Tana Tour with Maggie Appleton: Content pipeline, daily template, decision making, and travel

TL;DR

Ontologies (typed nodes with fields) are the foundation for making notes queryable and dashboard-driven rather than static storage.

Briefing Cornell Notes

Briefing

Ontologies—structured “types” with fields—are the missing ingredient that makes Tana feel like a true writing and research system rather than just a note bucket. Maggie Appleton built her workflow around day-level templates, structured writing objects (like “garden notes” and “patents”), and live queries that turn those fields into dashboards for deciding what to work on next. The payoff is practical: she can scan how her thinking changes over time, sort ideas by motivation and status, and navigate from a concept to its sources and related work without drowning in raw backlinks.

Her daily setup starts with “super tags” that populate every day page with default content. She keeps a loose time log, a “what’s on your mind” field, and uses conditional visibility so fields stay hidden unless they’re filled in—reducing clutter while still preserving a consistent structure. A live query then surfaces only the days where “what’s on your mind” was actually added, letting her quickly review themes across weeks and spot gaps where she didn’t write. The same field discipline powers dynamic tables: with consistent day fields, she can generate cross-time views of her activity (for example, tracking writing output) without manually compiling spreadsheets.

For writing, Appleton’s core objects are “garden notes” (essays/notes) and “patents” (design patterns defined as solutions to recurring problems). Each patent carries fields that track creation status (idea → ambient research → active synthesis → alive in public), a public growth stage (seedling/evergreen-style maturity), and a “personal motivation” level that ranks how strongly she’s compelled to write it. She also uses fields for “problem,” “context” (what the pattern relates to), “solution,” and research tracking. Live queries then group and sort these cards by stage and motivation, producing a contextual board that effectively answers: what should get attention now, and what might never be worth touching.

Navigation and signal improve further through structured relationships. Instead of treating backlinks as a giant list, she uses a dedicated “source” field to indicate which articles or papers a piece draws from. That makes back-references higher signal: when she opens a node, she can see which items cite it as a source, while “related to” can remain broader and include non-article connections like tweets or concepts. She also values inline opening of nodes and reference counts to surface back-references without losing context.

Appleton’s approach extends to decision-making and question tracking. She uses a “decision” structure to break down reasoning into components—costs, benefits, known unknowns, and nested working hypotheses—starting from small debates (like whether to buy an ice cream maker) and scaling to larger ones (like buying a house). Questions function as an investigative primitive: once questions exist, they can be turned into queries that pull in older notes that were relevant before the question was even formalized.

Looking ahead, she’s most excited about ontology-based apps paired with AI augmentation—where users can create machine-readable structure, and AI can then help rewrite, prompt, and generate new thought paths. Her advice to new users is outcome-focused: start simple with a few essential tags/types, add more as needs emerge, and begin with a clear purpose rather than trying to design the entire schema upfront.

Cornell Notes

Maggie Appleton’s Tana workflow centers on ontologies: structured “types” with fields that make notes queryable and dashboard-ready. She uses day templates (“super tags”) with conditional fields so “what’s on your mind” and time logs appear only when filled, then relies on live queries to review patterns across days. For writing, she models “garden notes” and “patents” with fields for status, growth stage, personal motivation, problem/context/solution, and research tracking—then groups and sorts them to decide what to work on next. Instead of treating backlinks as a noisy dump, she uses a “source” field to keep references high-signal and navigable. She also applies the same structure to decisions and question tracking, and expects AI to become more powerful once users provide machine-readable structure.

Why does Appleton treat ontologies (structured types) as the key missing feature in note-taking tools?

She describes ontologies as “structured types” such as book/person/company, with fields that can be queried cleanly. In her experience, earlier tools forced workarounds—like encoding type information into node names or attributes—so querying and using the structure “the way you wanted” never worked properly. She also connects this to her prior interest in ontology-based systems (including work tied to the semantic web), which made Tana’s ontology-first design feel like the version she had been waiting for.

How does the “what’s on your mind” daily field avoid clutter while still enabling later review?

Appleton sets default content on every day page via super tags, including a “what’s on your mind” field. Most days she doesn’t fill every field, so she uses conditional hiding rules (e.g., “hide when empty,” “only show it when empty,” or similar conditions) so blank fields don’t appear. A live query then shows only days where the “what’s on your mind” field was actually added, letting her scan her evolving themes without a wall of empty entries.

What makes her “patent” writing objects actionable rather than just descriptive?

Each patent is a structured node with fields that map directly to workflow decisions: creation status (idea/ambiently researching/actively synthesizing/alive in public), public growth stage (seedling → evergreen-style maturity), and personal motivation ranking (e.g., “born to write this” vs weaker interest). Live queries group cards by stage and sort by motivation, so the board becomes a prioritized plan for what to write or tend next, not just a library of drafts.

How does she keep backlinks from becoming overwhelming?

Instead of relying on raw backlinks as a giant list, she uses a dedicated “source” field to mark which items serve as sources for an article/piece. When she opens a node, the back-reference view becomes more meaningful because it’s filtered by that structured relationship. She contrasts this with Rome-style backlink dumps (e.g., “150 back links”) that are hard to read and require manual extraction of signal.

How does the decision structure work in practice, even for small choices?

She uses a “decision” tag to force reasoning into components: a working hypothesis, costs, benefits, and “known unknowns” that become their own questions with nested hypotheses. She gives an example of debating whether to buy an ice cream maker: she captured research notes and hypotheses about costs/benefits and uncertainties in a structured way. She says this same approach scales to bigger decisions like buying a house, where logistics (mortgage rates, prices, broker fees, taxes) can be decomposed into answerable sub-questions.

What is her core advice for new users building a Tana system?

Start outcome-focused: define what you want the system to produce (e.g., well-researched writing and tracking work problems/questions). Then begin simple—create a few essential tags/types (like person or company) and start adding as you go. She emphasizes that schema design should emerge from actual use; she even mentions creating new types/tags later when unexpected needs appear (like flight tracking or other project-specific categories).

Review Questions

Which specific fields in Appleton’s “patent” model determine prioritization, and how do live queries use them?
How do conditional field visibility and live queries work together to make day-level review practical?
What’s the difference between using a “source” field versus a general “related to” relationship in her navigation workflow?

Key Points

1
Ontologies (typed nodes with fields) are the foundation for making notes queryable and dashboard-driven rather than static storage.
2
Day-level templates with conditional field hiding let users keep consistent structure without cluttering empty entries.
3
Live queries can turn structured writing objects into prioritized worklists by grouping and sorting on fields like status and personal motivation.
4
Structured relationships (especially a dedicated “source” field) keep backlinks high-signal and easier to act on than raw backlink dumps.
5
Appleton treats questions and decisions as first-class workflow objects, breaking reasoning into component questions with working hypotheses.
6
AI augmentation becomes more useful when users provide machine-readable structure that the system can prompt, rewrite, and extend.

Highlights

Appleton’s “what’s on your mind” workflow uses conditional visibility plus a live query to show only the days where she actually wrote something—turning scattered journaling into an analyzable timeline.

Her “patent” model turns writing into a decision system: creation status and personal motivation feed live grouping/sorting so the board becomes a prioritized plan.

Instead of drowning in backlinks, she uses a “source” field so back-references function like curated citations rather than a noisy list.

Her decision template decomposes reasoning into costs, benefits, and “known unknowns,” each tied to working hypotheses and nested questions.

Topics

Ontologies
Tana Workflow
Live Queries
Writing Pipelines
Decision Tracking

Mentioned

Maggie Appleton
GPT-3