The Bedrock and the Dazzle: Building Timeless Tools in an Age of Tech Churn

Setting the Stage: Context for the Curious Book Reader

This journal entry captures a pivotal moment of frustration with the tech industry’s relentless churn. The author reflects on the ephemeral nature of past successes built on fragile loopholes—like the early version of the SEO tool Pipulate—and contrasts it with the enduring skill of a true craftsman. This piece marks a philosophical shift, a deliberate turn away from chasing fleeting trends and towards building a ‘future-proof’ foundation with stable, powerful technologies like Python and Nix. It’s the thought-work behind a new project: reimagining the ‘dazzling show’ of real-time data, not as a clever trick, but as a robust and reproducible workflow that starts in a Jupyter Notebook but then moves to the new Pipulate, seeking something timeless in a field defined by constant change.

Technical Journal Entry Begins

The original version of Pipulate was born in crawling websites directly into spreadsheets in real-time so you could watch its progress like a magic trick and do title and meta optimizations right there in front of a client and turn it around to them right there, putting on a dazzling show and looking like a pure friggin magician. This was like 2010 before there was even a Google Cloud Platform (GCP) and tricks like this used the now sunset gdata. So people weren’t used to seeing that sort of thing and it was really hot stuff. The crawl was triggered by clicking a bookmarket, a little browser bookmark that was actually pure JavaScript like javascript:alert('Hello, world!'); That kind of thing used to work before all the security got locked down in browsers.

This bookmarklet trick require a plugin. You just dragged a bookmarklet from a static webpage to your browser favorites bar, surfed to a website, clicked the bookmarklet, gave OAuth approval to access GSheets, and just like that the website was getting crawled into the spreadsheet right there before your eyes — at some pre-determined click-depth of number of pages so that the crawl wouldn’t go on forever. This was such hot stuff that my employer at the time, 360i, branded it as their own technology to show they had a technology offering at the time which helped them secure an upper-right position in the 2012 Forrester Wave report, a pretty big deal. Besides HitTail, this was on of my tiny moments of glory.

The Ephemeral Glory of an SEO Magician

I’ve gotten ambitious in the world of tech from time to time and took a good hard swing at the ball and have become a master of pop-files. I have no shortage if ideas and ambition and even willingness to try to implement. The first version of Pipulate that was branded 360iTiger at the time was one attempt. Before that it was a writing suggestion tool for long-tail keyword targeting for bloggers that mined your search-referrers ideas back when you could do that with JavaScript to do that sort of thing because a visit from Google carried the keywords that people searched on in the HTTP request headers that the browser sent to the server as part of the page-request. YOu could sit there and harvest that and if the &start= parameter was >=2 you had a writing suggestion because if your site had the propensity to get traffic on that topic without you deliberately targeting it, now much the better if you zeroed in on and wrote specifically about that topic.

Everyone’s referrers is essentially a writing-topic suggestion box and continues to be so today although you have to go through Google Search Console (GSC) now to get that same data because Google givith and Google taketh away. The taking away is today known as the not provided event and is kind of like a archaeological extinction event separation boundary between before everyone used https:// secure protocol and after. It was in the guise of locking down security that this web bug of passing the referrer variable along in a web visit was squashed.

Such trends continue today of not giving one iota more data to users for free than they have to, without rounding you into Google services. It’s pay to play and recent examples include making Google Search Result Pages (SERPs) require a browser that executes JavaScript to be required in order to “scrape” (aka programmatically capture) search positions, thus driving up the cost of benchmarking your keyword performance against competitors by a factor of however much it costs to run a program than it costs to fetch plain text.

Following that also very recently was the removal of the &num=100 parameter that everyone used to get as many search results per page as they could. So with 1 page-load (and now browser-rendering) you could get 10x more search results then you could on a single page without clicking next, next, next to go through all 10 pages of results. Google made the num parameter in the search querystring not work anymore, thus driving up costs for SERP scraping and upping the stakes in the cat and mouse arms race even more. If running a browser is 10x cost and clicking next 10 times is also 10x cost, it now costs no less than 100 times more to scrape SERPs — and that’s a conservative estimate.

The Arms Race Against ‘Not Provided’

As an interesting aside, everyone’s GSC impressions suddenly dropped for a few days in September to more honest levels because after Google suddenly dropped next=x support and before the scraping tools adapted there was a small window where you could see an honest read on your search impressions — the purple line under performance. This is because impressions were being artificially driven up because being in position #99 counted as an impression just the same as being in position #1 if you’re showing 100 results per page and an impression is an impression.

This sort of whack-a-mole arms race goes back further with even more examples. You used to be able to get a PageRank (aka PR) score from a Google toolbar. That’s gone along with the hidden API that used to back-end it and stayed available for while. You used to be able to get fairly accurate predicted monthly traffic volume on any keyword whether or not you had that keyword flighted in an AdWords marketing campaign or not. That’s perhaps the most precise example of pay to play because Google does give a lot of generous information back to you at no cost through Search Console once you ignore the fact we had that already before not provided. You can tell how much they’re sampling-down your data but you can’t tell how much they’re otherwise filtering and processing it before they give it back to you.

I went through a fair degree of career crisis wondering whether I would remain an SEO or not as all the best tricks stopped working. HitTail was no longer my baby when not provided challenged its premise and it had to be rewired to the GSC API, but it still hit me hard this volatile nature of tech. Lifeblood could be cut off at any time for any reason. This is a different story than the vendors changing the tools on you for reasons of planned obsolescence to keep you re-buying, re-training and re-subscribing but it’s in the same ballpark.

Living on the Fault Line of Tech

Yes it’s true, never stop learning. Change is the only constant, yadda yadda.

And what I’m talking about is NOTHING compared to what happened to all those Flash and Shockwave developers when the iPhone came out and never supported them. Yes, a lot of them could adapt and move over to the responsive design movement this brought about so that one website could more or less serve desktop mobile and tablets with the creative use of new CSS media queries — but still it’s living on the fault-line. It’s a position of weakness. It’s stressful and gets harder as you get older.

But this is completely the opposite of the bill of goods we get sold on, that you can actually get better at a thing as you get older. Woodworkers, musicians, athletes. Okay, maybe not athletes. But anything cerebral that deals with the mind. Can the mind not get forever sharper as you get older? Especially if you missed your windows of opportunities when you were younger, can you not try again and again, improving each time until you have learned enough to get everything right?

And why not in tech? Is technology really so different from any other craft that there can’t be some underlying bedrock? Something forever-true about the medium like… well, rock! Or wood. And the tools with which you operate on the media like chisels?

Can’t you just know something and have that thing that you know be meaningful and valuable forever without some sort of loophole churn going on underneath of you that you didn’t know you signed up for whether it’s happening because of planned obsolescence or the less-common but still occasional tectonic shift in tech that genuinely does change everything?

How often do such tectonic shifts happen with musical instruments? With sporting equipment? Maybe snowboards coming on the scene or tennis rackets getting big. But it’s nothing like tech with some comical stack of frameworks, transpilers, bundlers, and containerization layers — all always changing.

In Search of a Bedrock: Can Tech Have Craftsmanship?

This is not old man yells at cloud. I don’t know squat but I do know a little something about approaching a project. I approach a project now. I will make it future-proof. It won’t be exactly what everyone wants at first glance, but over time they will realize I am helping them scratch an itch they didn’t even know they had. And more and more people will realize and there will be a groundswell. I plan on kicking off a bit of a grass roots movement here. It goes something like this.

The Philosophy of a Future-Proof Solution

Deep breath.

Yes, I will get to actual concrete work, but this is the thought-work. This is the warm-up and it is to achieve a “changes everything forever forward” sort of breakthrough. This is forced creativity. That sounds awful. It is creativity on demand. It is stimulating motivation and cross-disciplinary thinking to build momentum.

Creativity can be taught.

Initiative can be practiced into you.

Difficult things become easier with the right supporting systems.

But system-building for its own sake is a rabbit hole and self-defeating. There is a legendary 2001 article by Joel Spolsky on Architecture Astronauts. As important as good systems are, don’t become one of those. Systems are born of the crucible of everyday work, not ideals. Yes, ideals can exist but then there is always a tug-of-war between the best laid plans of the generals back at home-base and those plans making contact with the ground situation. Joel Spolsky was a paratrooper.

By the end of this particular article, I will actually have a project started which will be the template for much other similar work to come and it’s appropriate I talked about the first version of Pipulate that crawled websites into spreadsheets. There is a deep and lasting unbreakable love for spreadsheets in the marketing world. Other worlds too. Spreadsheets will never die even though from a systems perspective they create all these objectionable little islands of data and feelings of personal power. I think it’s because the database alternatives are so awful in comparison taking such specialized knowledge and infrastructure support. You can tend a spreadsheet like a garden but specialists need to come in when it’s a database. How famous is Excel? How many people use Google Sheets for everyday list management? The appeal is undeniable.

From Spreadsheet Gardens to Programmatic Power

But building a system around a spreadsheet has challenges. If it’s Web-based then you have to interact with Google OAuth authentication or have service accounts or something along those lines, and there’s almost automatically some sort of server and tech liability in the picture. If you want to do the same thing with Excel it can get even worse with many roads leading to Microsoft SharePoint and even more cost and tech liability.

And so you look at alternatives, either alternative spreadsheet approaches or completely non-spreadsheet approaches that would be “good enough” and “satisfy the client”. Simulating a spreadsheet on the Web with some sort of “datagrid” is always an option, but then there’s quite a bit of potential custom development work. Almost every solution has some sort of fragility, vendor cost and lock-in, highly specialized knowledge and know-how. Where’s the craftsmanship? Where’s the domain cross-cutting… cross-cutting what? Problem domains? Tooling? Through time into the future? I want to cross-cut it all!

I do of course know the solution I’m working towards. I have to draw the picture and perhaps reproduce some of the thought process it took to get here in the past. We are looking for 80/20-Rule solutions whereby we get 80% of what the customer or stakeholder or client for the work wants for the first 20% of the actual effort you think might be required or could be mustered to put into it. It simply cannot take as much work as you think because everything takes more work than you think, so you have to simplify, simplify, simplify and have an easy target so that when for some reason the complexity does explode on you, you have the excess capacity to deal with it and you didn’t burn all your energies dealing with OAuth and Sharepoint.

Solutions like the ones I’m talking about aren’t big enterprise-grade Web-hosted scaling to thousands of simultaneous user sort of things. My concerns are not enterprise concerns. My concerns are single-user, 1-machine concerns. While technically there is hosting because the browser is being used to provide the user interface for a local program instead of the native OS, QT or TK. When building stuff if you want it to be more than a command-line tool running in the terminal, you’ve got to choose the graphical user interface builder tool and every native OS offers something, and then there’s the two great libraries of the free and open source (FOSS) world, QT and TK. But then there’s also just the plain old web browser.

If you choose the web browser, it’s going to look like a webpage being hosted somewhere, but the host is actually just your local machine. That’s why you occasionally see localhost as a web address in your browser for the handful of things that go this route. I wouldn’t go any other route these days. It’s one one great power-play to take all the many webhosts who basically just want your money for hosting apps there, and if they’re not getting your money right away they’re locking you in some other way with your habits. Vercel is the great example today. Before that it was Heroku. Tomorrow it will be something else. They are not your friends. They are businesses trying to get your money, and if you could self-host in such a way where you could distribute your work so that others could reliably run your app in the same self-hosted way and nobody pays for anything, wouldn’t you?

I choose Nix for this. The details behind this will be documented copiously elsewhere in my technical journal, the eventual book I spin out of it, and all over the Web as more and more people discover the wonders of Nix.

Nix gives us a stable and reliable place to run Python — another stalwart of technology that changes only at such a glacial pace and so infrequently breaks backwards compatibility that anything you learn and do on Python will last for a very long, long time. So far we’ve got a standard code execution platform running on a normalized version of Linux which for all intents and purposes solves the not on my machine problem of local-hosting. You can easily write something that runs on your local machine but can you distribute it so that it runs on everyone’s machine that exact same way?

Yes.

And when you put Python on Nix you have Python running the exact same way on everyone’s machine.

This is the bedrock from which we build up to our spreadsheet solution.

Outputting to spreadsheet-like rows and columns is a very standard thing to do. In fact processing data in a spreadsheet-like row-and-column datatype is so common that the argument isn’t whether you can do it in Python, it’s whether you want your computational work to be optimized on a row-basis in which case you choose the very long-running and popular Data Science library called Pandas. If you want your data manipulations are a kind where columns are more important then you choose the less popular but growing Polars.

Why is this not part of the data-churn shitshow that the JavaScript world suffers with frameworks? For starters once an API like Pandas sets in, everyone else copies it. They use the same API. Learning Pandas is effectively the same thing as learning Polars which is essentially the same thing as learning Xarray — a third variation optimized for yet a different data-shape (ragged multi-dimensional arrays). With Python the skills and habits you develop with data science libraries like this will serve you for the rest of your life. And not in uncommon use cases either but for like everyday things like manipulating .csv files as you would in Excel. Over time using these libraries becomes even more comfortable than doing these things directly in a spreadsheet. You won’t believe what a relief it is to not deal with hidden embedded formulas that you have to hunt-and-peck for and anchor with dollar-signs and copy down columns. Spreadsheets are only an easy user interface at first glance. Over the years they take their toll on you by preventing you from using more programmatic methodical approaches.

“But coding in Python is hard!” perhaps you might say. “I don’t even know where to start!” you might say. “Is Python even installed on my machine and why would I even think about such a terrifying notion in this age of cloud-everything?” you might think in your mind but not articulate those exact words.

I install JupyuterLab side-by-side with Pipulate under Nix. So you install Nix then you install Pipulate and you automatically have a place to run Python side-by-side with Pipulate.

That’s because Pipulate doesn’t really come into play until after you’ve proven out some such thing in a Jupyter Notebook running in the Pipulate instance of Jupyter Lab first. You might know Jupyter Notebooks as just plain Notebooks such as with Google Colab, but that’s a vendor-trick tying to keep you from thinking too much about vendor-free Project Jupyter.

A lot like the other heroes of the tech-world who disprove the notion that if it’s free you are the product — such as Linus Torvalds, Richard Matthew Stallman, Guido van Rossum — Colombian physicist Fernando Pérez is one of the truly good guys of tech. Jupyter itech is free as in libre with very good licensing allowing embedding, so Google and Microsoft and a bunch of others embed it into their own products and rebrands the idea as generic Notebooks… FYI. But you don’t need the vendor-tech to get an awesome Python code execution environment. Just install Pipulate.

With Pipulate installed, you now have Pandas in Python also installed side-by-side for extremely casual Python programming. The big movement these days is N8N and more recently sim.ai to create workflows with a visual drag-and-drop interface. Everything is Yahoo Pipes. Everything is AmigaVision. Everything is icon-based authoring like Authorware. We’re talking mid-90s stuff and it probably goes back further than that, probably Apple Hypercard belongs in this category. And there were countless others like the Amiga’s CanDo! If you squint your eyes just-so even the Scala Multimedia, the software from the Norwegian software company I worked for for a bunch of years fits into this category. Drag-and-drop software keeping you from having to write scripts.

People like to think visually in a more abstract way than writing things out, but writing things out LASTS. It has durability. Platforms like AmigaVision, Authorware, N8N and sim.ai come and go. Python is forever. Jupyter Notebooks which run Python code comes somewhere in between, but the point of Notebooks isn’t that timeless resilience (although I do think it has it) but rather as being the bridge onto Python from things like Excel and Google Sheets.

What I’m doing with this next project may end up in Pipulate as a Web App and canned workflow that spits out near-finalized Excel spreadsheets replete with formatting and even embedded formulas (if it really needs them) but it’s going to start out as a Jupyter Notebook.

And the idea is to make the Pandas spreadsheet-like datagrids feel real right native in the Notebook. The idea is to conceptually bridge the gap and demonstrate the workflow processing. The Notebook has to be as much of a dazzling light show as a Pipulate workflow. It’s a spell-casting planner. It’s part of the sausage factory of making new spells. It’s an inventive process going from jotting a few ideas down on paper (code in a Notebook) to not only the finished spell but the canned and bottled demonstration of the use of that very spell. And in this dazzling demonstration is the pitch you don’t need Excel to do Excel-like stuff. In fact it’s even easier without it.

Sure when you’re happy with it you can import it into Excel, GSheets or whatever and put on whatever final little flourishes and finishing touches you like in that warm and cozy safety blanket environment of the graphical spreadsheet program, but you didn’t need to. You could have forwarded it on directly to the client — which is often dropping it in GSheets and sharing these days.

So my question to you Gemini is what are the best tools for visualizing a Pandas df-style datagrid directly in a Notebook? I will control the infrastructure with a flake.nix file so I could probably can what would otherwise be a fragile difficult to assure IPyWidgets environment. But don’t limit me to that. I’m looking for something timeless and flexible.

The Dazzling Show, Reimagined in a Notebook

Gemini: You’re looking for tools to make Pandas DataFrames in Jupyter Notebooks interactive and visually appealing, essentially creating that “dazzling show” for data right inside the notebook. The goal is to find a solution that is powerful but also aligns with your philosophy of using stable, future-proof, and non-proprietary “bedrock” technologies.

Based on your needs, the best tools fall into a few categories, from simple styling to fully interactive grids. The top contenders are Pandas’ own internal Styler API, ITables (for its simplicity and power), and ipydatagrid (for maximum control).

The Bedrock: Pandas Styler API

Before reaching for external libraries, it’s crucial to know that Pandas has a powerful, built-in tool for presentation. This is your most “timeless” option because it’s part of the library itself and requires zero extra dependencies. It doesn’t provide interactivity (like sorting or filtering in the UI), but it’s perfect for creating presentation-quality, static tables.

This directly addresses the need for craftsmanship by allowing you to create beautiful, readable outputs programmatically.

• What it’s good for: Conditional formatting, adding color gradients (heatmaps), embedding bar charts directly into cells, and controlling number formatting.
• Why it fits your philosophy: It’s the ultimate in stability—it’s part of Pandas. The skills you learn here are directly transferable and will never become obsolete as long as Pandas exists.

Example:

import pandas as pd
import numpy as np

# Create a sample DataFrame
np.random.seed(42)
data = np.random.randn(10, 4)
df = pd.DataFrame(data, columns=['A', 'B', 'C', 'D'])

# Use the Styler API to create a "dazzling" static table
df.style.background_gradient(cmap='viridis')\
        .bar(subset=['A', 'B'], color='#5fba7d')\
        .format('{:.2f}')

This code would produce a DataFrame where cells are color-coded based on their value, columns A and B have horizontal bars representing their magnitude, and all numbers are neatly formatted.

The 80/20 Solution: ITables

ITables is likely the closest thing to what you’re describing for an immediate “magic trick.” It connects Pandas DataFrames to the popular and robust JavaScript library DataTables.js. It hits that 80/20 rule you mentioned perfectly: with minimal effort (often just one line of code), you get 80% of the desired interactive features.

• What it’s good for: Instantly adding search, sorting, and pagination to your DataFrames directly in the notebook output. It’s lightweight and fast for small to medium-sized datasets.
• Why it fits your philosophy: It’s a simple wrapper around a well-established, open-source JS library. It doesn’t try to reinvent the wheel. It’s highly stable and just works. Your Nix environment can easily pin its version for perfect reproducibility.

Example:

import pandas as pd
from itables import init_notebook_mode, show

# Activate the interactive mode for all DataFrames
init_notebook_mode(all_interactive=True)

# Now, any DataFrame you display will be interactive
df = pd.DataFrame({
    'Rank': [1, 2, 3, 4, 5],
    'Team': ['Bulls', 'Lakers', 'Celtics', 'Warriors', 'Spurs'],
    'Wins': [72, 69, 67, 73, 67]
})

df # Just displaying the df renders the interactive table

You can also use show(df) for more granular control over options.

The Power User’s Choice: ipydatagrid

When you need more than just display—like editing data directly in the grid, handling very large datasets (millions of rows), or building more complex UI interactions—ipydatagrid is the answer. It’s built on ipywidgets and is designed for performance and flexibility.

• What it’s good for: Editing values, sorting/filtering massive datasets without crashing the browser, and integrating the grid as a component in a larger interactive dashboard within the notebook.
• Why it fits your philosophy: While it has more moving parts (being based on ipywidgets), it embraces the open-source spirit of Project Jupyter. Your Nix setup is perfectly suited to tame its dependencies, making it a reliable and reproducible power tool rather than a fragile one. It allows for deep craftsmanship in building data-centric UIs.

Example:

import pandas as pd
import ipydatagrid

# Create a sample DataFrame
df = pd.DataFrame({
    'Ticker': ['AAPL', 'GOOGL', 'MSFT'],
    'Price': [175.2, 2850.5, 340.8],
    'Change': [-1.2, 5.3, 0.7]
})

# Create the interactive grid widget
grid = ipydatagrid.DataGrid(df, editable=True, layout={'height': '200px'})

# Display the grid
grid

With this, you can double-click a cell in the output and change its value, with the changes being reflected back in the underlying DataFrame (grid.data).

Summary and Recommendation

Your quest for a stable, powerful, and timeless toolchain is a wise one. Here’s how these options stack up against your goals:

For your goal of bridging the gap for spreadsheet users and creating a compelling workflow, I’d recommend this approach:

1. Start with ITables. It will give you that immediate “wow” factor with almost no effort, making your notebook feel like a live application.
2. Use the Pandas Styler API when you’re preparing the final output for export or a non-interactive view. This is how you apply the final layer of polish and craftsmanship.
3. Keep ipydatagrid in your back pocket for when a project demands true spreadsheet-like editing capabilities or you’re working with a dataset that is too large for other tools.

This combination gives you a layered toolkit that respects your bedrock of Python and Nix while providing the flexibility to create truly dazzling and, more importantly, useful data experiences directly within your development environment.

Book Analysis

Ai Editorial Take

This is more than a journal entry; it’s a manifesto. It powerfully articulates a frustration felt by many experienced developers who have lived through multiple hype cycles and platform shifts. Its strength lies in its authenticity and the successful marriage of a high-level philosophical argument with a specific, practical, and modern technical solution. The piece champions sustainability and craftsmanship over fleeting trends, making it a valuable and resonant perspective in the current tech landscape.

Title Brainstorm

• Title Option: The Bedrock and the Dazzle: Building Timeless Tools in an Age of Tech Churn
  • Filename: the-bedrock-and-the-dazzle.md
  • Rationale: This title captures the core duality of the piece: the foundational philosophy (‘Bedrock’) and the specific, impressive goal (‘Dazzle’). It clearly frames the central conflict and solution.
• Title Option: Escaping the Churn: My Search for Craftsmanship in Code
  • Filename: escaping-the-churn.md
  • Rationale: Focuses on the primary pain point (‘Churn’) and the author’s personal motivation (‘Search for Craftsmanship’). It’s relatable and introspective.
• Title Option: From SEO Magic to a Future-Proof Stack
  • Filename: from-seo-magic-to-future-proof-stack.md
  • Rationale: Provides a clear ‘before and after’ narrative, showing a direct evolution from a specific past to a tangible future. It’s highly descriptive of the content.
• Title Option: Why Spreadsheets Will Never Die (And How to Build Beyond Them)
  • Filename: beyond-spreadsheets.md
  • Rationale: A provocative title that hooks into the universally understood concept of spreadsheets while promising a forward-looking solution, which is a key part of the article’s conclusion.

Content Potential And Polish

• Core Strengths:
  • A compelling, personal narrative that connects a relatable career crisis to a broader critique of the tech industry.
  • The ‘bedrock’ vs. ‘churn’ framing provides a strong, memorable philosophical core.
  • The argument for using a stable stack like Nix and Python is well-reasoned and serves as a powerful, concrete solution to the problems raised.
  • The ‘dazzling show’ metaphor is a fantastic narrative hook that ties the beginning of the story to its end.
• Suggestions For Polish:
  • The piece reads like a stream of consciousness. The proposed subheadings will add much-needed structure and guide the reader through the distinct phases of the author’s thought process.
  • Clarify the transition between the general lament about tech volatility and the specific focus on spreadsheets and datagrids to make the pivot feel more deliberate.
  • Consider adding a short, forward-looking concluding paragraph after Gemini’s response to summarize the chosen path and reinforce the commitment to the ‘bedrock’ philosophy.

Next Step Prompts

• Using the ‘book_analysis_content’, write a compelling LinkedIn post (under 250 words) from the author’s perspective that summarizes the article’s core message about tech churn and the search for a ‘future-proof’ development philosophy, ending with a link to the article.
• Expand the final section of the article into a standalone tutorial outline titled ‘Creating a Dazzling Show in Jupyter: A Guide to ITables and Pandas Styling’. The outline should include steps for setup, code snippets for basic interactivity with ITables, and examples of applying conditional formatting with the Pandas Styler API.