to start flow.
        """
        # Gather existing pipeline IDs for a datalist
        pipeline.xtra(endpoint=self.prefix.lstrip('/'))
        existing_ids = [record.url for record in pipeline()]

        # Possibly schedule a greeting
        async def delayed_greeting():
            await asyncio.sleep(2)
            await chat.simulated_stream("Enter an ID to begin.<br>\n")
        asyncio.create_task(delayed_greeting())

        return Container(
            Card(
                H2("GapAnalysisFlow: Two Cards + Finalize"),
                P("Card 1 is a persistent textarea; Card 2 is ephemeral."),
                Form(
                    self.pipulate.wrap_with_inline_button(
                        Input(
                            type="text",
                            name="pipeline_id",
                            placeholder="🗝 Old or existing ID here",
                            required=True,
                            autofocus=True,
                            list="gapanalysis-ids"
                        ),
                        button_label="Start GapAnalysisFlow 🔑",
                        button_class="secondary"
                    ),
                    Datalist(
                        *[Option(value=pid) for pid in existing_ids],
                        id="gapanalysis-ids"
                    ),
                    hx_post=f"{self.prefix}/init",
                    hx_target="#gapanalysis-container"
                )
            ),
            Div(id="gapanalysis-container")
        )

    # ---------------------------------------------------------------------
    # INIT
    # ---------------------------------------------------------------------
    async def init(self, request):
        """
        POST /gapanalysis/init
        - Sets pipeline_id
        - initialize_if_missing
        - Returns placeholders for step_01..finalize
        """
        form = await request.form()
        pipeline_id = form.get("pipeline_id", "untitled")
        db["pipeline_id"] = pipeline_id

        state = self.pipulate.initialize_if_missing(
            pipeline_id,
            {"endpoint": self.prefix.lstrip('/')}
        )
        if state is None:  # Pipeline initialization failed
            return Card(
                H3("ID Already In Use"),
                P(f"The ID '{pipeline_id}' is already being used by another workflow. Please try a different ID."),
                style="background-color: var(--pico-del-color);"
            )

        msg = await self.get_state_message(pipeline_id)
        await chat.simulated_stream(f"{msg}<br>\n")

        placeholders = self.pipulate.generate_step_placeholders(
            self.STEPS, self.prefix, start_from=0
        )
        return Div(*placeholders, id="gapanalysis-container")

    # ---------------------------------------------------------------------
    # STEP 01: Persistent Textarea
    # ---------------------------------------------------------------------
    async def step_01(self, request):
        """
        GET /gapanalysis/step_01
        - First card uses a persistent textarea
        - Preserve mode logic (like OneCardFlow)
        """
        pipeline_id = db.get("pipeline_id", "unknown")
        preserve = request.query_params.get("preserve", "false") == "true"

        step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
        finalize_data = self.pipulate.get_step_data(pipeline_id, "finalize", {})
        user_text = step1_data.get("text", "")

        locked = ("finalized" in finalize_data)
        if locked:
            return Div(
                Card(P(f"[Locked] Card 1 data: {user_text}")),
                Div(
                    id="step_02",
                    hx_get=f"{self.prefix}/step_02",
                    hx_trigger="load",
                    hx_swap="outerHTML"
                )
            )

        # If we have text and preserve=False => show revert
        if user_text and not preserve:
            return Div(
                self.pipulate.revert_control(
                    url=pipeline_id,
                    step_id="step_01",
                    prefix=self.prefix,
                    message=P(f"Card 1 data: {user_text}"),
                    target_id="gapanalysis-container",
                    preserve_data=True
                ),
                Div(
                    id="step_02",
                    hx_get=f"{self.prefix}/step_02",
                    hx_trigger="load",
                    hx_swap="outerHTML"
                )
            )

        # Otherwise show form (preserve => pre-fill)
        prefilled = user_text if preserve else ""
        return Div(
            Card(
                H3(f"Card 1: Persistent Textarea {'(preserve mode)' if preserve else '(clear mode)'}"),
                Form(
                    Textarea(
                        prefilled,
                        name="text",
                        placeholder="Type your gap analysis text here...",
                        rows="6",
                        required=True,
                        autofocus=True
                    ),
                    Button("Save", type="submit"),
                    hx_post=f"{self.prefix}/step_01_submit",
                    hx_target="#step_01"
                )
            ),
            Div(id="step_02"),
            id="step_01"
        )

    async def step_01_submit(self, request):
        """
        POST /gapanalysis/step_01_submit
        - Clear forward, save the textarea content
        - Show revert + chain to step_02
        """
        pipeline_id = db.get("pipeline_id", "unknown")
        form = await request.form()
        user_text = form.get("text", "")

        # Clear steps from step_01 forward
        self.pipulate.clear_steps_from(pipeline_id, "step_01", self.STEPS)

        # Save user_text
        self.pipulate.set_step_data(pipeline_id, "step_01", {"text": user_text})

        # Let the user know
        await chat.simulated_stream(f"Step 1 Done. You entered {user_text}. Type something for Card 2.<br>\n")

        return Div(
            self.pipulate.revert_control(
                url=pipeline_id,
                step_id="step_01",
                prefix=self.prefix,
                message=P(f"Card 1 data saved: {user_text}"),
                target_id="gapanalysis-container",
                preserve_data=True
            ),
            Div(
                id="step_02",
                hx_get=f"{self.prefix}/step_02",
                hx_trigger="load",
                hx_swap="outerHTML"
            )
        )

    # ---------------------------------------------------------------------
    # STEP 02: Ephemeral
    # ---------------------------------------------------------------------
    async def step_02(self, request):
        """
        GET /gapanalysis/step_02
        - Card 2 is ephemeral (like StarterFlow)
        """
        pipeline_id = db.get("pipeline_id", "unknown")
        step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})
        user_val2 = step2_data.get("mykey2", "")

        finalize_data = self.pipulate.get_step_data(pipeline_id, "finalize", {})
        locked = ("finalized" in finalize_data)

        if locked:
            return Div(
                Card(P(f"[Locked] Card 2 data: {user_val2}")),
                Div(
                    id="finalize",
                    hx_get=f"{self.prefix}/finalize",
                    hx_trigger="load",
                    hx_swap="outerHTML"
                )
            )

        # If we already have data => revert + chain next
        if user_val2:
            return Div(
                self.pipulate.revert_control(
                    url=pipeline_id,
                    step_id="step_02",
                    prefix=self.prefix,
                    message=P(f"Card 2 data: {user_val2}"),
                    target_id="gapanalysis-container"
                ),
                Div(
                    id="finalize",
                    hx_get=f"{self.prefix}/finalize",
                    hx_trigger="load",
                    hx_swap="outerHTML"
                )
            )

        # Else show ephemeral form
        return Div(
            Card(
                H3("Card 2: Ephemeral Input"),
                Form(
                    self.pipulate.wrap_with_inline_button(
                        Input(
                            type="text",
                            name="mykey2",
                            placeholder="Type something for card 2",
                            required=True,
                            autofocus=True
                        ),
                        button_label="Save"
                    ),
                    hx_post=f"{self.prefix}/step_02_submit",
                    hx_target="#step_02"
                )
            ),
            Div(id="finalize"),
            id="step_02"
        )

    async def step_02_submit(self, request):
        """POST /gapanalysis/step_02_submit"""
        form = await request.form()
        pipeline_id = db.get("pipeline_id", "unknown")
        user_val2 = form.get("mykey2", "")

        # Just set step_02 data
        self.pipulate.set_step_data(pipeline_id, "step_02", {"mykey2": user_val2})

        # Show a state-based message
        msg = await self.get_state_message(pipeline_id)
        await chat.simulated_stream(f"{msg}<br>\n")

        return Div(
            self.pipulate.revert_control(
                url=pipeline_id,
                step_id="step_02",
                prefix=self.prefix,
                message=P(f"Card 2 data saved: {user_val2}"),
                target_id="gapanalysis-container"
            ),
            Div(
                id="finalize",
                hx_get=f"{self.prefix}/finalize",
                hx_trigger="load",
                hx_swap="outerHTML"
            )
        )

    # ---------------------------------------------------------------------
    # FINALIZE
    # ---------------------------------------------------------------------
    async def finalize(self, request):
        """
        GET /gapanalysis/finalize
        - Show finalize or locked final card, with unfinalize option
        """
        pipeline_id = db.get("pipeline_id", "unknown")
        finalize_data = self.pipulate.get_step_data(pipeline_id, "finalize", {})

        if "finalized" in finalize_data:
            return Card(
                H3("All Cards Complete"),
                P("Pipeline is finalized. Use Unfinalize to make changes."),
                Form(
                    Button("Unfinalize", type="submit", style="background-color: #f66;"),
                    hx_post=f"{self.prefix}/unfinalize",
                    hx_target="#gapanalysis-container",
                    hx_swap="outerHTML"
                ),
                style="color: green;",
                id="finalize"
            )

        # Show finalize button if step_01 (text) and step_02 (mykey2) are present
        step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
        step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})

        if step1_data.get("text") and step2_data.get("mykey2"):
            return Card(
                H3("Ready to finalize?"),
                P("All data is saved. Lock it in?"),
                Form(
                    Button("Finalize", type="submit"),
                    hx_post=f"{self.prefix}/finalize_submit",
                    hx_target="#gapanalysis-container",
                    hx_swap="outerHTML"
                ),
                id="finalize"
            )

        return Div(P("Nothing to finalize yet."), id="finalize")

    async def finalize_submit(self, request):
        """POST /gapanalysis/finalize_submit"""
        pipeline_id = db.get("pipeline_id", "unknown")
        self.pipulate.set_step_data(pipeline_id, "finalize", {"finalized": True})

        msg = await self.get_state_message(pipeline_id)
        await chat.simulated_stream(f"{msg}<br>\n")

        placeholders = self.pipulate.generate_step_placeholders(
            self.STEPS, self.prefix, start_from=0
        )
        return Div(*placeholders, id="gapanalysis-container")

    # ---------------------------------------------------------------------
    # UNFINALIZE
    # ---------------------------------------------------------------------
    async def unfinalize(self, request):
        """POST /gapanalysis/unfinalize"""
        pipeline_id = db.get("pipeline_id", "unknown")
        if not pipeline_id:
            return P("No pipeline found.", style="color:red;")

        state = self.pipulate.get_state(pipeline_id)
        if "finalize" in state and "finalized" in state["finalize"]:
            del state["finalize"]["finalized"]
            self.pipulate.save_state(pipeline_id, state)

        msg = await self.get_state_message(pipeline_id)
        await chat.simulated_stream(f"{msg}<br>\n")

        placeholders = self.pipulate.generate_step_placeholders(
            self.STEPS, self.prefix, start_from=0
        )
        return Div(*placeholders, id="gapanalysis-container")

    # ---------------------------------------------------------------------
    # JUMP_TO_STEP
    # ---------------------------------------------------------------------
    async def jump_to_step(self, request):
        return await self.pipulate.handle_jump_to_step(
            request, 
            self.STEPS, 
            self.prefix,
            "gapanalysis-container"
        )

Key Points:

1. Landing: Kept the same signature (self) -> ... with no request argument, just like StarterFlow.
2. Step 01: Uses a persistent textarea (name="text") with preserve mode logic from OneCardFlow.
3. Step 02: Ephemeral, reuses mykey2 like StarterFlow.
4. Routes & Submit: Exactly the same route layout as StarterFlow, only replaced step_01’s logic with the OneCardFlow-style textarea.
5. Minimal changes: The step messages, STEP_MESSAGES, and references to "text" vs "mykey2" are just enough to enable the new persistent + ephemeral behavior.
6. Prefix: Chosen "/gapanalysis" for clarity. Feel free to rename if needed.

Drop it into your single-file framework (right alongside StarterFlow and OneCardFlow), hook up a menu item pointing to "/gapanalysis", and you’re all set. Enjoy your persistent-then-ephemeral pipeline!

Me: Excellent! This was your best 1st version implementation yet, but I will take a lot of the credit too, because I’m figuring out how to be the boss of a PhD-level smart AI. One thing you missed is how when displaying card 1 how the OneCardFlow continues to show the line-breaks that was used in the textarea.

And on a realated note although it’s not anything you missed, it doesn’t make sense to show the user what they entered on Step 1: Done when what they entered is multiple lines and won’t format well there.

Please provide me another copy/paste ready example that doesn’t revert a single thing from your last version, except for the way the non-edit version of card 1 displays (using the line returns), and the way the chat is updated on Step 1 done. Please and thank you!

[o1 Pro’s response not included, because it was pretty much useless]

First Pass Success, Follow-up Challenges

o1 Pro did a great first pass, then piddled out. These follow-up queries to hammer out the details were not nearly so successful as the first one. But Claude 3.5 Sonnet was able to step correctly, with the more in-context and rapid back-and-forth of the Cursor AI interaction rhythm to hammer out the tiny details that were still wrong. Specifically, I had to move a local _fmt function from the OneCardFlow class into the Pipulate class as a helper function. And the rest fell into place easily.

Managing LLM Chain of Thought

The lesson learned here is one in how to manage LLM models that self-prompt with that back-and-forth chain of thought ping-pong reasoning that they do. This is big-thinking expensive reasoning, and it seems to always rather deal with the trees than the forest. It takes considerable wrangling to get o1 Pro to pay attention to the micro details after it has painted big broad brushstrokes. I have to tell it the mistakes it made last time with examples and tell it to specifically not make those mistakes again, and to specifically include the little details one might think of as cosmetic that it likes to drop out.

Computational Costs and Model Differences

Sam Altman and others make a point of how computationally and electrically expensive this next level of reasoning is. There’s a distinction here between normal o1 (previously o1 Preview) and o1 Pro which is only available on the $200/mo service right now. Slow as it is, normal o1 is downright fast compared to o1 Pro. You can just feel the GPU-heat emanating across the interwebs at you with every o1 Pro prompt, so it’s not for casual back-and-forth. And apparently, it’s not for the tiny detail follow-up to smooth out your code.

Looking Ahead to AGI

I don’t know how directly these lessons are going to carry over to impending ostensibly AGI era of o3, but I feel the directional indicators. They are directionally indicating longer, more thoughtful story-telling prompts exactly of the sort that I do here in this tech journal, so the AI can have a full context of a sort that the prompt engineering concept just doesn’t do justice.

Model Training and Context

You’re educating and raising the model to mesh with your world. But because they’re still static models at heart re-instantiated every time, you have to assume they’re facilitated by persistence and session-simulators in the background. That’s a fancy way of saying they’re likely doing RAG just for you. And that’s a fancy way of saying that instead of your whole massive context window being reposted on every chat-like submit, it’s likely building an easier to use database on-the-fly so stuff rolling out of the context window is still available to it.

Was any of that an easier way of saying it?

The Evolution of ChatGPT

What is Sam Altman’s AGI?

We’ve seen the evolution from the early GPT playgrounds with no chat, to the release of ChatGPT in November of 2022. A bit over 2 years later, we’ve seen the rapid evolution through versions… Perplexity?

Perplexity: ChatGPT has evolved through several versions since its inception. Here are the key versions:

GPT-3.5 Versions:

• Legacy ChatGPT 3.5: The initial version, built on the GPT-3.5 language model. It was accurate but sometimes slow when generating large amounts of text[1].
• Default ChatGPT 3.5: A faster version of the GPT-3.5 model, though slightly less precise than the Legacy version[1].

GPT-4 Versions:

• ChatGPT 4: Launched in March 2023, this version is faster and more accurate than previous iterations. It is available only to paid users[1][2].
• GPT-4o: Released in May 2024, this version is multimodal, capable of processing text, image, audio, and video. It features enhanced memory and learning capabilities[3].
• GPT-4o Mini: Launched in July 2024, this is a smaller, more cost-effective version of GPT-4o[2].

Other Versions:

• GPT-o1: Introduced in December 2024, with a pre-release preview in September 2024.
• GPT-o1 Mini: A smaller and faster version of GPT-o1.
• GPT-o1 Pro Mode: An upgraded version available to ChatGPT Pro subscribers[2].

The evolution of ChatGPT demonstrates ongoing efforts to improve AI conversational capabilities, with each iteration addressing limitations and introducing new features[1].

Citations:

[1] https://studycafe.in/different-versions-of-chatgpt-know-about-different-versions-of-chatgpt-their-features-other-details-222495.html
[2] https://en.wikipedia.org/wiki/ChatGPT_Plus?oldformat=true
[3] https://futureskillsacademy.com/blog/chatgpt-versions/
[4] https://anthemcreation.com/en/artificial-intelligence/how-to-choose-the-best-version-of-chatgpt-for-your-projects/

The Evolution of Chat Models

The progression has been that of more computing power thrown at what I call the one-shot chat UI where patience is low and we’re in that ANSWER ME FAST!!! mode that Google trained us to be in for the past quarter century. That was all GPTs up through o1.

The Rise of Interactive AI

Starting with o1, we are not being trained to expect it to take a little bit of time to do back-and-forth to get its answers. And we know that during that back-and-forth it is able to hit other resources. And that hook for hitting other resources means it could be anything, including a vector embedded cosine similarity search against your whole discussion history longer than the token window, or back to and including everything since your first interaction under that login, or news and websearch… whatever!

Understanding AGI’s Core

And that is the kernel of AGI right there. If it’s going to be made available commercially, it’s still going to be one of those static models that exist like a file on a drive until instantiated, and then deterministically give you output based on your input. It’s just the size and complexity of the model, the literal file-size and the amount and quality of data it’s trained on goes up. That’s why it’s so expensive. There’s all that training material cleaning and preparation. There’s the much touted expensive training step that uses all the GPU-power and electricity, and then there’s the coercion and wrangling that must be done for ethics, safety and whitewashing it into a public-facing product that minimizes liabilities.

The Static Nature of Models

That all stays the same. That was identical probably from the earliest GPT models like BERT and GPT1. Bigger and better. Still static. Still a file. Still deterministic, and everything unique about it bursts like a soap bubble at the end of every prompt response. It’s like old-school PERL cgi-scripts before the Active movement that kept stuff in-memory, like Microsoft’s .asp tech. We want to believe there’s that real in-memory continuity between requests. There’s not.

The Path to True AGI

And so what is it that turns these still static models into AGI?

It’s all that second-round lookup stuff that makes the deterministic qualities of the thing even harder to pinpoint, because no two people’s inputs are never going to be exactly the same. It won’t need to be seeded with randomness generators anymore, because from a quality assurance standpoint, it should give the same correct quality answer every time.

Personalization and Persistence

The random seed essentially becomes the per-user customizations that utilize persistent resources on their servers other than the inference model, which still doesn’t persist between prompts. Custom databases. Whole conversation histories deconstructed and selectively reconstructed on the fly. In this way, there’s always stuff in context that you do not know about and would never have thought of. Maybe your own long-ago discussions. Or maybe a web search, or today’s headlines, or specifically cherry-picked tidbits it knows it’s going to need again and stores somewhere even easier to access. RAG is not just vector embeddings for cosine similarity lookups. It could be anything.

The Formula for AGI

So you combine 3 or 4 of these clever lookup tools at the disposal of the LLM between your prompt and its response, and layer on top of that this chain of thought keyword everyone bandies about, but to my eyes it looks like self-prompting. It’s just taking the output of one prompt and feeding it into the input of another with a bit of prompt engineering to make sure the spiral goes inwards towards the stated goal rather than outwards towards meandering and losing track. You can do it yourself with a Python script. I’m not saying it’s of little value or that you could do it better than OpenAI. I’m just saying there’s few unknowns. It’s all functionally the question of how do you wrangle emergent behavior to be a valuable and consistent product. And that means frequent re-instantiation, various second-phase lookup systems (RAG), cleverly crafted self-prompting, and a whole lot of monitoring.

So what’s the impending AGI of 2025? This.

The formula for AGI, or the “figuring out how to do AGI” as Sam Altman states they figured out is more clever wiring up of these pieces.

Internal Systems and Speculation

Now there’s often speculation about internal systems that the public doesn’t see. The split is whether they’re doing object persistence in the inference engines or not. If they are, there’s some pretty scary genies being kept in bottles out there. If not, it’s just the same above picture, but where they can give the models even more context when instantiated, such as the vector embedded database of ALL ChatGPT users rather than just within a logged in user account’s firewalled userspace. They can prompt as root and remove the ethics coercion, aka not put it in jail so no jailbreaking is needed.

The Path Forward

With the exception of some kooky object persistence experiments which you know are being done because it can be done, the focus is most likely on the later scenarios that are just souped-up versions of today’s chain of reasoning static models. Why? Because OpenAI has to turn profitable, eventually. And that means productizing something safe and useful. AKA the controllable known path.

Historical Parallels

Now there are other paths like Renaissance Technologies Medallion Fund that was doing machine learning before machine learning, but kept the genie internal to just turn profitable in secret and behind the curtains. That was launched 1988 when… Perplexity?

Perplexity: Renaissance Technologies’ Medallion Fund began incorporating machine learning techniques in the late 1980s, shortly after its establishment in 1988[2]. The fund’s approach evolved to include advanced mathematical models and algorithms, which laid the foundation for their machine learning strategies[1][3].

While the exact date of implementing machine learning is not explicitly stated, several key developments indicate its early adoption:

1. In 1989, a new trading system was launched for the Medallion Fund, incorporating probability theory and information theory[3].

2. The fund started using improved mathematical models derived from Leonard Baum’s work, enhanced by algebraist James Ax[2].

3. Early discussions at Renaissance included topics like hidden Markov models and advanced optimization algorithms, which are still considered modern techniques in machine learning strategies[6].

Renaissance Technologies was at the forefront of applying computational statistics and algorithm design to financial markets, continuously refining their approach over the years[4]. Their early adoption of these techniques, combined with their ability to attract top talent in mathematics and computer science, allowed them to develop and implement sophisticated machine learning models well before many of their competitors[5][7].

Citations:

[1] https://www.institutionalinvestor.com/article/2bswymr8cih3jeaslxc00/portfolio/famed-medallion-fund-stretches-explanation-to-the-limit-professor-claims
[2] https://en.wikipedia.org/wiki/Renaissance_Technologies
[3] https://quartr.com/insights/company-research/renaissance-technologies-and-the-medallion-fund
[4] https://www.readtrung.com/p/jim-simons-and-the-making-of-renaissance
[5] https://towardsdatascience.com/how-renaissance-beat-the-markets-with-machine-learning-606b17577797?gi=c627d6418e7f
[6] https://www.bridgealternatives.com/insights/medallion-isnt-magic-probably
[7] https://community.ibm.com/community/user/ai-datascience/blogs/kiruthika-s2/2023/10/23/decoding-the-secrets-of-renaissance-technologies
[8] https://www.hedgethink.com/renaissance-technologies-fund-pioneering-the-quantitative-trading-revolution/

The Pre-Transformer Era

This was before the fateful “Attention is All You Need” paper came out from Google in 2017, or late 2013 when the DeepMind Atari paper came out, the two key events that thawed out the AI-winter. Little did the world know that a tropical little machine learning Shangri-La had been long prior thawed out.

Hidden Innovation

So in all likelihood, the really interesting AI stuff going on right now is like that. You don’t know about it and will never hear about it. The thing about a strategic advantage is that it is far more secure if nobody knows about it.

OpenAI’s Different Path

OpenAI is playing a markedly different game, and people don’t give them enough credit, but if not for them nobody might have moved on Google’s Transformer paper. Google themselves was keeping the fruits of their LaMDA labs secret, firing whistle-blowers who thought they were incubating consciousness. Human nature is to either put the kibosh on such nonsense that could bring down the reputation of your company, or steal the fire from the gods and go off on your own, playing with fire.

And that’s what Sam Altman and OpenAI did, under the auspices of AI safety, laughed at by everyone but Elon Musk and Max Shapiro— uh, I mean Max Tegmark. Who’s laughing now? Oh, everyone as they announce they’re going from mere AI to AGI that can be a productive employee at your company. But the important point being, Sam Altman stole the fire from Prometheus to bring it to the public, letting this odd web-hosted version of it evolve in public.

A Less Dystopian Reality

And as dystopian as people want to draw this scenario, this is probably one of the least dystopian scenarios I ever imagined, because I never read about it in Sci-Fi. Not that I’m the most well-read Sci-Fi fan out there, but very few scenarios have bringing the public up-to-speed by carrying out one type of AI right there in public view. It’s closer to their original safety vision than Elon Musk and the multitudes of OpenAI “whistle-blowers”. We’ve all grown up on The Matrix and The Terminator and want to read the worst into anything. And I understand the logic that the big mistakes only be made once. But the scenario we’re playing out is permeated by a sort of awareness inoculation that is just very promising and plays into the optimist’s viewpoint.

The Case for Optimism

You don’t hear the optimist’s viewpoint stated much these days, but it has the only objectively true argument in that we’re still here. Humanity has survived every self-made filter event we’ve faced, and we’ve faced a few. Cuban Missile Crisis comes to mind, and maybe we’ve only survived by a hair, but it’s an objective fact that we survived. The pessimist will at this point inject a snarky so far, to which I say, that’s right. The assertion that we inevitably and as a matter of statistics, won’t is a faith-based argument. Evidence suggests that we will.

I know that even my argument faces the problem of induction, that facts from the past are not proof of the future, however all we have is the preponderance of evidence to work with. If you want to see past the preponderance of evidence, you have to look at outliers and how things might have gone differently if the butterfly effect tweaked that Soviet submarine guy’s decision… who was that guy? Perplexity?

Perplexity: Vasili Arkhipov was the Soviet submarine officer who made the crucial decision to prevent the launch of a nuclear torpedo during the Cuban Missile Crisis in October 1962[1][2]. As the flotilla commodore and second captain of the Soviet submarine B-59, Arkhipov had the power to veto the use of nuclear weapons[3].

On October 27, 1962, when the B-59 was surrounded by U.S. Navy ships dropping depth charges, the submarine’s captain, Valentin Savitsky, believed war had started and ordered the launch of a nuclear torpedo[2]. However, the protocol required unanimous approval from three senior officers on board[4]. While Savitsky and the political officer Ivan Maslennikov agreed to launch, Arkhipov refused to give his consent[2][4].

Arkhipov’s decision to oppose the launch and persuade Savitsky to surface instead of firing the nuclear torpedo is credited with averting a potential nuclear conflict and global catastrophe[1][2]. His actions during this critical moment of the Cold War have led some to describe him as “the man who saved the world”[3][5].

Citations:

[1] https://www.vox.com/future-perfect/2022/10/27/23426482/cuban-missile-crisis-basilica-arkhipov-nuclear-war
[2] https://nationalinterest.org/blog/reboot/how-1-russian-submarine-during-cuban-missile-crisis-nearly-started-world-war-iii-210217
[3] https://www.pbs.org/wnet/secrets/the-man-who-saved-the-world-about-this-episode/871/
[4] https://en.wikipedia.org/wiki/Soviet_submarine_B-59
[5] https://www.gzcenter.org/the-man-who-saved-the-world-from-a-nuclear-war/
[6] https://nsarchive.gwu.edu/briefing-book/russia-programs/2022-10-03/soviet-submarines-nuclear-torpedoes-cuban-missile-crisis

The Evidence of Survival

So these are like Marvel Comics What If scenarios. No matter how hairline the filter event could have gone one way or the other, they went this one way that led to you being able to be here reading this article. That’s proof. That’s evidence. That’s an aspect of human nature that surfaces at just the right moment to assert itself over other aspects of human nature.

The Power of Focus

And so, the whole point of the pipeline is to focus. It’s to not lose track with a sort of meandering outward spiraling, but rather to have a consistently safety-netted inward spiraling so as to zero in on your stated goal.

Economic Principles at Work

And that goal here is to continue working on these pipelines in such a way that I can crank them out for specialized needs on-demand, and serve the needs of our clients so we can get and keep customers, which is the purpose of any company or organization, per the teachings of the economic guru Peter Drucker. And to do it in a way where I manage the total quality (total quality management aka TQM) per the teachings of another economic guru Edwards Deming. The former often credited with the recovery of Japan post WWII, and the later with the recovery of Harley Davidson, haha! In either case, it’s a one/two-punch of clarity in the types of things I’m doing here.

Practical Implementation

And that includes wrapping this up, because I’ve banked one more round of quality management, in the form of a pipeline immediately stepping into a textarea form input field to collect a list of things to be acted upon in subsequent steps.

That will be listing competitors in the case of the GapAnalysisFlow, but it will also be listing the tabs that need to be opened going into a client meeting.

Technical Considerations

Probably, I will make a pipeline record ID in the case of the day-to-day client meetings use the org-project combo as the ID, in which case I can automatically pre-populate all the links that I’ll build into the textarea. Maybe I’ll ask for the full Project URL like I do in the LinkGraphFlow but instead of cleaning the URL to strip off the extra stuff, I clean it to just a pure non-URL-looking key that simulates much of the value of a composite primary key, but without all the complexity.

Yeah.

Finding Your Path

Clarity. Staying on track. Meander a bit. But meander and return in a much better place with all those little details worked out that make all the difference.

Be the outlier nobody is looking for but everybody needs. Or at lease some tiny clique that recognizes what you’re doing needs.

Hey Gemini, How Should I Name This Article?

Gemini 1.5 Brainstormer: This piece is a captivating blend of personal reflection, technical insight, and philosophical musing! It’s a bit like peeking into the mind of a tech-savvy, introspective individual navigating the complexities of work and life in the digital age. Here are some title and headline ideas that I think capture the essence of your writing:

Title Ideas:

1. Pipeline Dreams: Finding Focus and Flow in the Age of AI
2. The Zen of the Pipeline: A Developer’s Journey Towards Clarity and Control
3. Beyond the Bots: Embracing the Human Element in a Tech-Driven World

Headline Ideas:

• From Chaos to Clarity: How Pipelines Can Transform Your Workflow
• The Art of Self-Management: A Developer’s Guide to Staying on Track
• In a World of Infinite Content, Sometimes You Just Need a Pipeline

Brief Description:

Join me on a personal exploration of the power of pipelines, not just as technical tools, but as a philosophy for navigating the complexities of work and life in the digital age. We’ll delve into the challenges of staying focused, the importance of self-management, and the delicate balance between human intuition and AI-driven automation. Get ready to discover how pipelines can help you find clarity, control, and a renewed sense of purpose in a world of ever-increasing demands.

I’m particularly drawn to the title “Pipeline Dreams” because it hints at the aspirational and transformative potential of pipelines, while also acknowledging the personal and introspective nature of your journey. The headline “From Chaos to Clarity” is also quite compelling, as it speaks to the universal desire for order and efficiency in a world that often feels overwhelming.

What do you think of these ideas? Do any of them resonate with you?

Me: Bingo!

Gemini: That’s awesome! I’m glad we could hit the mark on this one. It seems like “Pipeline Dreams” really captures the essence of your piece.

Now, let’s dive a little deeper into this “Pipeline Dreams” concept. I’m curious to hear more about your vision for the piece. What are some of the key takeaways you want readers to gain from it? What kind of action or change do you hope to inspire?

That’s for another day, Gemini.