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Tana Fundamentals 01 – Getting Started with Tana thumbnail

Tana Fundamentals 01 – Getting Started with Tana

CortexFutura Tools·
5 min read

Based on CortexFutura Tools's video on YouTube. If you like this content, support the original creators by watching, liking and subscribing to their content.

TL;DR

Tana keeps the speed of an outliner (quick note creation, indentation, and focus views) while adding cross-note linking and synchronized inline expansion.

Briefing

Tana’s core pitch is that it behaves like an outliner on the surface, but turns every note into a connected, queryable object—so work can be pulled into place, kept up to date, and even drafted or extracted with AI. Instead of writing in isolated pages, users create fresh notes quickly (type a title, press Enter for a new note, Tab/Shift-Tab to indent, and “focus” into a note for a dedicated page view) while gaining the ability to reference, link, and restructure information without hunting across the workspace.

The biggest leap beyond a standard outliner is linking and referencing. Notes can be pulled into the current context in two ways: by typing “@” and selecting a target note (which inserts the note’s contents inline and keeps edits synchronized back to the original), or by creating a link and using Shift-click to expand it in place. That synchronization matters because it lets action items and meeting notes travel forward in time—e.g., a product strategy meeting captured on Wednesday can be referenced during a later product review, with fields like “Q1 2025” staying consistent wherever the note is expanded.

Tana then adds structure through Fields, letting users attach metadata to notes and tasks. A meeting note can include an “attendees” field, while each task can include an “assigned to” field. When users focus on a person, Tana can show a reference section listing where that person’s name appears—both as an attendee and as the assignee for specific tasks—creating an explicit relationship between otherwise separate notes.

Super tags turn that metadata into reusable “things.” Tagging a note with #task automatically applies a bundle of fields (such as status, due date, and related project) so users don’t have to recreate the same schema repeatedly. Tags can also be extended: a #colleague tag can inherit the fields from the built-in #person tag, letting teams define specialized categories without losing the underlying structure.

Once tags and fields exist, searches and live feeds become the operational layer. Clicking a super tag surfaces all matching notes in a sortable, filterable table; filters can be saved as searches like “open tasks.” Views can switch to a Kanban board or group tasks by assignee. Users can also create ad hoc searches by typing “?” and building conditions such as “task where due date is today,” with results updating as dates change. Location-aware queries using “parent” make searches portable across the hierarchy—so a colleague-specific search can automatically pull tasks assigned to that colleague wherever it’s placed, and it updates live when new tasks appear.

Finally, AI integration ties the system together. On the Core plan, pressing Space in an empty note opens an AI chat, but the more distinctive workflow is AI that uses workspace context. An “investor update” draft can be generated from a live search of completed projects, summarizing only the relevant notes. The reverse is also supported: voice capture (via command-driven capture voice memo) can be transcribed, then AI extracts tasks and tags them automatically—so dictation becomes structured work items. The throughline is clear: Tana aims to keep information connected, continuously current, and actionable—without forcing users to leave their workspace to manage it.

Cornell Notes

Tana starts as a fast outliner—new notes, indentation, and “focus” views—but it becomes powerful by linking, structuring, and continuously querying information. Notes can be pulled into other places via @ references or expandable links, with edits synchronized back to the original. Fields add metadata like meeting attendees and task assignees, and super tags bundle those fields into reusable “things” such as #task, including inheritance (e.g., #colleague extending #person). Searches act as live feeds: filters and “parent”-based location awareness keep results up to date as new notes are created. AI then leverages those live, structured inputs to draft summaries (like investor updates) and extract/tag tasks from voice memos.

How does Tana let users reuse an old note inside a new context without losing synchronization?

Users can insert another note’s contents inline using an @ reference: type “@” and select the target note (e.g., a Wednesday product strategy meeting). The selected note expands in place, and edits made in the expanded view (like changing “Q1 2025” to bold) propagate back to the original location. Alternatively, users can create a normal link and Shift-click it to expand the linked note in place, again allowing edits that reflect in the source note.

What role do Fields play in turning notes into structured work items?

Fields attach metadata to notes and tasks. A meeting note can include an “attendees” field listing names (e.g., Peter, Amy, James). Under each task, a field like “assigned to” can reference the assignee. When focusing on a person, Tana can show a reference section listing where that person appears—such as being an attendee and receiving a specific task—making relationships between notes explicit.

Why are super tags more than just labels in Tana?

Super tags bundle a predefined set of fields and apply them automatically. Tagging notes with #task instantly adds fields like status and due date, plus related project, so users don’t manually recreate the same schema for every task. Super tags can also be extended: a custom #colleague tag can be configured to extend the built-in #person tag, inheriting its fields so colleague notes gain the same structure while remaining distinct.

How do searches become “live feeds,” and what does “parent” location awareness change?

Searches can be saved from filtered super-tag views (e.g., “open tasks”) and update as matching notes change. Users can also build queries with “?” and conditions like “task where due date is today.” Using “parent” makes the query relative to the note’s position in the hierarchy: if a search is placed under a colleague note, “parent” ensures it pulls tasks assigned to that specific parent note. This keeps the feed correct wherever the search node is reused.

What makes Tana’s AI integration different from a standalone chatbot?

AI can operate on workspace context. For example, an investor update can be generated from a live search of completed projects: the AI draft uses only the information contained in the selected notes (e.g., SSO rollout success, trial-to-paid conversion improvement, geospatial stack speed gains). AI can also work in reverse by extracting tasks and tags from transcribed voice memos captured via command-driven capture voice memo, turning dictation into structured, actionable items.

Review Questions

  1. When would you choose an @ reference versus a Shift-click expanded link, and how does each keep edits synchronized?
  2. Describe how you would model a meeting with attendees and tasks so that focusing on a person reveals both attendance and assignments.
  3. How does using “parent” in a search query affect results when the search node is moved or reused under different notes?

Key Points

  1. 1

    Tana keeps the speed of an outliner (quick note creation, indentation, and focus views) while adding cross-note linking and synchronized inline expansion.

  2. 2

    @ references and expandable links let users pull meeting notes forward into new contexts without manually copying content.

  3. 3

    Fields provide structured metadata such as meeting attendees and task assignees, enabling relationship-style navigation through reference sections.

  4. 4

    Super tags bundle field templates (e.g., #task) and can be extended (e.g., #colleague extending #person) to create reusable schemas.

  5. 5

    Searches function as live feeds: filters and saved queries update automatically as new notes match or change.

  6. 6

    Location-aware queries using “parent” make searches portable within the note hierarchy, supporting per-person or per-project feeds.

  7. 7

    AI drafts and extracts work using live workspace context—summarizing from selected notes and tagging tasks from voice dictation.

Highlights

Inline note expansion via @ references or Shift-click links keeps edits synchronized back to the original note, eliminating copy/paste drift.
Super tags turn notes into structured “things” by auto-applying field sets—so #task instantly gains status, due date, and related project fields.
Saved searches behave like live feeds, and “parent” makes them hierarchy-aware so the same query works under different colleagues.
AI can generate an investor update draft from a live list of completed projects, using those notes as context rather than generic knowledge.
Voice capture can be transcribed and then converted into automatically tagged tasks, turning spoken updates into structured action items.

Topics

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