In the past week I’ve started three personal experiments that use AI (in this case Claude Code). For each, the experiment lies in automating steps in my cognitive work that are useful or necessary but not the actual cognitive work itself. They’re helper activities, supporting the main task. For two of the three that is the clear focus, the third is slightly different.

The three experiments are:

• Filtering on interests in my feed reader, let’s call it ‘Weak-tAIs’.
• ‘Slopsidian’, lifting concepts and argumentation from papers into Obsidian notes, and linking them iteratively.
• Explore questions with pre-existing ‘recipes’ that take a specific philosophical perspective. Perhaps I should dub this type of language game ‘WittgenstAIn III’.

It started from an automation task, which I mentioned here: manipulating non-fiction e-books. I have a script that I can point to an e-book in my Calibre collection, and then will populate a note with elements from the book: foreword, index and literature list, content overview, all if present, and for each chapter of the book the first and last few paragraphs. This is what I look at and skim whenever I want to gain a first impression and understanding what a book is about, and what questions it addresses or what it proposes. All very Mortimer Adler. From it I can then decide which parts of a book to read more closely, which parts likely contain things I am already familiar with or fall outside my current interest in the book. From those skims I jot down things in my note for the book. This quickly turned out to be useful to me, because it removed the wall between the e-book and my notes by bringing parts of the e-book into my notes temporarily where I could more quickly go through them in preparation for ‘proper’ reading (although in fact it is part of reading).

It got me thinking what other helper activities in reading and filtering I could identify.
Helper activities are tasks that support a main task by making it easier or providing guard rails. Checklists are an example, they ensure that you don’t skip important steps. In most cases nothing will immediately go wrong if you don’t do the helper activity but if you do them the main task gets a little easier to do well. A lot of helper tasks can be regularly automated, like the e-book excerpt script above. Others less so because they contain elements of processing actual texts, like the three experiments I describe here. There perhaps using a model like Claude Code can be of value (and hopefully soon, through local model deployment).

A brief description of the three experiments:

Weak tAIs
I order my RSS feeds by social distance for reading. Part of the reasoning is that I want to be well informed about what close ties write, but I am aware that interesting information likely comes from a wider social distance. This practice has been in place for some two decades and enormously valuable all that time. The most interesting stuff usually comes from the third layer, a folder named ‘c150’, in my feedreader: close enough to know who the author is, and engage in interaction if I want, disconnected enough for them to encounter things I am less likely to have already seen myself. That is the The Strength of Weak Ties (1973) as Granovetter called it.

I also keep a list of current interests, a bit like Feynman’s dozen or so currently favourite problems. For each interest I have formulated a few aspects:

• what is conceptually interesting to me in a topic (e.g. my interest in EU digital and data policy conceptually is that it forms a geopolitical proposition externally, while being a quality improvement instrument internally that takes rights and societal values as yardstick),
• am I theoretically interested or more practically,
• do I have a knowledge fundament for the topic or am I a newbie,
• is there a link with any long term goals,
• can it be put into a specific context or tied to a specific issue/question,
• can I shape or create an enduring practice around it,
• can I build a bridge to outputs, like blogposts, presentations, or client proposals

My feedreader tracks just under five hundred people writing on the open web. That can easily amount to two thousand postings in a week. I can have several intentions to start reading, one of them is to find and read material relevant to my list of current interests. A reading intention does not do away with items, it’s not a filter to remove material. It’s essentially just a view on the entire set of incoming items in the feed reader that I usually construct in my mind. What if I can construct those views on my screen too?
The ‘c150’ social layer, the weak ties, what do they write about that connects to the fields of interest from my list? Such filtering does not lend itself to text based search based on fixed terms. I usually skim titles for first impressions, and click opportunistically through the postings. What if I can have a model weigh the postings and compare them to my list of current interests, to mark them for my attention? In aid of that one specific reading intention.

That’s what the first experiment does: label postings that seem to fit my interests, and express why. So that I can skim the folder of weak ties by interest, and read those items first if my intention is to explore those interests. I limited it to the c150 folder as feeding all rss feeds into the model is consuming a lot of time and tokens, so I started with the part most likely to bring useful results.
The labeling works now as part of my feedreader. I am not yet convinced of the quality of it though. The motivation for the labels usually is along the lines of "it fits interest X but not in the way you’re looking for", which to me means it actually doesn’t really fit.

Slopsidian
This week I read an article about AI documenting its own actions and output in a wiki, and saw one or two similar efforts described. I applied that to a different helper task, which is the preparation of reading a paper and helping me to decide to dig deeper. This is similar to skimming a non-fiction book, but more involved. Can AI reliably pull from a paper the concepts used and introduced, and the line of argumentation? Saving them both in a single note for the resource, and in separate notes for each of the concepts? Additionally can it logically link concepts from different resources? This is what an ‘ingestion skill’ now does for me. I let it store the output it generates in a folder that I can also open as an Obsidian vault, hence the name Slopsidian. The papers come from my Zotero collection, meaning I previously saved them. That original step of curation also means I have a line or two about why I thought them interesting at the time. Feeding that curating decision and the paper into the ingestion skill allows a second order look at a paper. What are the concepts discussed, and, reading the output, do I think some of those are of interest to me? If so, I can look at the paper more closely and do my own note making and paraphrasing and placement in my actual Obsidian collection. Lifting out concepts works rather well, the linking is less useful in the first experiences (too obvious, not sparse enough) and can seem forced when you look at why some concepts get linked.

WittgenstAIn III
The third experiment is a bit more on the edge I think. Here the probabilistic language games that LLMs are have more of a free rein. Part of the university courses on philosophy of science I did 25 years ago was using different philosophical schools of thought as lenses to approach a question. Not to answer the question, that is hardly ever the point after all, but to holding it, and holding it differently. Plato’s essentialism, Kant’s transcendence, dialectics (Hegel), phenomenology (Husserl), Wittgenstein II’s analytical method, hermeneutics (Heidegger), deconstruction (Derrida), and Rorty’s pragmatism. For each of these, for over 2 decades, I’ve had a recipe in my notes to apply to a question.
I put together a ‘language game’ in which I pose a question, which a ‘router’ prompt tries to match to one or more of the 8 recipes, or to a combination of recipes chained together (e.g. first look at a question from an analytical perspective and then feed the results in to a deconstruction exercise.)
My existing multi-step recipes are followed, and output is generated for each of those steps, into a resulting note.
I read those resulting notes, lift out what catches my eye or what resonates and I use it to flesh it out more, for me to hold the question still longer. Models are language games of a sort, so hence the name WittgenstAIn III, a third iteration, extending the second Wittgenstein’s language games to and with AI.
The output here makes me more uncomfortable than the other two. Reasoning is being mimicked, with the usual overconfident wrongness we’ve come to expect from generative AI, and that works out in odd ways sometimes. Still there is utility that can be lifted from the output. It is a good kickstart for exploring questions to quickly see if a recipe might yield something or not, judging by my first attempts in this experiment. It does certainly lower the threshold, as helper task, to engage with the recipes. I’ve used it more in the past days than in the past months. Part of that is the novelty of the experiment, and that may wear off quickly, but perhaps it carries the kernel of more habitual use.