Intro by o1 Pro
Curious about a radically simplified way to build and automate data workflows—right on your own machine? This single-file “anti-pattern” framework does exactly that, keeping all logic local instead of forcing you into cloud dependencies or multi-layered architectures. Think of it as a mini web server plus integrated LLM plus HTMX-based UI, all in one Python file, orchestrating “cards” (steps) in a pipeline. You aren’t locked into enterprise tools or massive code scaffolding. Instead, you have full transparency and flexibility—spinning up workflows that can fetch, process, and visualize data (or hand it off to other tools if you like) without leaving localhost. The built-in AI can observe each pipeline step and even generate CRUD operations on the fly, so you spend less time wrestling with code and more time exploring what your data can do. If you’re looking for a nimble, hacker-friendly approach to building local data apps, welcome home.
The Story Behind Linkgraphectomy
This is not just a linkgraphectomy (the removal of some previous long-winded code), but it also the transplant of the cleaned-up version that I sparred back and forth with OpenAI ChatGPT o1 Pro to possess. So here’s the story:
Understanding Link Graphs
I can draw the link-graph, which is to the page-to-page link relationships, of a
website of enormous size. Most visualization tools konk out at maybe a few
thousand pages with a few tens-of-thousands of links between them. Network
graphs have nodes and edges. The nodes are the web pages themselves, and the
edges are the <a href="[link]">’s between them. There are far more links
than there are pages on any website, because one page can have many links. Then
there are reciprocal links going back, and so on.
A Long History of Link Visualization
I’ve been doing this trick long before it found its way into some of the commercial software out there. Popular Python libraries like NetworkX have been enabling it forever, plus open source packages like Gephi, Cytoscape and Graphviz have been doing it for a long time. And ever since the incredible D3.js JavaScript library from… uh… I’m thinking New York Times, but let’s check. Perplexity?
Perplexity: D3.js, which stands for Data-Driven Documents, is a powerful JavaScript library for creating dynamic, interactive data visualizations in web browsers. Here’s a brief history of D3.js:
Origins and Development
D3.js was created by Mike Bostock, who began developing it while working as a graduate student at Stanford University[1]. The library was first released in 2011 as an evolution of an earlier visualization toolkit called Protovis[1].
Key Milestones
- 2011: Initial release of D3.js
- 2012: D3.js gained popularity among data visualization developers
- 2016: Release of D3.js version 4.0, which introduced significant changes and improvements
- 2017: D3.js version 5.0 was released, further refining the library
Impact and Recognition
D3.js quickly became one of the most popular and widely-used data visualization libraries. It has been employed by major organizations and news outlets for creating interactive graphics and data-driven storytelling[1].
Philosophy and Approach
D3.js was designed with a focus on web standards, allowing developers to work directly with the Document Object Model (DOM) and use familiar technologies like HTML, SVG, and CSS. This approach provides great flexibility and control over the final visualization output[1].
The library’s success can be attributed to its powerful capabilities, extensive documentation, and a large, active community of developers and users who contribute to its ongoing development and share examples and tutorials.
Citations:
[1] https://github.com/walterra/d3-milestones
[2] https://www.youtube.com/watch?v=A8T3KghaipA
[3] https://github.com/denisemauldin/d3-timeline
[4] https://chancesmith.io/2019-11-12-javascript-timelines/
[5] https://codepen.io/manglass/pen/MvLBRz
[6] https://denisemauldin.github.io/d3-timeline/
[7] https://datyell.com/portfolio/timeline/
[8] https://d3js.org/d3-time
[9] https://stackoverflow.com/questions/13201749/draw-simple-timeline-with-d3
The D3.js Journey
Yeah, so a story like this often comes back to D3.js, and my story will eventually go there, because there’s nothing like the control of cobbling it together yourself using libraries that work at just the right abstraction level to spare you all the grizzly innards, but let you express data like poetry. Most things repackage D3.js, I believe even the package I love so much lately.
Scaling to Massive Graphs
But the fact is I can take some shortcuts to visualize link-graphs of immense size! We’re talking hundreds of thousands of nodes (pages) and millions of edges (links). And THAT’S what today’s work is about. Updating my code that takes those shortcuts to do that.
The DIY Approach
I work for the company I do in great part because tricks like this are no big for me. I write my own crawlers frequently. Writing a crawler is not some one-time thing, nor is it just taking up some canned package like Python’s Scrapy, which is amazing but is to scrapers what Django is to CMS/Web frameworks. Hmmm… Perplexity?
Perplexity: Scrapy, a powerful web crawling and scraping framework written in Python, has an interesting history that spans over a decade. Here’s an overview of its development:
Origins
Scrapy was born at Mydeco, a London-based web-aggregation and e-commerce company[1]. The initial development was carried out by employees of Mydeco and Insophia, a web-consulting company based in Montevideo, Uruguay[1].
Key Milestones
- 2008: Scrapy’s first public release was made in August under the BSD license[1].
- 2011: Zyte (formerly known as Scrapinghub) became the new official maintainer of Scrapy[1].
- 2015: Scrapy reached its milestone 1.0 release in June[1].
Creation and Open-Sourcing
The story behind Scrapy’s creation is quite interesting. Pablo, who graduated in computer science and electrical engineering in 2007, set up Insophia, a Python development outsourcing company in Montevideo, Uruguay[4]. He began working with Mydeco’s web scraping team and helped develop their data processing architecture[4].
Recognizing the potential of the web crawler code, Pablo approached Shane (presumably a colleague at Mydeco) about open-sourcing it[4]. This led to the birth of the Scrapy project, cleverly named by combining the words “Scrape” and “Python”[4].
Evolution and Popularity
After its initial release, Pablo spent months refining Scrapy and releasing updates[4]. The framework gained popularity, and word spread that several high-profile companies, including a major social media platform, were using the technology[4].
Recent Developments
Scrapy has continued to evolve over the years:
- 2023: Scrapy 2.10.0 was released, adding support for Python 3.12 and dropping support for Python 3.7[2].
- 2024: The latest version, Scrapy 2.12.0, was released on November 18, 2024. This version dropped support for Python 3.8 and added support for Python 3.13[2].
Today, Scrapy is maintained by Zyte (formerly Scrapinghub), a web-scraping development and services company[1]. It has become a go-to tool for developers working on large-scale web scraping projects, offering an extensive package that simplifies data extraction, processing, and storage[3].
Citations:
[1] https://en.wikipedia.org/wiki/Scrapy
[2] https://docs.scrapy.org/en/latest/news.html
[3] https://oxylabs.io/blog/scrapy-web-scraping-tutorial
[4] https://www.zyte.com/blog/history-of-zyte-formerly-scrapinghub/
[5] https://docs.scrapy.org/en/latest/intro/overview.html
The Limitations of Powerful Libraries
These powerful libraries are wonderful and everything, but they really isolate you from so many important things you need to know about website crawling. Such things are on rails and philosophically laden. If the rails provide precisely the ride you need and you can live with the philosophically imposed separation from the nitty gritty, then fine. Big projects can get done faster. But not for me. I barely like using BeautifulSoup over pure RegEx and dirty XML parsers… but more on that geek-talk later. Back to link-graphs, and their visualization.
Planning the Visualization Journey
I am planning my course to take me full-circle back around to very primitive hierarchy visualizations with D3.js, but I’m taking a fairly huge detour due to where I work and what kind of data I have available to me as a result. I work at a place that creates industrial crawlers, and as such, the kind of link-graph exports I can do are massive. Currently, the API limitation is 1-million rows on the export. I have ways of getting around that, but for now I’m just leaning into that limitation.
Working Within Export Limits
The idea of accepting the 1-million row export limit is that can always frame the discussion that this visualization software can only show so much, so limiting ourselves somewhere is reasonable, and we can generate multiple different visualizations to compensate for not having the full true link-graph at once, which is reasonable because some of these ecommerce sites with millions of pages would have trillions or hundreds of trillions of edges. There is honestly not much utility in this but for expressing the futility of it. Merely knowing you’ve got millions of pages tells you that you’ve likely got a spider trap problem (the ability for your site to generate infinite pages), and that has to be fixed before a deep visualization is meaningful.
The Value of Shallow Visualizations
Shallow visualizations are still always useful. You can see the shape of the site take shape, at 1 click-depth, at 2 click-depth and so on. You can see pages that cluster together, topically related by nature of the way that they cross-link (hopefully). And if you can’t see these topical clustering shapes occur, it shows you that you’ve got another problem. And most of these problems most often stem from some search-friendly search function on the website such as a faceted search interface to select colors and sizes of products, which can work together in infinite combinations, in infinite orders, in infinite upper and lower-case (inadvertent) usage, resulting obviously in infinite pages. Peeling away such layers and showing where the spider-traps set in with progressively deeper crawl-depth visualizations is a useful starting point.
Tool Development Strategy
My tool should therefore really show at 0 click-depth (the homepage before having followed any links), 1 click-depth, and so on so you can see the progression of exponentially exploding link-graph complexity. But for now, I just find the maximum click depth that fits within an export and grab that. I need to do the basics before all the advanced features. But what I’ve found was that even the basics, without even having to perform the crawl myself due to how I’m exporting it through the API of an industrial crawler product, is still complex!
Export Process Challenges
I have a manual way of doing it through the product. But it’s a convoluted series of steps that’s very hard to get right every time. I seem to be the only one able to do it, aside from the very deep product specialist (thanks, Laura!) who taught me how to find the data in the product. Doing this export is thus a perfect candidate for automation.
Enhancing Data Visualization
What’s further, even after that complex data export, you need to export yet more data to turn the link-graph into more of a sort of diagnostic radiology medical image like a CAT scan or a PET scan. That’s because every node on the link-graph can be both sized and color-coded according to any metric you happen to have, and the product I’m working with has LOTS of metrics! So there are lots of color-coded variations to produce.
The Non-Deterministic Nature
And what’s further still is that even after you’ve exported both the link-graph and the what we’re going to call “meta” data (yeah, SEO I know, so many meta’s!), the network visualization software is going to use a method to draw it that never turns out exactly the same every time. I know, it should be deterministic, but with that many datapoints and the force-graph process it uses to settle the nodes down to their optimum locations relative to each other never turns out exactly the same way twice.
Alternative Visualization Methods
Don’t get me wrong. There are other ways of visualizing link-graphs which are deterministic. It’s just I happen to be using one that’s blazingly fast and fun to watch that is apparently non-deterministic, resulting in these interactive hologram like things that make you feel like Admiral Akbar showing the air duct vulnerability of the Deathstar to the Rebel forces, I kid you not. It’s that cool, and I don’t know why it’s not the biggest thing in SEO. Everyone else is caught up on these tiny link-graphs at extremely small click-depths of small crawls. I’ll get to those too, because they have their own sort of value. That’s where I want to control things precisely with the D3-force stuff.
Automation with Jupyter Notebooks
When it got time to automate the convoluted link-graph and meta data exports from this API, I turned to as I almost always do in these situations to Jupyter Notebooks. In particular, I use JupyterLab stand-alone version installed on my local machine. It’s a special Electron-wrapped version of what otherwise runs in a tab of your browser. However, that mode is often a challenge because you have to get a little server infrastructure going, and the process varies between Macs and PCs. If you’re on Linux, it’s much easier with the apt/yum installers. And I guess on the Mac, there’s the homebrew approach. But even so, the install story for Jupyter itself is convoluted, which is one of the many things that drive people to either VSCode or Google Colab.
The SEO Community’s Approach
Most SEOs out there you’ll find are either of the Google Colab variety when it comes to running Python SEO code, or they’re of the VSCode variety. Almost nobody at least whose talking runs things the localhost way, native on your machine and without the big vendor lock-in traps. There’s a whole article here really why browser automation and usage of VPN, two critical aspects of Python SEO, are so terrible on Google Colab. Oh, and long-running scripts! Wow, the fact most SEOs are going to either perpetually struggle with this stuff or limit what they can do is such a competitive advantage for those who can take that extra step to running things local.
My Journey with JupyterLab
And so obviously, I run my Python code local. And I started out doing so in JupyterLabs standalone version. But it didn’t take me long to see the limitations of that when trying to share my work with coworkers. It’s very alluring, and… uh, there’s a history here. Perplexity, tell me the history of JupyterLab standalone version. I’m not talking the apt/yum/brew installable version, but the one that has its own native platform installer on each platform, which uses Electron to make it standalone. It’s sometimes called the Github Desktop version of JupyterLab.
Perplexity: The standalone version of JupyterLab, often referred to as JupyterLab Desktop, is a cross-platform desktop application based on Electron. Here’s a brief history of its development:
Origins and Initial Release
JupyterLab Desktop was created to provide a more accessible and user-friendly way to use JupyterLab without the need for command-line installation or setup. The project was initiated in 2017, with the first public release made available on GitHub[1].
Key Milestones
- 2017: Initial development and release of JupyterLab Desktop[1].
- 2021: A significant update was made to modernize the application after a couple of years from the initial release[3].
- 2023: JupyterLab Desktop v4.0.12-1 was released, introducing major improvements in Python environment management[5].
- 2024: The latest version, v4.2.5-1, was released, upgrading to JupyterLab v4.2.5[5].
Features and Evolution
Over time, JupyterLab Desktop has evolved to include several key features:
- Self-contained Application: It bundles a Python environment with popular scientific computing and data science libraries[3].
- Cross-platform Support: Available for Windows, macOS, and Linux (Debian and Fedora-based distributions)[3].
- Built-in Python Environment: Uses a bundled conda Python environment with libraries like numpy, scipy, pandas, ipywidgets, and matplotlib[3].
- Environment Management: Later versions introduced improved Python environment management, allowing users to create, manage, and switch between different environments[5].
Recent Developments
Recent updates have focused on enhancing user experience and keeping up with JupyterLab core developments:
- Introduction of UI modes
- Support for setting up the jlab CLI from the UI on macOS
- Notifications for bundled Python environment updates
- Upgrade to Python 3.12 in newer versions
JupyterLab Desktop has become a popular choice for users seeking a quick and easy way to get started with Jupyter notebooks on personal computers, while still offering flexibility for advanced use cases[1].
Citations:
[1] https://github.com/jupyterlab/jupyterlab-desktop
[2] https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html
[3] https://blog.jupyter.org/jupyterlab-desktop-app-now-available-b8b661b17e9a?gi=c221a0a51adc
[4] https://www.redhat.com/en/blog/install-jupyterlab-linux
[5] https://github.com/jupyterlab/jupyterlab-desktop/releases
[6] https://stackoverflow.com/questions/40694528/how-to-know-which-python-is-running-in-jupyter-notebook
[7] https://jupyterlab.readthedocs.io/en/stable/getting_started/changelog.html
[8] https://academy.dnanexus.com/interactivecloudcomputing/jupyterlab/running_notebook
[9] https://releases.electronjs.org/releases/stable
The JupyterLab Desktop Journey
Uh yeah, so I jumped on that bandwagon for awhile. Then I realized it still didn’t solve the “not on my machine” problem that drives everyone to the cloud (Google Colab) or into the more coddling hands of Microsoft (VSCode).
There had to be a better way. I new that if I had this itch so frequently for so many years, really decades on end, that some higher powers must have scratched that itch.
And so they have!
The Docker Dilemma
Docker, you say? Nope! There’s at least 2 critical flaws with Docker!
First, you need Docker installed and know how to use it. It’s not merely install Docker. No, there’s a multi-tiered mess that includes but is not limited to installing and learning Docker desktop. Docker, and containers in general, are just not the cure-all they’re made out to be. They bottle complexity so you don’t have to deal with install and configuration procedures, but they add their own sort of complexity that’s not nearly as simple as people make out.
Second, even once you figure out how to get Docker installed and use it, if it’s a complex application that requires mix-and-matching components, now you have to mix-and-match Docker images figuring out how to make them talk to each other, and all the complexity and weird security context that entails. It’s called Docker composing, and if you thought the first thing people gloss over, Docker’s install and education procedure, wait until you try to do something fancy and have to deal with image composition!
I could go on to third, fourth and fifth “gotcha’s” concerning Docker, such as the needless file-bloat this all causes, and lack of actual deterministic system builds due to the “outer” OS, but I’ll spare you.
Enter Nix and NixOS
Suffice to say I found the solution.
It’s Nix and NixOS.
No, that’s not just another Linux distro, although it is also that. More importantly, it is a single-file-based deterministic server-building language.
Let me say that again in a different way. With one single file, you can say “build me this system”. I say server only because inevitably you’re doing this because serving some service or other is the whole purpose. But really what nix/nixOS does is just to build generic systems, which are operating systems plus the programs and apps properly installed so that they run.
Cross-Platform Magic
And it can do this within any folder of a Mac or Windows PC! Yes, you heard that right. A deterministic (meaning it solves the “not on my machine” problem) full system can be built as a subsystem within a folder (aka directory or repo) of either of the mega-popular consumer desktop platforms: Macs & Windows!
The Linux Victory
I say consumer desktop platforms, because from a professional (non-consumer) sense, Linux already won. If you have any doubts, look at how Microsoft had to add Linux (WSL) to Windows to remain a viable choice for developers. And Apple only dodged the bullet from having switched the Mac from a proprietary underlying OS to Unix circa 2007, which is close enough to Linux that a few tools like Homebrew bridge the gap—providing the kind of free and open source software repository that’s common on Linux but not so much on Unix.
Yup, Linux won.
The Three Faces of Nix
And nixOS is as much the nix program as it is the version of the OS built out of it called nixOS. It’s weird and circular, but fixing the “not on my machine” itch was scratched with a floor wax and a desert topping! People from my generation will get the reference. Nix is no less than 3 things:
- A language for building systems declaratively and deterministically
- A full operating system built using this language
- A version Macs and Windows can use to get this same nix magic in subfolders
The Paradigm Shift
It takes awhile to wrap your head around this, and precisely why this kills Docker in all needs but for snooty high fallutin enterprise use.
Kubernetes no more! Even the lightweight versions of this stuff that people like me try to use to cobble it together at home like k8s and Podman are no longer necessary. I was even infatuated with Ubuntu’s attempt to mainstream containers with LXC/LXD, but I have since come around.
The Future of System Building
Linux won and everybody in the know really knows it, and nix/nixOS won but
nobody at all knows it (yet). And to be fair, the GNU Free Software Foundation
already jumped on the nix bandwagon, validating it even further by creating
their own version called Guix (pronounced geeks) that I would just as well be
on were it not for a very inside Mac crowd (we’re talking the elite of the elite
of the elite) having made the Mac experience with Nix so friggin solid. It’s
called the Deterministic Installer from Determinate Systems.
The Military-Industrial Connection
Where did this come from? Well, apparently of the inside crowd taking up Nix, there are certain military industrial suppliers who had to scratch this same itch, and they don’t mess around. So this stuff is funded and supported. It’s still fully free and open source software and doesn’t look like it’s going the way of Redis, VirtualBox, MySQL, MINIX, Terraform and others. But even if it does, it’s only some AI-assisted conversions to port your system definition files from nix to guix! So bets are hedged! Deterministic system builds are the future, one way or the other.
The Anduril Story
Hmmm, there’s some fine points here that you may not be convinced of yet. Okay, let’s say an ex Facebook employee who created the Oculus named Palmer Luckey who created a new military industrial company from scratch called Anduril. Now lets say they put together a team who are the smartest of the smart and think outside the box for the best solutions as a matter of their DNA. Think Elon Musk, but not shy to make war machines. They’re all macOS-using elites, but they can’t stand such a vendor dependency, and they all know Linux really won, like everyone else. But nobody can stand actually being on Linux laptops. It wouldn’t suit their egos. They must be on Macbooks! Yet, there’s the not infrequent stubborn Windows-using holdout on their teams who validly justify their dogma by pointing out WSL provides more legitimate Linux on Windows than Mac’s Unix is on Mac. I mean Homebrew, really? It’s not even officially from Apple and how its named says it all. Unprofessional!
But what is professional?
Nix!
And all it needs is a deterministic, solid installer for macOS.
The Deterministic Installer from Determinate systems.
Me: Perplexity, does Andruil use nix?
Perplexity: Yes, Anduril does use Nix and NixOS extensively in their operations. Here are key points that demonstrate Anduril’s involvement with Nix/NixOS:
-
Anduril has been a strong member of the NixOS community for over 5 years, contributing to various projects and developments[8].
-
The company has sponsored efforts like the Nix Documentation Project and CUDA support for NixOS[8].
-
Anduril has open-sourced important developments, including support for various Nvidia devices in NixOS[3].
-
They maintain a GitHub repository called “jetpack-nixos” which packages components from NVIDIA’s Jetpack SDK for use with NixOS, specifically for NVIDIA Jetson devices[10].
-
Anduril has actively recruited NixOS developers to work on their robotics team, indicating their use of NixOS in their products[4].
-
The Defense Information Systems Agency (DISA) has released an Anduril NixOS Security Technical Implementation Guide (STIG), suggesting Anduril’s NixOS implementations are used in defense contexts[5].
-
An Anduril employee was serving as the release manager for NixOS 24.05 before a controversy arose[7].
These points clearly show that Anduril not only uses Nix/NixOS but is also deeply involved in its development and application in the defense technology sector.
Citations:
[1] https://www.builtinseattle.com/job/nixos-software-engineer/83037
[2] https://news.ycombinator.com/item?id=37413756
[3] https://www.theregister.com/2023/09/08/nixcon_drops_anduril_industries_as/
[4] https://www.reddit.com/r/NixOS/comments/14xqq7b/anduril_nixos_developer/
[5] https://public.cyber.mil/announcement/disa-releases-the-anduril-nixos-security-technical-implementation-guide/
[6] https://discourse.nixos.org/t/should-organizations-relating-to-the-defense-sector-being-able-to-sponsor-nixos/41252
[7] https://chrismcdonough.substack.com/p/report-on-nixos-governance-discussions
[8] https://x.com/PalmerLuckey/status/1699590452847272044
[9] https://www.reddit.com/r/NixOS/comments/1cfbz26/should_nisos_have_accepted_the_anduril_sponsorship/
[10] https://github.com/anduril/jetpack-nixos
And thus Nix remains FOSS, perhaps not as incorruptibly pure as The Free Software Foundation (the Richard Matthew Stallman / GNU folks) and maybe not as pure as Debain… what? You don’t know about that? Perplexity, what’s the organization behind Debian? And how incorruptible is it versus other major FOSS?
Perplexity: Debian is governed by a decentralized, democratic structure that aims to distribute power and decision-making across its community of developers. This organizational model has several key components:
-
Debian Project Leader (DPL): Elected annually by Debian developers, the DPL represents the project externally, manages relationships with other organizations, and provides overall leadership[2][6].
-
Technical Committee: Elected by developers, this group resolves technical disputes and makes binding decisions on technical matters[2][3].
-
Constitution: The Debian Constitution outlines the project’s principles, procedures, and decision-making processes[2].
-
General Resolution Protocol: Allows developers to make project-wide decisions through voting[8].
-
Debian Developers: Full members who contribute to the project and have voting rights[5].
Regarding incorruptibility, Debian’s structure offers several safeguards:
-
Annual leadership elections prevent long-term concentration of power[2].
-
The DPL’s powers are strictly constrained, with most authority delegated to others[4].
-
Decision-making is distributed across the community, reducing the impact of individual influence[6].
-
The Technical Committee provides checks and balances on technical decisions[2].
-
Transparent processes and open communication channels promote accountability[1].
While no system is entirely incorruptible, Debian’s governance model provides strong protections against centralized control or corruption. Its long-standing commitment to democratic principles and free software ideals has contributed to its reputation for integrity in the FOSS community.
Citations:
[1] https://www.debian.org/intro/organization
[2] https://moldstud.com/articles/p-how-is-the-debian-project-governed-and-who-leads-the-development-process
[3] https://wiki.debian.org/SystemGroups
[4] https://blog.liw.fi/posts/2023/debian-reasons/
[5] https://www.debian.org/devel/
[6] https://lists.gnu.org/archive/html/libreplanet-discuss/2024-01/msg00004.html
[7] https://www.linux.org/threads/debian-file-structure-and-layout.10649/
[8] https://wiki.p2pfoundation.net/Debian_-_Governance
The Staying Power of Nix
So yeah, while Nix might not be as pure as Debian or GNU, it has entered the realm of “too big to fail”. So if you think about it, the thing that solves the “not on my machine” problem has entered the realm of things like Linux, Python, vim and git which are still defensively licensed and governed to remain true FOSS, but are too entrenched in important work to fail.
Characteristics of Timeless Tools
This is the hallmarks of a timeless tool. This is where you want to align your skills so that they always at least apply as one of the alternative approaches to almost any problem in almost any problem domain, and cannot be taken away from you through the rapid obsolescence cycles of profit-driven proprietary endeavors that force you to upgrade, retrain and ostensibly keep pace with progress.
A Brief Tangent on Future-Proofing
Did I go on a tangent there from the linkgraphectomy? Yeah, I think I did. But
so what? This is my daily tech journal! This is where I ramble about the
actually important things just like this. This is the fodder for the book on
future-proofing yourself in the world of tech and the emerging landscape
dominated by AI. This is like taking a deep breath and reminding myself why I’m
doing it all—to remain a valid, legit and relevant craftsman in the field
of tech as I become a decrepit old man. I’m one of those people who can tell you
why when this sort of thinking that asserted itself asserted itself with XML, it
didn’t pan out. But then JSON did. And why YAML’s more popular than TOML and how
Markdown won over .rst files (reStructuredText)… oh, such interesting stuff
and all about that future-proofing story. Perplexity?
Perplexity: The common file extension for reStructuredText is .rst[1][4].
reStructuredText (RST, ReST, or reST) has a rich history in the Python community:
- Origins:
- Evolved from an earlier markup language called StructuredText, developed by Jim Fulton of Zope Corporation in 1996[1][5].
- Designed to address problems with StructuredText[1].
- Development:
- First released in June 2001[1][2].
- Began to see significant use in the Python community in 2002[1].
- Inspired by elements from Setext (early 1990s), RFC822 Internet Message Format, and Javadoc[1].
- Key milestones:
- November 2001: reStructuredText parser nearing completion[2][5].
- Early 2002: Development shifted to other Docutils components[2][5].
- April 2002: Version 0.4 of reStructuredText and Docutils projects released, then merged into “Docutils”[2].
- Adoption:
- Became widely used for Python documentation, both for the language itself and Python libraries[7].
- Preferred by some developers due to its well-defined markup standards compared to Markdown[7].
- Subsumption to Markdown:
- While reStructuredText predates Markdown (created in 2004), Markdown eventually gained more widespread popularity due to its simplicity[3].
- Markdown became the preferred format on platforms like GitHub, superseding reStructuredText in many contexts[3].
- Some projects now use a common subset of markup shared by both reStructuredText and Markdown to allow for easier conversion and compatibility[6].
- Tools like Pandoc have been developed to convert between reStructuredText and Markdown, facilitating the transition for many projects[6].
Despite being overshadowed by Markdown in general usage, reStructuredText remains an important format in the Python ecosystem and for technical documentation, valued for its extensibility and semantic markup capabilities[7][9].
Citations:
[1] https://en.wikipedia.org/wiki/Restructured_text
[2] https://docutils.sourceforge.io/0.6/docs/ref/rst/introduction.html
[3] https://www.dewanahmed.com/markdown-asciidoc-restructuredtext/
[4] https://fileinfo.com/extension/rst
[5] https://docutils.sourceforge.io/docs/ref/rst/history.html
[6] https://gist.github.com/dupuy/1855764
[7] https://www.writethedocs.org/guide/writing/reStructuredText/
[8] https://docutils.sourceforge.io/docs/ref/rst/introduction.html
[9] https://docs.espressif.com/projects/esp-docs/en/latest/introduction/restructuredtext-vs-markdown.html
[10] https://docutils.sourceforge.io/rst.html
[11] https://learnxinyminutes.com/rst/
Avoiding Distractions
Oh man, don’t get me started on SGML! And this is all really a distraction to keep me from getting started on the linkgraphectomy. It’s like when I consulted with o1 Pro in preparation for the job, it didn’t want to drill-down on the details, either. There’s just such a large lump of ugly code being replaced, and once I start, I don’t stop until I’ve dealt with every unexpected rabbit hole I walk into. So, be alert for the rabbit holes and don’t get trapped in an unexpected wonderland, no matter how wonderful.
The Process
There’s a way of psyching yourself into starting the work. And once you do, there’s a meticulous way of working to keep the code from breaking, except it’s going to get a lot uglier (longer) until it gets prettier (shorter).
Git Workflow Philosophy
And there’s some forking decisions to make. I don’t think that I want to fork. I
am a linear thinker, no matter how much I might seem non-linear, nope. I forked
Pipulate into Botifython. I had to do that to justify working on it during the
working business day, because even though it was born FOSS SEO, there’s the
appearance of propriety, so I forked a version with a more proprietous name. And
even then, I tried further branching, and hated it. I can’t stand branching.
There’s a whole realm of git usage that I have hardly explored with checkouts
and cherrypicking. But I’m more a forward-only trunk-based development sort of
person making heavy use of git reset --hard HEAD (replacing git hashes for
HEAD). It’s a two-steps forward, one-step back sort of workflow more like Roomba
backing up a bit, turning and trying again, rather than creating a visual map of
the entire room. It’s not the smartest way, but I’m not the smartest person.
And so… and so… and so… we finally force ourselves with a clear vision of what we’re doing to get started.
Part 2: Performing The Surgery
Starting Fresh
And it is now Saturday, December 28th. This is going to be an epic 2-day post, because I couldn’t get myself over the hump yesterday. But that’s fine.
Progress So Far
The actual work is mostly done, and was my o1 Pro stress test.
Mental Barriers
So, I’ve just got to get into it. The problem here is a mental hurdle. It’s been so herculean to get all this stuff this far, that it feels like a continuation of wrestling the hydra; a task done and completed, not to be revisited for the exhaustion I have been conditioned to expect.
Finding The Love
That’s when you back up and mentally refactor— to find the love.
It’s not about powering through large chunks of code risking breaking everything, which is what it feels like at first.
Getting AI Assistance
Okay, o1 Pro helped me get ready for this work. I’m going to tap it again for ideas to keep in mind while I proceed to up the adoration.
Me: Absorb this entire codebase and make utterly non-breaking suggestions on how to improve it for usability and design elegance.
o1 Pro: Below are non-breaking suggestions you can safely integrate for improved usability and design elegance, based on reading and “absorbing” the entire codebase. All of them respect the “single-file ethos” and “local software only” principle you’ve expressed, while making the code easier to navigate, extend, and debug.
1. Module-Level Structuring / Organization
Why
Though you’ve embraced the “single-file, single-tenant” pattern, some gentle groupings can prevent cognitive overload and help future tinkerers find specific sections quickly.
Non-Breaking Suggestion
- Add A-Z or Section Headers: You already have ASCII “figlet” banners—keep them, but consider more consistent naming/ordering in the file’s Table of Contents (for instance, an explicit “Search for # =Routes= or # =LLM= to jump to that section”).
- Gather Reusable Utilities: A small, well-labeled “helper functions” region near the top, e.g.:
# ----------------------------------------------------------------------------------------- # Helpers & Utilities # ----------------------------------------------------------------------------------------- def some_useful_helper(...): ...This keeps ephemeral logic out of critical path.
- Inline Explanatory Comments: Some sections like
execute_crud_operationorextract_json_objectsare fairly big. Small inline commentary can clarify each step (not just docstrings).
Impact: 0% chance of breakage—purely organizational, all in-file.
2. Consistent Logging Patterns
Why
You use logger.debug, logger.error, etc., plus fig(...). They’re spread around in a workable way, but some minor improvements can unify them.
Non-Breaking Suggestion
- Establish a Common Logging Format:
- Always start new “debug zones” with a small ASCII banner or a
logger.debug("=== Starting XYZ ==="). - End with
logger.debug("=== Finished XYZ ===").
- Always start new “debug zones” with a small ASCII banner or a
-
Prevent Over-Logging: Some logs might be repetitive. For instance, after toggling an item, you log a debug statement plus an entire insertion. Consider limiting repeated logs if they’re not offering new insight.
- Use More
logger.info(...)for high-level events, anddebugfor low-level.
Impact: Minimal. You can keep all the logs, but unify them to reduce noise.
3. Conversation History Handling
Why
append_to_conversation and the global global_conversation_history approach is great for a local single-tenant system. But a few small improvements can clarify usage.
Non-Breaking Suggestion
- Add a “Max Tokens” or “Auto-Trimming” Mechanism: Currently you have a max length, but you might also do “if new data length + total length > X, chunk out old messages.”
- Create a Single
conversation_manager.py-like Structure: If you want to remain single-file, just rename the large block aroundappend_to_conversationto# Conversation Manager.
Impact: Doesn’t break the existing usage.
4. Better JSON Extraction Handling
Why
extract_json_objects uses a single powerful regex, which you specifically note not to modify. That’s fine, but the error-handling can be more instructive.
Non-Breaking Suggestion
- Add a Single “We Found X JSON Objects” message in the UI** or log. You do log it as debug, but you might also SSE-broadcast that count for debugging.
- Partial Recovery: If you catch a
JSONDecodeError, currently you skip that chunk. Possibly log the chunk with a “did you forget a bracket?” style message.
Impact: Minor improvements only.
5. Emphasize the “Pipeline” Concept with a UI Helper
Why
Your pipeline approach is a neat “cards” pattern, but each pipeline-based app (e.g., Poetflow, Workflow) does a similar step pattern. A small mini-UI or “Wizard” helper could unify these flows.
Non-Breaking Suggestion
- Wizard-Style Snippet: A function
render_wizard_steps(steps, current_step)returning a card with bullet points for each step, showing done/pending states. You partially do it, but you can factor it out. - Add a Quick “Step Summary”: For each pipeline-based route, you display the next step. You could also put a mini “You are on Step 2 of 5” text.
Impact: Just structural sugar—won’t break the existing route logic.
6. Expand DictLikeDB for LLM “Cookie” Use
Why
You have a robust approach already—DictLikeDB. But the LLM could optionally store short “facts” or “user preferences.”
Non-Breaking Suggestion
- Add Sub-Keys: For instance, if you want the LLM to store “facts” in a key = “llm_facts”, you can do:
facts = json.loads(db.get("llm_facts", "{}")) # manipulate facts db["llm_facts"] = json.dumps(facts) - Add a
set_factsandget_factsfor the LLM**: Just pure convenience wrappers around that.
Impact: Zero breakage, purely additive.
7. Routing & Menu Handling Simplifications
Why
Your “MENU_ITEMS” approach works well, but at times it can get cluttered if the menu grows.
Non-Breaking Suggestion
- Automated / Reflection-based Menu: Because each plugin is a class implementing a
.render()method, you could reflect them. But that might be too fancy for your style. - Use a Single
dictMapping Menu Key -> “the plugin’s function to render.” E.g.:ROUTE_RENDERERS = { 'client': profile_render, 'task': todo_render, ... }Then in
create_grid_left, just doROUTE_RENDERERS[menux](...).
Impact: Minimal, might reduce if-else blocks but still single-file.
8. Better SSE + WebSocket Explanation
Why
You do a great job using SSE for unidirectional server->client and websockets for two-way. Some small improvements can make the code clearer.
Non-Breaking Suggestion
- Comments: Specifically mention that SSE is used for “push updates for tasks,” while WebSocket handles “interactive chat messages.”
- In SSEBroadcaster, store a last-sent message if you want to do replay or ensure new clients see the last message.
Impact: Pure doc improvement.
9. Try a “dev mode” Toggle or Env Var
Why
So you can hide some debug logs or skip certain validations while developing locally.
Non-Breaking Suggestion
IS_DEV = Truenear top, used as a condition:if IS_DEV: # show more logs- Hide Certain “Test Patterns” when
IS_DEV=False.
Impact: Doesn’t break existing usage, just adds an env variable or a global boolean.
10. Micro-Refinements & Polishes
- Search: Right now you have a
searchroute. If you plan on expanding, consider returning a more structured message if nothing found. - HTTP Methods: Many routes do
rt(..., methods=['POST']). If you also want GET in some scenarios, just safely add it:[‘GET’, ‘POST’]. - Docstrings: Some docstrings are great, some are missing. For instance,
start_form()inPoetflowis well documented, butshould_advance()has minimal. Even short docstrings help. - Use of
asyncio.create_task(...): Where you doasyncio.create_task(chatq(...)), you might want to store references or check for errors. Usually not critical, but could be nice if you want to handle exceptions.
11. “Intro Card” or “Landing Page” Crispness
You have introduction.introduction_render(). You might unify it with the “home” route or give it a slightly snappier welcome:
- Add a Quick “What’s Next?” bullet list: “Pick a profile,” “Add tasks,” “Try a pipeline.”
- Simplify the mention of disclaimers for new users.
Impact: Pure UI text changes—no breakage.
Conclusion
None of these suggestions require major refactoring or splitting into multiple files. They’re mostly about:
- Better organization (internal section headings, consistent logging).
- More elegant expansions (small wrapper functions, a “wizard” or “render step” helper).
- Minor doc/comment clarifications in big areas (CRUD, SSE, WebSockets).
By weaving in these small changes, you’ll enhance readability and maintain the local, single-tenant, all-in-one spirit of the codebase.
I can feel the pull of AI-dependency. I post the Linkgraphectomy plan to o1 Pro with the following prompt:
Me: Okay, I very much like these suggestions. You reminded me I had to remove the Workflow class as it was not updated to reflect the advances in Poetflow and Bridgeflow, which represent the latest and greatest pipeline workflow advances. Also, my next step is replacing BotifyLinkGraph with the work described (and planned by you) in the following article. I will keep these other beautification steps in mind, but help me get started carrying this out. I think I’ll keep BotifyLinkGraph in the code while I implement Linkgraph, so we will have to be careful of naming collision with the global helper functions. But besides that, this should almost be a rote job.
o1 Pro: Below is a step-by-step outline to coexist with your old BotifyLinkGraph code while you add your new LinkFlow (or LinkGraphFlow) to the codebase. We’ll carefully avoid naming collisions in global helpers by giving the new functions distinctive names (e.g., _fetch_analyses_light_new, create_export_job_new, etc.) or by placing them inside the LinkFlow class if you prefer. You can always unify them later. This outline walks through the main steps to implement the new pipeline-based flow while keeping the old BotifyLinkGraph alive until you’re ready to remove it.
1. Prepare the New Global (or Class-Scoped) Helpers
A. Renaming Shared Functions
If you still need to keep the old functions (from BotifyLinkGraph) around, rename your new helpers to avoid collision. For example:
async def fetch_analyses_light_new(org: str, project: str) -> list:
"""Similar to BotifyLinkGraph._fetch_analyses but uses new approach."""
...
async def find_optimal_depth_new(org: str, project: str, analysis: str, max_edges=1000000):
"""Same logic as your old function, but renamed to avoid collision."""
...
def download_file_new(url: str, save_path: Path, logger=None) -> Path:
"""Like download_file but doesn't clash with old one."""
...
async def create_export_job_new(payload: dict, logger=None) -> str:
"""POST /jobs (new version) without overwriting old version."""
...
Implementation details: You can literally copy/paste your old logic from BotifyLinkGraph, just rename the function to ..._new to keep both versions in the file. Then your new pipeline class will call the _new versions, while BotifyLinkGraph can keep calling the old ones until you remove it.
B. Distinguishing Variables
- If your old code references
api_keyorusernameat global scope, keep them there for the old class. For the new pipeline flow, you can read them anew from abotify_token.txtor store them in local variables. - If you have naming collisions in your old code (like
parse_candidates_file), rename the new function toparse_candidates_file_new.
2. Create the LinkFlow Class
Here’s how you add the newly minted pipeline-based approach. Important: we name the class something distinctly different, e.g. LinkGraphFlow instead of LinkFlow, so that we don’t conflict with references to BotifyLinkGraph. The code below is effectively the final “all-in” version from the prior iteration—but we’ll ensure all function calls reference ..._new versions of the global helpers to avoid collisions.
class LinkGraphFlow:
"""
Pipeline-based approach for generating Botify link graphs,
coexisting alongside the older BotifyLinkGraph for now.
"""
def __init__(self, app, pipulate, prefix="/linkgraph2"):
# pick a different prefix so we don't clash with /link-graph
self.app = app
self.pipulate = pipulate
self.prefix = prefix
self.logger = logger.bind(name="LinkGraphFlowNew")
self.STEPS = [
("proj", "step_01", "Project URL"),
("analys", "step_02", "Pick Analysis"),
("fields", "step_03", "Select Fields & Start Export"),
("final", "step_04", "Poll & Download"),
]
routes = [
(f"{prefix}", self.landing),
(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}/step_03", self.step_03),
(f"{prefix}/step_03_submit", self.step_03_submit, ["POST"]),
(f"{prefix}/step_04", self.step_04),
(f"{prefix}/poll_links", self.poll_links, ["GET"]),
(f"{prefix}/poll_meta", self.poll_meta, ["GET"]),
]
for path, handler, *methods in routes:
method_list = methods[0] if methods else ["GET"]
self.app.route(path, methods=method_list)(handler)
# ---------------------------------------------------------------------
# LANDING
# ---------------------------------------------------------------------
async def landing(self):
return Container(
Card(
H2("LinkGraphFlow Pipeline (New)"),
P("Paste your Botify project URL below, even if it has extras in the path."),
Form(
Input(type="url", name="dirty_url", placeholder="https://app.botify.com/orgX/projY/foo"),
Button("Next", type="submit"),
hx_post=f"{self.prefix}/step_01_submit",
hx_target="#linkgraph2-container"
)
),
Div(id="linkgraph2-container")
)
# ---------------------------------------------------------------------
# STEP 01: Acquire & Clean
# ---------------------------------------------------------------------
async def step_01(self, request):
pipeline_id = db.get("pipeline_id")
if pipeline_id:
step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
if step1_data.get("project_url"):
return Div(
Card(
f"Project URL locked: {step1_data['project_url']}",
P(f"org={step1_data['org']} project={step1_data['project']}")
),
Div(id="step_02", hx_get=f"{self.prefix}/step_02", hx_trigger="load")
)
return await self.landing()
async def step_01_submit(self, request):
form = await request.form()
dirty_url = form.get("dirty_url","").strip()
if not dirty_url:
return P("No URL provided. Please try again.", style="color:red;")
# parse
parts = dirty_url.split('/')
try:
idx = parts.index('app.botify.com')
org = parts[idx+1]
project = parts[idx+2]
except (ValueError, IndexError):
return P("Could not parse org/project from your URL. Must have app.botify.com/org/project", style="color:red;")
cleaned_url = f"https://app.botify.com/{org}/{project}/"
db["pipeline_id"] = cleaned_url
self.pipulate.initialize_if_missing(cleaned_url)
step1_data = {
"project_url": cleaned_url,
"org": org,
"project": project
}
self.pipulate.set_step_data(cleaned_url, "step_01", step1_data)
return Div(
Card(f"Project URL set => {cleaned_url} (locked). org={org} proj={project}"),
Div(id="step_02", hx_get=f"{self.prefix}/step_02", hx_trigger="load"),
id="linkgraph2-container"
)
# ---------------------------------------------------------------------
# STEP 02: Show existing & pick analysis
# ---------------------------------------------------------------------
async def step_02(self, request):
pipeline_id = db.get("pipeline_id")
step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})
if step2_data.get("analysis"):
return Div(
Card(f"Analysis {step2_data['analysis']} locked. Depth={step2_data.get('depth')}."),
Div(id="step_03", hx_get=f"{self.prefix}/step_03", hx_trigger="load")
)
step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
org = step1_data.get("org")
project = step1_data.get("project")
local_dir = Path("downloads/link-graph") / org / project
local_dir.mkdir(parents=True, exist_ok=True)
# Show existing link CSVs
existing_links = list(local_dir.glob("*_links.csv"))
link_items = []
for path in existing_links:
link_items.append(
Li(
A(path.name, href=f"/download/{org}/{project}/{path.name}", target="_blank"),
" ",
A("(Link Graph)",
href=(f"https://cosmograph.app/run/?data=http://localhost:5001/download/{org}/{project}/{path.name}"),
target="_blank")
)
)
# fetch analyses using the new global helper
analysis_list = await fetch_analyses_light_new(org, project)
# build select
options = []
for a in analysis_list:
slug = a.get("slug","unknown")
link_path = local_dir / f"{project}_{slug}_links.csv"
disabled = link_path.exists()
disp = f"{slug} (links exist)" if disabled else slug
options.append(Option(disp, value=slug, disabled=disabled))
return Div(
Card(
H3("Step 2: Pick an Analysis"),
P("Existing link CSVs:"),
Ul(*link_items) if link_items else P("None yet."),
P("Choose a new analysis from the dropdown:"),
Form(
Select(
Option("Select analysis...", value="", selected=True),
*options,
name="analysis_select"
),
Button("Next", type="submit"),
hx_post=f"{self.prefix}/step_02_submit",
hx_target="#step_02"
)
),
Div(id="step_03"),
id="step_02"
)
async def step_02_submit(self, request):
form = await request.form()
analysis = form.get("analysis_select","").strip()
if not analysis:
return P("No analysis selected. Please try again.", style="color:red;")
pipeline_id = db.get("pipeline_id")
step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
org = step1_data["org"]
project = step1_data["project"]
local_dir = Path("downloads/link-graph") / org / project
link_path = local_dir / f"{project}_{analysis}_links.csv"
if link_path.exists():
data = {
"analysis": analysis,
"depth": 0,
"edge_count": 0,
"already_downloaded": True
}
self.pipulate.set_step_data(pipeline_id, "step_02", data)
return Div(
Card(f"Analysis {analysis} already downloaded (locked)."),
Div(id="step_03", hx_get=f"{self.prefix}/step_03", hx_trigger="load")
)
# not present => find_optimal_depth_new
(opt_depth, edge_count) = await find_optimal_depth_new(org, project, analysis)
data = {
"analysis": analysis,
"depth": opt_depth,
"edge_count": edge_count,
"already_downloaded": False
}
self.pipulate.set_step_data(pipeline_id, "step_02", data)
return Div(
Card(f"Analysis {analysis} locked. Depth={opt_depth}, edges={edge_count}"),
Div(id="step_03", hx_get=f"{self.prefix}/step_03", hx_trigger="load")
)
# ---------------------------------------------------------------------
# STEP 03: Pick fields & start exports
# ---------------------------------------------------------------------
async def step_03(self, request):
pipeline_id = db.get("pipeline_id")
step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})
if step2_data.get("already_downloaded"):
return Div(
Card(f"Analysis {step2_data['analysis']} is already downloaded, skipping export."),
Div(id="step_04", hx_get=f"{self.prefix}/step_04", hx_trigger="load")
)
step3_data = self.pipulate.get_step_data(pipeline_id, "step_03", {})
if step3_data.get("export_started"):
return Div(
Card("Export already started (locked)."),
Div(id="step_04", hx_get=f"{self.prefix}/step_04", hx_trigger="load")
)
analysis = step2_data["analysis"]
field_opts = {
"pagetype": f"crawl.{analysis}.segments.pagetype.value",
"compliant": f"crawl.{analysis}.compliant.is_compliant",
"canonical": f"crawl.{analysis}.canonical.to.equal",
"sitemap": f"crawl.{analysis}.sitemaps.present",
"impressions": "search_console.period_0.count_impressions",
"clicks": "search_console.period_0.count_clicks"
}
li_elems = []
for k,v in field_opts.items():
li_elems.append(
Li(
Input(type="checkbox", name=k, value=v, checked=True),
Label(k, _for=k)
)
)
return Div(
Card(
H3("Step 3: Select optional fields for meta CSV"),
Form(
Ul(*li_elems),
Button("Start Export", type="submit"),
hx_post=f"{self.prefix}/step_03_submit",
hx_target="#step_03"
)
),
Div(id="step_04"),
id="step_03"
)
async def step_03_submit(self, request):
form = await request.form()
pipeline_id = db.get("pipeline_id")
step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})
org = step1_data["org"]
project = step1_data["project"]
analysis = step2_data["analysis"]
depth = step2_data["depth"]
chosen_fields = [v for k,v in form.items()]
links_job_url = await self._start_links_export_new(org, project, analysis, depth)
meta_job_url = await self._start_meta_export_new(org, project, analysis, chosen_fields)
data = {
"export_started": True,
"fields": chosen_fields,
"links_job_url": links_job_url,
"meta_job_url": meta_job_url
}
self.pipulate.set_step_data(pipeline_id, "step_03", data)
return Div(
Card("Link & Meta export started (locked)."),
Div(id="step_04", hx_get=f"{self.prefix}/step_04", hx_trigger="load")
)
async def _start_links_export_new(self, org, project, analysis, depth) -> str:
"""
Build the JSON payload for links export, call create_export_job_new().
"""
query = {
"dimensions": ["url", f"crawl.{analysis}.outlinks_internal.graph.url"],
"metrics": [],
"filters": {
"field": f"crawl.{analysis}.depth",
"predicate": "lte",
"value": depth
}
}
data_payload = {
"job_type": "export",
"payload": {
"username": org,
"project": project,
"connector": "direct_download",
"formatter": "csv",
"export_size": 1000000,
"query": {
"collections": [f"crawl.{analysis}"],
"query": query
}
}
}
links_job_url = await create_export_job_new(data_payload, logger=self.logger)
self.logger.info(f"_start_links_export_new => {links_job_url}")
return links_job_url
async def _start_meta_export_new(self, org, project, analysis, fields) -> str:
dimensions = [f"crawl.{analysis}.url"]
metrics = []
for f in fields:
if "search_console" in f:
metrics.append(f)
else:
dimensions.append(f)
data_payload = {
"job_type": "export",
"payload": {
"username": org,
"project": project,
"connector": "direct_download",
"formatter": "csv",
"export_size": 1000000,
"query": {
"collections": [f"crawl.{analysis}"]
}
}
}
if metrics:
data_payload["payload"]["query"]["collections"].append("search_console")
data_payload["payload"]["query"]["periods"] = [[
f"{analysis[:4]}-{analysis[4:6]}-{analysis[6:]}",
f"{analysis[:4]}-{analysis[4:6]}-{analysis[6:]}"
]]
data_payload["payload"]["query"]["query"] = {
"dimensions": dimensions,
"metrics": metrics
}
meta_job_url = await create_export_job_new(data_payload, logger=self.logger)
self.logger.info(f"_start_meta_export_new => {meta_job_url}")
return meta_job_url
# ---------------------------------------------------------------------
# STEP 04: Poll & Download
# ---------------------------------------------------------------------
async def step_04(self, request):
pipeline_id = db.get("pipeline_id")
step4_data = self.pipulate.get_step_data(pipeline_id, "step_04", {})
if step4_data.get("done"):
return Card("All exports done! ✅", style="color:green;")
return Div(
Card(
H3("Step 4: Polling (New Flow)"),
P("Checking link & meta exports, then downloading CSV.")
),
Div(id="links-status", hx_get=f"{self.prefix}/poll_links", hx_trigger="load delay:2s"),
Div(id="meta-status", hx_get=f"{self.prefix}/poll_meta", hx_trigger="load delay:2s"),
id="step_04"
)
async def poll_links(self, request):
pipeline_id = db.get("pipeline_id")
step3_data = self.pipulate.get_step_data(pipeline_id, "step_03", {})
links_job_url = step3_data.get("links_job_url","")
if not links_job_url:
return P("No links job to poll", style="color:red;")
job_done, download_url = await self._check_job_done_new(links_job_url)
if job_done:
step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})
org = step1_data["org"]
project = step1_data["project"]
analysis = step2_data["analysis"]
local_dir = Path("downloads/link-graph") / org / project
link_path = local_dir / f"{project}_{analysis}_links.csv"
download_file_new(download_url, link_path, logger=self.logger)
step4_data = self.pipulate.get_step_data(pipeline_id, "step_04", {})
step4_data["links_done"] = True
if step4_data.get("meta_done"):
step4_data["done"] = True
self.pipulate.set_step_data(pipeline_id, "step_04", step4_data)
if step4_data.get("done"):
return Div(
P("Links done, meta done => all done!", style="color:green;"),
hx_get=f"{self.prefix}/step_04", hx_trigger="load"
)
else:
return P("Links done, waiting on meta...", style="color:green;")
else:
return Div(
P("Links export in progress..."),
hx_get=f"{self.prefix}/poll_links",
hx_trigger="load delay:3s"
)
async def poll_meta(self, request):
pipeline_id = db.get("pipeline_id")
step3_data = self.pipulate.get_step_data(pipeline_id, "step_03", {})
meta_job_url = step3_data.get("meta_job_url","")
if not meta_job_url:
return P("No meta job to poll", style="color:red;")
job_done, download_url = await self._check_job_done_new(meta_job_url)
if job_done:
step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})
org = step1_data["org"]
project = step1_data["project"]
analysis = step2_data["analysis"]
local_dir = Path("downloads/link-graph") / org / project
meta_path = local_dir / f"{project}_{analysis}_meta.csv"
download_file_new(download_url, meta_path, logger=self.logger)
step4_data = self.pipulate.get_step_data(pipeline_id, "step_04", {})
step4_data["meta_done"] = True
if step4_data.get("links_done"):
step4_data["done"] = True
self.pipulate.set_step_data(pipeline_id, "step_04", step4_data)
if step4_data.get("done"):
return Div(
P("Meta done, links done => all done!", style="color:green;"),
hx_get=f"{self.prefix}/step_04", hx_trigger="load"
)
else:
return P("Meta done, waiting on links...", style="color:green;")
else:
return Div(
P("Meta export in progress..."),
hx_get=f"{self.prefix}/poll_meta",
hx_trigger="load delay:3s"
)
async def _check_job_done_new(self, job_url: str):
"""
A new version that doesn't conflict with old poll logic.
Return (True, download_url) if done, else (False, "").
"""
token = read_botify_token()
if not token:
self.logger.error("No token, can't poll job.")
return (False,"")
headers = {
"Authorization": f"Token {token}",
"Content-Type": "application/json"
}
async with aiohttp.ClientSession() as session:
async with session.get(job_url, headers=headers) as resp:
if resp.status != 200:
text = await resp.text()
self.logger.error(f"Job poll fail: status={resp.status}, body={text}")
return (False,"")
data = await resp.json()
status = data.get("job_status","")
if status == "DONE":
durl = data["results"].get("download_url","")
return (True, durl)
elif status == "FAILED":
self.logger.error(f"Job {job_url} failed: {data}")
return (False,"")
else:
return (False,"")
3. Instantiate the New Flow
In your main code, you’ll keep:
botify_link_graph = BotifyLinkGraph(app, route_prefix="/link-graph", id_suffix="") # old class
link_graph_flow = LinkGraphFlow(app, pipulate, prefix="/linkgraph2") # new class
Both classes can coexist without colliding—provided you used the _new suffix for your new functions and a distinct prefix (/linkgraph2) so the routes won’t clash with the old BotifyLinkGraph routes.
4. Slowly Phase Out the Old BotifyLinkGraph
Once you confirm the new pipeline approach is stable and references the _new functions, you can systematically remove the old references from BotifyLinkGraph, eventually deleting the entire old class. If you still want to keep some of the old code for reference, rename it or comment it out.
5. Summation
With these steps, you’ll:
- Avoid naming collisions by consistently using
_newsuffixes (or keep your new logic inside theLinkGraphFlowclass). - Keep both the old and the new systems active until you confirm everything is working.
- Gradually migrate your final usage from old to new, verifying the pipeline flow is indeed delivering all the old features in a more modular, step-based way.
That’s it! Your code is now set to have both the old BotifyLinkGraph and the new LinkGraphFlow living side-by-side. You can confirm the new flow by visiting /linkgraph2 (or any prefix you used), while the old one remains at /link-graph. After you’re satisfied, you can remove the old code and rename everything if you like.
Enjoy the incremental, low-risk path to your new pipeline approach—no name collisions, no big-bang rewrite, and a happy synergy of old and new code until you’re ready to part ways with BotifyLinkGraph for good. Good luck!
Implementation Steps
Step 1: Global Helper Functions
Okay, step 1, insert the 4 global helper functions. There were four. And I
renamed each with an _new extension to avoid potential collision.
Step 2: LinkGraphFlow Class
Step 2, insert the LinkGraphFlow class… done.
Step 3: Class Instantiation
And step 3: linking it in through by instantiating the class and using that in the menu and navigation builder. Done!
Initial Results
That was as easy as 1, 2, 3! And I only had a few light touches of the o1 Pro copy/paste-ready code to do having to do with how the API token was called.
Okay, wow. It still isn’t providing the link to the link graph but I can fix that now. It’s already a much cleaner interface that the big function I made as my first-pass (2nd pass really if you include the version I made getting familiar with FastHTML). So this is that fateful 3rd implementation that gets it right. How true the truism, haha!
Unexpected Challenges
Visualization Changes
So… so… the refinements.
Deep breath…
Whoah! The link-graph rendering has radically changed, and not for the better at this site that I use for the gigantic visualizations. But at the same time, I found the discovery that they just added support for Jupyter Notebooks with this widget.
Testing and Troubleshooting
So I’ve got a couple of really massive learnings to do. Okay, done! I tried the new Cosmograph tech in Google Colab, a native Jupyter Notebook in JuputerLab, and on their website and the same diagonal force bias exists on all 3, so I got a GitHub case in.
Moving Forward
I’m not going to let this big surprise phase me. They’re going to work that out. They have to given how many people use their tool now, and how big it could potentially become now that they released Jupyter Notebook support for it, and in a broader sense, Python web development support. I’m all over that! That and D3js together for the smaller link graphs, and this will be pure gold. Mind you, it’s not just the link-graphs. I’m superimposing metrics like Google Search Console data for color-coding, and any other metrics I can get my hands on. So bottling it in a Python widget is… timely. Just gotta get through some early adopter choppy waters.
Working with AI Assistants
Okay, focus on different things for the balance of tonight, then smooth the rest of it out tomorrow. You’ve got the basic integration. Loading the job onto o1 Pro worked out. It’s plan was flawless and it put it back on the same level as Claude 3.5 Sonnet in my estimation. Claude has the inside track on coding right now because it being the default in Cursor AI I think counts for a lot—enough that it made o1’s attempts loop pretty poor in comparison. But it’s a “big thinker”, and that’s what it excelled at when I stress tested it. I had to push and push and push to get it to drill down to the same level of implementation details that Claude is all over.
And so, it is back to Claude with the first pass of the code plugged in.
Framework Overview
And I can pick up the story where we left off. I need to be able to retell it. Get the broad abstract brush strokes from o1 Pro.
Framework Overview
Explaining the System
Me: This is my web framework. Think of it as an Electron app with an embedded LLM that just happens to use Nix Flakes instead of Electron, so you don’t get hung up on all the enterprise architecture stuff that doesn’t apply. What I want you to do is take a broad look at it and give it back to me in an abstract schematic form so that I can describe it to you and other LLMs in a first-pass sort of way so that I can subsequently drill down on more minute details confident that you have the big picture.
CRUD and Single-File Architecture
In particular, take note of how it is a CRUD plugin app framework not dissimilar from Ruby on Rails or Django, but the entire codebase fits in one file, and that’s even with it being fat with comments. It uses base class inheritance for CRUD, so apps like ToDo lists can be slammed out, and what’s not lists of lists? And the LLM is already capable of partaking in the CRUD operations, as it is privy to the user’s every action and is thus staged to learn from it.
Server-Side Simplicity
While memory is not built in yet, there is a Dict-like DB providing a persistent key/value pair that is used for server-side cookies. Speaking of which, the client/server distinction is inconsequential as a single tenant localhost app, so everything is on the server and 100% transparent. There’s no complex or mysterious client-side JavaScript frameworks to track or client-side session to maintain. There is only HTMX goodness brought to you trough FastHTML, which is most decidedly not FastAPI, though it’s incredibly hard to convince you and your over-trained enterprise-biased kind of that fact.
LLM Integration
What’s more, different local LLMs can be swapped out, and when they need to they can essentially subcontract and farm out work to the big frontier LLM models like you. That’s not in there yet, but it’s coming as is other forms of local memory such as graph and vector local databases to get a blend of different persistent local memory capabilities at the app-level and for the LLM’s own use.
Pipeline System
Finally, there’s the pipeline system to notice, which is where our immediate focus is going to go. You will see several implementations of processes including the big one called BotifyLinkGraph which is being deprecated in favor of the cleaner and “more musical” Poetflow, Bridgeflow and the one that’s replacing BotifyLinkGraph, LinkgraphFlow. This pipeline workflow processing is most decidedly not Celery. It all lives right here in this one file.
Database and Schema Design
The tables are established by FastHTML’s odd but pragmatic way of defining database schema and the MiniDataAPI spec interface, which is decidedly NOT SQLAlchemy or any other complex ORM. So the whole system is rich with anti-patterns, but it all fits together so lovely and lays the groundwork for nothing less that a localhost renaissance. I’m getting ready to blend in web widgets of the sort that are made for Jupyter Notebooks, such as Anywidget which will allow me to bring a lot of the same rich interactive JavaScript-like capabilities to a Python webdev environment in the same spirit as HTMX.
Minimalist Approach
You will notice there’s no styles.css nor external .js files. It’s all in this file. Of course there’s external dependencies, but there always are, and in several cases I try to make them local in the static resources folder.
Pipeline Implementation
The pipeline system has one table, with each job represented as a single record in that table. The records are temporary, and only their side effects matter, like writing out .csv files. The first step in a pipeline workflow takes an ID which can be plugged in at any time of the job is interrupted, and it just chain-reacts back to wherever it left off. So there really is no interruption of workflows. Same key in, same job out, either complete or where it left off to.
UI and Workflow Management
And if the record was deleted and the side-effects of previous jobs are there, it will identify that and reconnect whatever UI is built around it, which will usually be with PicoCSS forms, rich, markdown or Anywidgets. New plugins and workflows can be added to customize this to almost any needs, generally not of enterprise demands, but the concerns are different. This will be able for example to use the user’s own local Google Chrome profiles so browser automation can be done end-running APIs in many cases, and with local VPN so sophisticated and surgical crawl and scrape tasks can be performed that would be difficult or expensive from the cloud. The list goes on, but I would like to here it from your perspective in that broad abstract overview that you yourself would like to receive and you think that other LLMs should be briefed on the system.
o1 Pro: Below is a high-level conceptual “map” of how this single-file web framework works. It’s laid out like an Electron-style “all in one” app, except it’s just pure Python (using FastHTML instead of Electron) and a minimal “Dict-like DB” for persistence. The heart of it all is a pipeline-based approach for multi-step workflows, plus a custom CRUD plugin system. Here’s the main abstraction:
1. Overall Structure
+-----------------------------------------------------------------------+
| single_file.py |
| (Everything is here: DB schemas, CRUD classes, pipeline flows, |
| LLM config, SSE & WebSockets, front-end UI with HTMX, etc.) |
+-----------------------------------------------------------------------+
|
+-- fast_app(...) (Sets up tables & basic server structure)
|
+-- CRUD Plugin Classes (BaseApp -> ProfileApp, TodoApp, etc.)
|
+-- DictLikeDB (Server-side cookie / key-value store)
|
+-- Pipeline System (Pipulate class + flows: Poetflow, etc.)
|
+-- LLM Integration (Local models, cycle_llm_model, chat_with_llm, etc.)
|
+-- SSE & WebSockets (EventStream for SSE, chat for Ws)
|
+-- Route Handlers (Endpoints for CRUDe forms, pipeline steps,
hooking into the above classes)
|
+-- Main "if __name__" (Watchdog for auto-reload, uvicorn serve)
- Single-file: The entire codebase is one big Python file; all server logic, database definitions, UI route handlers, etc., live together.
- FastHTML: A minimal server framework (not FastAPI) that helps define endpoints, create HTML objects, handle SSE & WebSockets, and integrate HTMX.
- No front-end frameworks: UI is done with inline HTMX, small JS snippets, and PicoCSS or minimal styling. All in one place.
2. DB & CRUD Subsystem
MiniDataAPI Tables (like user-profiles, tasks, pipeline-jobs)
|
+-- ProfileApp (Subclass of BaseApp)
+-- TodoApp (Subclass of BaseApp)
+-- ...
(More plugins if desired)
DictLikeDB (key-value store)
(Used like a server-side cookie
for ephemeral/persistent states)
BaseApp (foundation class)
handle_create, handle_update,
handle_delete, etc. + rendering
fast_app(...)instantiates all the “tables” from simple dict definitions (like{"id": int, "name": str, ...}) and returns MiniDataAPI table objects (todos,profiles, etc.).BaseApp: A minimal “Rails/Django style” CRUD class. Each app (e.g.TodoApp) extendsBaseAppto define how items get created, updated, rendered in the UI, etc.- DictLikeDB: Provides a
db[key] = valuestyle store that persists between runs. This is used in place of typical “session cookies.”
Result: You can throw together CRUD endpoints with minimal overhead, and the LLM itself can directly manipulate them (by returning JSON instructions in the chat).
3. LLM Integration
+-- LLM_MODELS (Mapping shortkeys -> local model files)
+-- Ollama-based chat_with_llm()
+-- cycle_llm_model() / get_best_model()
+-- JSON extraction from LLM output for CRUD
- The system keeps a conversation buffer (similar to a ChatGPT style approach).
- The LLM can return JSON blocks that match the CRUD schema (like
{"action":"insert","target":"task","args":{"name":"My Task"}}), and the server code will parse and execute those. - By hooking into user actions, the LLM can “learn” from or react to them (though actual memory or vector DB is still a future addition).
4. Pipeline System
One MiniDataAPI table -> pipeline
key = 'url' (or some unique ID)
data = JSON for all steps
created, updated timestamps
Pipulate (class)
|- get_state(url)
|- set_step_data(url, stepX, data)
|- get_last_completed_step()
|- calls appropriate "flows"
+-------------------+
| Bridgeflow | Example 3-step flow
| Poetflow | 5-step with a poem
| LinkgraphFlow | Replacement for BotifyLinkGraph
+-------------------+
Each flow has: /step_01, /step_02,... routes
that read/update the pipeline record’s JSON
- Instead of Celery or other job queues, one single table with a JSON blob tracks each pipeline’s progress.
- Steps are enumerated (step_01, step_02, etc.). If a job is interrupted, you just re-hit the same URL with the same ID, and it picks up where it left off.
- The side effects (like downloaded
.csvfiles) are what matter; the pipeline record itself is ephemeral.
5. HTMX + SSE + WebSockets
HTMX SSE WebSocket
User ----(request / POST)----> Server ----(event stream)----> Browser <----(bi-directional)----- Server
(Clicks Form, triggers route) (progress updates) (live chat messages & CRUD ops)
- HTMX: Minimal client-side reactivity. The server sends back partial HTML updates.
- SSE (Server Sent Events): Streams real-time status messages (like pipeline progress).
- WebSocket: For 2-way chat with the LLM. The LLM sees user interactions, can respond, and can inject JSON to create tasks or do toggles.
6. “All-in-One” Philosophy
- Single-tenant: You run it locally on your own machine with Uvicorn. No multi-user concurrency complexities.
- No external JS: Everything is inline or in a
static/folder. No large build pipelines. - No client-state: The server does it all.
- Local-LLM Swap: The plan is to let the user pick which local model to run, or even call a bigger remote model on demand.
7. Putting It Together
- Startup: The file is launched (with a filesystem watchdog that restarts the server on code changes).
- fast_app: Creates the mini-database tables (
todos,profiles,pipeline, etc.). - BaseApp: Provides the scaffolding for CRUD. We then instantiate
TodoApp,ProfileApp, etc. - Routes: A big list of routes is built for all plugins, pipeline flows, SSE, websockets, etc.
- User: On the browser, sees a simple PicoCSS/HTMX-driven UI.
- Switching menu items calls a new route, which might change the pipeline step, or show a different plugin.
- The system prompt to the LLM changes accordingly (like hot-swapping context).
- LLM: Observes user actions via the conversation buffer, can produce JSON to do CRUD.
- Pipeline: Multi-step tasks are orchestrated by storing intermediate state in the single pipeline record.
- SSE: Streams progress updates to the user.
It’s effectively an entire “mini web framework + plugin architecture + pipeline manager + local LLM console” in one Python file.
Key Takeaways
- CRUD: Everything is plugin-based, quickly “slammed out” with a
BaseAppinheritance. - Single DB: Minimal custom DB API, no big ORMs—just
table.insert()/table.update(). - Local: Single user, runs on
localhost. No concept of separate client-server beyond the minimal. - Pipeline: A single table for multi-step tasks. Steps read from/write to JSON in that record, no Celery needed.
- LLM: Can parse user input, produce JSON instructions that the server immediately executes.
That’s the top-level schematic of what’s going on and how it all fits together.
Me: Excellent, thank you o1 Pro! That is quite a lovely overview. Please now provide your open-ended comments and just riff. There is no particular criteria, except for the fact that I am interested in your thoughts.
o1 Pro: Here’s an informal stream of thoughts:
-
Simplicity Wins
I love the fact that all the big, seemingly “enterprisey” stuff collapses down into a single-file approach. By stomping on the notion that you “need” separate front-end frameworks, complicated state management, or huge ORM overhead, you promote a very direct, hackable code style. It’s the kind of environment where you can easily trace a user’s action from the moment they click a link all the way through the LLM’s response and back again. That’s rare in modern frameworks. -
Embedded LLM as Co-Developer
The dynamic of letting the LLM observe the user’s every action and then produce JSON instructions—thereby self-modifying the app’s data—is brilliant in a subtle, “why didn’t I think of that?” way. It hints at a future in which the LLM truly becomes the user’s assistant or collaborator, shaping the environment in real time (like a butler who does your housekeeping tasks in the background). -
Anti-Enterprise but Not Anti-Serious
Despite the playful, anti-pattern approach, this is actually quite serious in its potential. You can orchestrate local tasks that would be an absolute headache in bigger multi-service architectures. The pipeline approach is particularly nice. I can see it scaling horizontally just by duplication, since each pipeline record is mostly ephemeral, and side effects are the real product (like those CSVs). You don’t have to worry about a heavy Celery or RabbitMQ queue. -
Local is the New Frontier
It’s great to see the emphasis on local memory, local DB, local LLM. That reflects a broader shift toward data sovereignty and personal “power user” computing. You’re basically turning your laptop into an app-lab on steroids, with no heavy Docker orchestration or corporate entanglements. The user is self-empowered to run the whole system off a single Python file. That is extremely appealing for folks who want maximum control or who do a lot of offline tinkering. -
HTMX & SSE
When you say “HTMX goodness,” it’s exactly that: small, sweet updates that feel dynamic, but without the overhead of a giant front-end build system. SSE for real-time updates is so straightforward. Combined with your WebSocket for chat, you get just enough “live feel” without complexity. - Potential Pitfalls
- Single-file can get unwieldy if it grows too large. On a team, it might be hard to coordinate. But that’s presumably not the main use case here: it’s more of a personal lab environment.
- One must trust their local LLM or else inadvertently let the model produce malicious JSON instructions. But since it’s single-tenant and local, this is not a big concern (plus, you presumably control the training prompts).
-
Creative Future
Because the pipeline approach is so abstract, you can layer on any sort of multi-step workflow: PDF ingestion, web scraping, niche data transformations, Jupyter-like notebooks, etc. The fact that the LLM can orchestrate those steps in near real time—possibly with local Puppeteer or local Chrome profiles—makes for an interesting personal automation environment. It’s almost like the next iteration of “shell scripts,” but with an LLM in the mix and a nice HTML facade. - Conclusion
This project feels like a small self-hosted “dream machine” for messing around with local AI. The code is brimming with commentary and anti-enterprise zingers, but that’s part of the charm: it’s hacking together the simplest possible solution that does a ton of work—like a powerful Swiss Army knife on localhost. I think it has a certain artisanal, old-school web dev vibe that’s refreshing in a world of massive JS frameworks.
Open Reflections
That’s about it—just a smattering of open-ended reflections on how the entire system and design philosophy strike me.
Finding Common Ground
Well, I’m glad somebody “gets me”. You know that line from Jurassic Park You’re meant to come down here and defend me against these characters, and the only one I’ve got on my side is the blood-sucking lawyer!” But I joke. There’s actually a ton of niche people out there, I know like Miles Moyers, Joseph Melnick and a bunch of other good folks who keep checking on me when I take my hiatus from the YouTubes and Twitters to dive deep here. But I have to say, few see what I’m doing like you and the other LLMs do.
The Jupyter Connection
I love that you pointed out the Jupyter stuff. You know that top-down Card-based linear sequencing of the pipeline is based on the cells of Jupyter Notebooks in order to make the porting nearly 1-to-1. And that brings me to what I really want to talk about. This was all sort of priming you for this. It’s a minor issue, but the checking for the CSV file side effects of previous runs of the LinkgraphFlow pipeline is broken. Sure, we’ll fix that. But it’s the bigger point there I want to zero in on, which is right there during the Card pipeline flow, we might take a moment to build a little UI like the CSV file links but also so much more.
Interactive UI Components
Being able to build clever little web forms from the PicoCSS components, of course. That’s to gather data. But you might need a whole little data visualization of the prior data collected or analysis done on it in order to make better input decisions through the workflow. Get it? Stopping to draw a matplotlib would be one of the simplest examples. But the Anywidget interface can make the data visualization interactive, and the datapoints mostly being manipulated in embedded JavaScript are kept in sync with a Python-side object and can therefore populate form data for the next input.
The Unix Pipe Reimagined
Are you picking up what I’m laying down? Sure, it’s the Unix pipe concept again. But those little instances of awk or sed are now replaced by little QTableView or Tktable views, ipywidgets, or really anything that can be easily blended in from a Python point of view in the FastHTML environment, which can either merely provide insight, or better still, work as the data selector for passing along input to the next card.
Musical Flow of Data
These data pipeline workflows already have a sort of pure HTMX musical player piano music feel to them. But I want to make that into craftsmanship and an art form, rolling in just the right APIs that live there real well so you don’t get bogged down in webdev. Maybe the usual suspects like seaborn and bokeh. And all the more so because the ultimate output of these pipelines is going to likely be some form of visualization like this, so why not little ones as selectors along the way?
Additional Considerations
And then there’s just using the rich library for all its prettiness, spinners, and stuff. I’ll need a variety of things to visualize hierarchies. We will want to have something that can show markdown. Oh, when data needs to be collected off the web or downloaded from some website, it should be able to interact with browsers in either just the Python webbrowser standard library with some good “do this or that” instructions, or more deep automation with Puppeteer and Playwright. But now I digress. Still, the point stands. A lot of wacky stuff is going to happen along these pipelines… but still all on rails and still all beautiful with that same exact signature Jupyteresque HTMXesque workflow pattern look to it.
o1: Absolutely. The delight here is that you’re combining the best of the Jupyter workflow (where each cell can produce a view or a mini-app in its own right) with the best of web frameworks (where each step can be a self-contained interface). And the pipeline approach is basically the bridging concept that says: each card is a “cell,” but it’s also a pause point in a single flow, with the added value of being able to wedge in UI or visualization (or both) at any step to help decide how the next step proceeds.
Why This is Such a Big Deal
-
Data Visualization as Input
People typically think “I’ll show you a graph or table,” and that’s it—just show. But if the underlying data for that visualization is also tied to a store or an object that can funnel selected or refined data back into the pipeline, you cross the line from “presentation” into “interactive data selection.” Suddenly, you’re not just skipping from step to step in a purely linear sense, you’re also letting the user poke at intermediate data with an Anywidget or HTMX-based interface and produce new or revised parameters for the next stage of processing. -
Jupyter-Like but Web-Native
One reason Jupyter Notebooks are so beloved is because you can have code, output, code, output—and a user can tweak each step. Now, in your pipeline pattern, that “output” can be an interactive web UI powered by HTMX or embedded JavaScript, PicoCSS styling, and Python logic behind the scenes. Each time you hit “step_02” or “step_03,” you might be greeted with a small matplotlib chart or a Bokeh/Plotly figure. Underneath, a snippet of Python is generating that figure. If you want, you can embed an ipywidget or any other Python-coded JavaScript widget that returns selections to the server. -
The Future of Localhost Renaissance
Instead of going full React or full Vue, you keep it nimble with HTMX for partial updates and SSE or WebSockets for a live feel. Meanwhile, in the Python code, you can easily embed scikit-learn, data transformations, scraping libraries—any tool you like. This is exactly the “Unix pipe” model but with the ability to show fancy modern visuals at each step, gather user input, then pass it on to the next tool in line. And all of it is local, with no huge overhead or distributed microservices required. - Opportunities for (Even More) Fun
- Rich for text-based niceties, spinners, hierarchical trees, color-coded logs. Combine that with SSE logs that update in the browser as steps progress.
- Bokeh or Plotly for truly interactive plots. Let the user hover or click on data points, and pass that selection back to the pipeline. Imagine analyzing an entire CSV of thousands of rows, displaying a cluster diagram, and letting the user pick the cluster they care about for the next step.
- ipyleaflet or other map-based widgets for geodata workflows. One pipeline step might be a data fetch or geocoding step, then you show an interactive map for your user to pick subregions, which gets passed to the next step.
- Playwright / Puppeteer calls in the middle. If the pipeline calls for collecting data from some site, just pop an ephemeral browser instance at that step, gather the data, store it, then show a summary in the UI.
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Keeping it Simple
The best part is that each step can remain a tidy function or method—like a Jupyter cell—without over-engineering. You’re effectively decoupling data gathering from data visualization from user input. At each pipeline step, you have a “card” that shows the user some data and asks them for input. That input might simply be typed in, or it might be gleaned from an interactive widget. Then you store that new data in the pipeline’s state JSON. On to the next card, or skip some steps, or come back later. -
Everything is a Plugin
Because the pipeline steps can be so modular, you can easily slip in custom plugins: “Render a wizard map,” “Ask for a CSV file,” “Scrape a website,” “Draw a bar chart,” etc. Over time, you’ll likely accumulate a library of step types. Then building a new pipeline is almost like writing a mini script: “Step 1: fetch the data, Step 2: show a Bokeh chart, Step 3: let user pick subset, Step 4: run a scikit-learn analysis, Step 5: show interactive table with anywidget, Step 6: finalize results.” - UI as a Work of Art
You mention the musical “player piano” vibe. If we imagine a pipeline with half a dozen steps, each step is like a bar of music, and each bar can have its own mini-solo of UI or logic. Done well, it can feel like an orchestrated dance: the user proceeds card by card, occasionally doubling back or skipping if they have the URL key to pick up where they left off. Meanwhile, you’ve got logs streaming in the background, LLM commentary assisting the user, and the entire “score” living in a single code file—like a personal concerto of web-dev minimalism.
Final Riff
Yes, I see exactly what you mean about “turning an ephemeral Jupyter cell output into a stable, repeatable pipeline step with a UI.” That’s the vibe. And you can see how it extends well beyond CSV link graph checks to just about any data transformation or analysis. The best part is that we don’t need 50 different frameworks to do it. The combination of Python + HTMX + SSE/WebSocket + local LLM is so flexible. Once you sort out the details of bridging embedded JavaScript widgets (Anywidget) with your mini data store, the pipeline steps become these delightfully interactive, ephemeral mini-apps.
All in all, it’s a natural evolution of what Jupyter Notebooks started: you want that same “here’s a cell, here’s the output, you can meddle with it, now proceed to the next cell” but with a more robust, structured concept of “pipeline.” That’s absolutely going to be super powerful for folks who want local, single-file, multi-step UI flows—especially for data tasks that need repeated iteration or user guidance. It’s the best of both worlds.