Een amanuensis (meervoud: amanuenses) is een assistent op natuurkundig, biologisch en/of scheikundig terrein op een school of in een laboratorium. De term kan ook naar een klerk of secretaris verwijzen. Een amanuensis kan bijvoorbeeld op een middelbare school natuurkundige, scheikundige of biologische proeven voorbereiden, en kan verantwoordelijk zijn voor het onderhoud van de technische hulpmiddelen en instrumenten voor natuurkundig, biologisch en scheikundig onderwijs aan die school. Een dergelijke amanuensis op school wordt ook wel een technisch onderwijsassistent (toa) genoemd.

Takeaways This list isn’t comprehensive. I’m still experimenting and would love to learn from your experiments as well.

9. Reflector This final role is different. Whereas the others took as the object of inquiry a particular work — e.g., a novel or a movie — this last one takes as the object your knowledge garden itself. That is, you point the LLM to a series of notes to analyze patterns over time and suggest improvements. Example: I fed all 52 weekly posts from my humanities crash course to Claude Code, and asked it to identify the various roles in which I used AI for learning throughout the year. Its answers — with some curation from me — are the roles you just read. Suggested prompt: Here are my notes from [X weeks/months] of reading on [TOPIC]. What patterns do you notice in what I pay attention to? What do I seem to find most interesting, and what do I seem to avoid or underweight?

8. Mapper This one’s a bit more esoteric. Some people — me included — are primarily visual: diagrams and drawings aid our understanding. Concept maps can be especially helpful. I’ve built an Agent Skill to allow LLMs like Claude draw concept maps. (Download it from Github.) Example: I used this mapping skill to generate a concept map of Virginia Woolf’s To the Lighthouse. It’s not especially insightful, but more of a proof point of using LLMs in a more visual modality. Suggested prompt: (Note: install my LLMapper Skill before issuing this prompt) Generate a concept map for [WORK] centered on the question: “How does the novel’s treatment of [THEME] illuminate [BROADER QUESTION]?”

7. Analyst This role will also help you appreciate a work from a different perspective. It’s easy: you ask for the LLM to apply a specific critical lens to a reading. Common lenses include Freudian, Marxist, feminist, Girardian, etc. Example: The same week I read Freud, my son and I watched Predator, the 1980s sci fi film starring Arnold Schwarzenegger. For fun, I asked ChatGPT to analyze the film through a Freudian lens. The result was both enlightening and hilarious. Suggested prompt: Apply a [Marxist / feminist / postcolonial / Jungian] reading to [WORK]. What does this lens reveal that a neutral summary would miss?

6. Adversary Here’s a fun role: asking for an LLM to push back on your position or steelman the opposing point of view. The idea is to expand your understanding by bringing your assumptions to the surface and challenging them. Example: After watching Modern Times, I asked ChatGPT to correct my understanding of the movie as a work of Marxist propaganda. The LLM convinced me that the film is in fact more of a humanist statement than a political one. As a result of this interaction, I changed my mind on Chaplin’s work. Suggested prompt: Here are my notes on [TOPIC]. Please help me see it through the lens of someone who might be sympathetic to [OPPOSING POSITION] without fully realizing it. What could I improve? Where is my argument weakest? [paste notes]

5. Recommender This is a useful role for deepening your understanding of a subject: asking for related works that reflect similar themes. It’s also a use case where I noticed considerable improvements in LLM performance over 2025. Example: Early in 2025, I read Confucius’s Analects. Perplexity was ahead in web-backed interactions at the time, so I asked it for a list of classic Chinese movies that reflected Confucian values. It responded with five suggestions, some of which it hallucinated. But one of them, Spring in a Small Town, was a bona fide classic — and I likely wouldn’t have learned of it without an LLM. (Later in the year, other chatbots gained this ability and hallucinations dropped across the board.) Suggested prompt: I just finished [WORK]. Recommend three films that explore similar themes or ideas. Prioritize films with strong critical reputations — I’d rather have one great recommendation than five mediocre ones.

4. Orienter This role is something of an inversion of the validator. Instead of asking for feedback on your notes after reading a text, here you ask the AI for guidance before reading. You’re looking for framing, historical context, high level outlines, etc. — ideally, without spoilers. Example: Before reading Nietzsche’s Beyond Good and Evil and Tolstoy’s The Death of Ivan Illych, I uploaded both books to NotebookLM, which created a podcast for me that explained their thematic contexts. Listening to this podcast in my daily walk helped me better understand the readings. Suggested prompt: I’m about to read [WORK] for the first time. Give me enough context to make sense of it — historical background, key arguments, things to watch for — but don’t spoil the experience of discovering it myself.

3. Connector Here’s yet another role you can easily do via chat: identifying thematic, philosophical, or narrative parallels between works. Note I wrote “works” — it’s fun and illuminating to ask for connections across media, genre, time, etc. Example: I watched Francis Ford Coppola’s The Conversation on the same week I read Oedipus Rex. For fun, I asked ChatGPT for possible parallels between the two works. Its reply was enlightening: it pointed out how the protagonists of both stories undertook an obsessive investigation that uncovered terrible knowledge. Suggested prompt: I’ve been reading [WORK A] and [WORK B]. What philosophical or thematic threads connect them? I’m looking for non-obvious resonances, not surface similarities.

2. Validator Another basic role for AI is validating your understanding. To do this, you ask it to review your notes for errors or gaps, do basic fact checking, or critique your reasoning. Again, you can do this via the chat interface, but I also experimented with passing my notes in Obsidian using the Copilot plugin and in Emacs using gptel. Example: After reading The Epic of Gilgamesh, I wrote a note in Obsidian summarizing its plot. When I asked ChatGPT to critique my summary, it pointed out that I’d given the central character a redemption arc that isn’t present in the text. I’m so accustomed to the standard hero’s journey, that I projected it onto the book — and an LLM helped me correct this ‘hallucination.’ Suggested prompt: Here are my notes on [WORK]. What important ideas did I miss or underemphasize? Don’t rewrite my notes — just flag the gaps.

1. Tutor The simplest role for AI is as a tutor. You ask it to explain a difficult concept, clarify a confusing passage, translate jargon, etc. I mostly did this via the standard chat UI (although I created a ChatGPT project to preserve context for the course.) Example: While reading Freud’s The Interpretation of Dreams, I came across three unfamiliar German terms: es, ich, and über-ich. ChatGPT helpfully explained these are more commonly known as id, ego, and superego — three terms I already understood. Suggested prompt: I just read [PASSAGE]. I understand [X] but I’m confused about [Y]. Can you explain [Y] in plain terms, without assuming I have background in [FIELD]?

I consider amanuensis to be the ideal role for AIs in your knowledge garden.

Some early modern scholars employed live-in secretaries to do various tasks for them: researching, indexing, archiving, retrieving, organizing, translating, summarizing, and running errands. While not as famous as their employers, these people were often seen more as collaborators than anonymous servants. They were called amanuenses

I get most value around the middle of the spectrum. There’s a historical precedent here.

In these instances, he could outsource partsof the work process to his personal amanuenses, his youngest children,Martha and Friedrich, who acted as scribes and copied book passagesthat he had marked.

Wordcraft shined the most as a brainstorming partner and source of inspiration. Writers found it particularly useful for coming up with novel ideas and elaborating on them. AI-powered creative tools seem particularly well suited to sparking creativity and addressing the dreaded writer's block.

In addition to specific operations such as rewriting, there are also controls for elaboration and continutation. The user can even ask Wordcraft to perform arbitrary tasks, such as "describe the gold earring" or "tell me why the dog was trying to climb the tree", a control we call freeform prompting. And, because sometimes knowing what to ask is the hardest part, the user can ask Wordcraft to generate these freeform prompts and then use them to generate text. We've also integrated a chatbot feature into the app to enable unstructured conversation about the story being written. This way, Wordcraft becomes both an editor and creative partner for the writer, opening up new and exciting creative workflows.

For instance, if the user selects a phrase, a button to "Rewrite this phrase" is revealed along with a text input in which the user can describe how they would like the phrase to be rewritten. The user might type "to be funnier" or "to be more melancholy", and the Wordcraft application uses LaMDA and in-context learning to perform the task.

Digitizing and compressing notes - Question

Do not depend on the typistfor any service or assistance except actual copying.

Are you scanning all your analogue note cards?