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Created, Jun 28, 2025

Updated, Sep 09, 2025, 20 min read

An assortment of links I’ve found cool at a surface level, I’m storing them here to read later:

  • Integrated AI Assistance — smart guidance. Modern tools need built-in AI agents that can explain results, generate workflows, and even teach concepts in real time.
  • Full Data Science Workflow Support — from loading and cleaning data to modeling, evaluation, and reporting. No-code tools must cover the entire lifecycle, not just charts or dashboards.
  • Model Transparency & Explainability — especially important in regulated industries or critical applications. Users need visual explanations, feature importance, and interpretable outputs — even without code.
  • Offline & Secure Execution — privacy matters. Tools should allow local work with sensitive data, without requiring constant cloud access.
  • Smooth Transition to Code — the best tools are no-code by default, but not code-locked. They should allow exporting workflows, reviewing code, or switching to script mode when needed.
  • Collaboration & Reproducibility — In 2025, teams work together asynchronously. Tools must support shared workflows, versioning, and notebook-style exports. Support for AI & AutoML — manual feature engineering is out; smart automation is in. Users expect tools to suggest models, tune hyperparameters, and optimize pipelines, with just a few clicks. MLJAR Studio

Advantages: Built-in AI Agent (3 modes) — Code Assistant, Data Analyst, and Teacher — supports code generation, data insights, and interactive learning. Fully offline — ideal for working with sensitive data in enterprise and regulated environments. Code export — every step can be saved as clean Python code that is compatible with Jupyter Notebooks. AutoML included — MLJAR-supervised provides fast model training, a leaderboard, model interpretation, and detailed reports. Simple desktop installation — no manual setup of packages or environments. Ready to use code recipes — called Piece of code, sorted by categories. Works with all data types. Complete transparency — you always see what’s happening under the hood. Limitations: There are no built-in cloud collaboration features (but you can share the notebook as a web app or dashboard with Mercury). Desktop-only — not accessible from a browser.

KNIME

  • Visual workflow builder — powerful drag-and-drop interface for building complex data pipelines.
  • Extensive library of nodes — covers data prep, ML, NLP, database access, Python, R, Spark.
  • Offline mode — works entirely on local machines.
  • Strong integrations — supports external tools, scripting, and enterprise connectors. Limitations:
  • Can be overwhelming for beginners — lots of configuration steps.
  • Limited support for modern ML frameworks like transformers or LLMs.
  • High memory usage on large workflows.Basic AI assistance is available via LLM extension (as of KNIME 5.2), but is limited in capabilities compared to native, notebook-integrated agents.
  • At a time when the AI tools we develop and use are being built on the labor of exploited ghost workers in the Global South, when Google, Microsoft, and Amazon’s contracts with the IDF to provide cloud computing for automated missile- targeting in the genocide of Palestinians in Gaza, when the Department of Homeland Security is scraping social media to abduct students off of the street and detain scientists at the border, our position as workers who build these and similar tools becomes highlighted not by flashlights but floodlights.

serious work

  • We’ve been disappointed by how unambitious people are in this sense with Jupyter notebooks. They haven’t pushed the medium all that hard; there is no Citizen Kane of Jupyter notebooks. Indeed, we’re barely beyond the Lumière brothers. Examples like Norvig’s notebook are fine work, but seem disappointing when evaluated as leading examples of the medium.

  • Aspiring to canonicity, one fun project would be to take the most recent IPCC climate assessment report (perhaps starting with a small part), and develop a version which is executable. Instead of a report full of assertions and references, you’d have a live climate model – actually, many interrelated models – for people to explore. If it was good enough, people would teach classes from it; if it was really superb, not only would they teach classes from it, it could perhaps become the creative working environment for many climate scientists.

  • The understanding would be transferable. Even a user who has understood only a tiny part of the material could begin tinkering, building up an understanding based on play and exploration. It’s common to dismiss such an approach as leading to a toy understanding; we believe, on the contrary, that with well enough designed scaffolding it can lead to a deep understanding. Developed in enough depth, such an environment may even be used to explore novel research ideas. To our knowledge this kind of project has never been seriously pursued. But it’d be fun to try.

  • Tools for thought are (mostly) public goods, and as a result are undersupplied: That said, there are closely-related models of production which have succeeded (the games industry, Adobe, AutoDesk, Pixar). These models should be studied, emulated where possible, and used as inspiration to find more such models. → What practices would lead to tools for thought as transformative as Hindu-Arabic numerals? And in what ways does modern design practice and tech industry product practice fall short? To be successful, you need an insight-through-making loop to be operating at full throttle, combining the best of deep research culture with the best of Silicon Valley product culture.

  • Take emotion seriously: Historically, work on tools for thought has focused principally on cognition; much of the work has been stuck in Spock-space. But it should take emotion as seriously as the best musicians, movie directors, and video game designers. Mnemonic video is a promising vehicle for such explorations, possibly combining both deep emotional connection with the detailed intellectual mastery the mnemonic medium aspires toward.

  • Tools for thought must be developed in tandem with deep, original creative work: Much work on tools for thought focuses on toy problems and toy environments. This is useful when prototyping, but to be successful such tools must ultimately be used to do serious, original creative work. That’s a baseline litmus test for whether the tools are genuinely working, or merely telling a good story. Ideally, for any such tool there will be a stream of canonical media expanding the form, and entering the consciousness of other creators.

Ben Shneiderman, a pioneering human-computer interaction researcher, offers this charming schematic for research project design in The New ABCs of Research. He calls it the “two parents, three children” pattern.

alt text

The challenge is similar to what learning scientists must do in designing educational interventions. In Principles and Methods of Development Research, Jan van den Akker offers a beautiful distillation of what a unit of progress looks like in that field (thanks to Sarah Lim for the pointer):

[Educational design] principles are usually heuristic statements of a format such as: “If you want to design intervention X (for the purpose/function Y in context Z), then you are best advised to give that intervention the characteristics A, B, and C (substantive emphasis), and to do that via procedures K, L, and M (procedural emphasis), because of arguments P, Q, and R [(theoretical emphasis)].”

The key thing it does is to explicitly connect the dots between a grounded theoretical claim, the implied design approach, and the desired outcome. I’m certainly wary of trying to fit all research into some kind of formula like this, but how clarifying it is to have this target painted so sharply! If you’re a researcher and you want to develop some new intervention, you need to design an experiment whose results can generate a statement of this kind.

When I read your question about people completing “full cycles” of work I have a strong intuition that we should look for the names of institutions or perhaps loose movements rather than individuals. The mix of skills required are too broad even for most polymaths. And if I look at whole “scene” of tools for thought with a focus on people doing 1, 2, and 3, which is a very loose movement, I see just what you point out: a missing component for a working ratchet is the distillation of insights and critical reflection. I suspect this requires more than publication; what’s needed is some degree of interpersonal connection and mutual dependence. I see a very slow but productive ratcheting in the Quantified Self community, which unites academic researchers, (a few) clinicians and allied health professionals, and technologists of various types around supporting self-research, with the most active developers focusing their work around their own self-research projects while sharing tools, methods, and critical support. That’s the positive example: a scene that jelled. BUT, on the other hand, the resources associated with commercialization of self-tracking remain deeply siloed in consumer tech companies. The potential insights from the hundreds of millions of users of the commercial tools do not feed back very efficiently into the development of high level insights and new theories. In fact, the theoretical material about “behavior change” referenced by these companies is so outdated that I often doubt it carries much real weight in their internal research road map. It’s more window dressing than motivating theory. All of that said, I do think the development of Quantified Self and personal science offers an example with features worth imitating. Specifically:

  1. articulating a very high level common theoretical and/or cultural position can bring participants into contact based on the promise and challenge of realizing these rather abstract but important goals. (For us: “the right to participate in science” “individual discovery as a meaningful contribution to knowledge even in the absence of generalizability” “personal agency in determining one’s own research question and in control of data”)
  2. a common protocol/ritual for sharing knowledge (For us: First person point of view, and answering the three “prime questions”: what did you do, how did you do it, what did you learn.) These high level agreements and common structures create a scaffold for the different kinds of participants to begin to make their own contributions. As evidence, here’s a recent paper that attempts to theorize some self-research practice. It is based on the researchers own surveys, prototypes, and pilots, but it is deeply informed by their engagement with the wider community. It sticks to its lane (at least explicitly) in offering an academic contribution, but the implications will be clear to others “on the scene” wondering how to make their tools more effective: https://www.frontiersin.org/articles/10.3389/fdgth.2020.00003/full.

Gary Bernhardt / Execute Program seems like a decent example, as does Julia Evans / WizardZines.

Not all of the ideas explored in their work are fully articulated, generalized, and published, so some of it gets lost in tweets. But, ExecuteProgram has an internal representation of the conceptual dependencies between lessons. WizardZines is also not producing strictly generalizable academic insights, but a lot of the practical tools-for-making and how-to-think-about-making are reflected. in the blog, e.g. https://jvns.ca/blog/2020/06/14/questions-to-help-you-learn/

Both seem like they have the full cycle going - make tools (for learning, not for thought exactly), publish them, observe their use, distill insights, share.

I started auto archiving all the URLs I visit to the wayback machine today. Hopefully this will help with link rot…

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