Skip to content

SSTorytime

Profile silhouette of a head on the left; a network of labeled concept nodes — NETWORK, IDEAS, CONTEXT, STORY, MEMORY, PERCEPTION — expands outward to the right like a cognitive map.

Keep notes that remember how they are connected — and ask them questions later. An NLnet-sponsored project by Mark Burgess / ChiTek-i.

Most notes go flat. You jot something down, and a week later you can't find it because the thing you typed then is not the thing you are searching for now. SSTorytime lets you write the connections alongside the content — what a book is about, what it cites, what came before, who said what — and then asks the graph, not a search box, when you want an answer.

What you can do with it

  • Capture what you know, with the connections. Write notes in N4L, a plain text notation for statements like "this book is about that topic", "this decision came before that one", "this person said this". The graph comes out of the text you wrote — no schema to design up front.
  • Ask questions that follow the connections. What connects these two papers? What have I read that is about decision-making? What did this meeting lead to? Questions that would be painful in SQL or a note-taking app are one line here.
  • Keep the shape of your thinking. Your N4L files in version control are the source of truth; the database is a cache that makes queries fast. Your graph is yours, local, and visible.

Is this for you?

  • You want to capture something

    A research trail, a decision log, a reading list, a family tree, a set of meeting notes. You have the material in your head; you want it in a shape you can ask questions of.

    Your first story

  • You have a corpus already

    Notes, papers, transcripts. You want to explore the relationships, find paths between things, pull out what is near a given idea.

    Finding things

  • You want the "why" first

    Semantic spacetime, context as a first-class citizen, and what this approach buys you over the alternatives.

    Semantic spacetime in plain English

One concrete example

Here is a small reading list written in N4L:

- reading list

 :: books, topics, authors ::

 Thinking Fast and Slow   (about)    decision making
        "                 (by)       Daniel Kahneman
        "                 (bib-cite) Judgment under Uncertainty

 Superforecasting         (about)    decision making
        "                 (by)       Philip Tetlock
        "                 (bib-cite) Thinking Fast and Slow

 Thinking in Systems      (about)    decision making
        "                 (by)       Donella Meadows

Load it, then ask: what have I read about decision making?

0: "decision making"    in chapter: reading list

      -    (is the topic/theme of) - Thinking Fast and Slow
           -    (is a bibtex citation label for) - Superforecasting
      -    (is the topic/theme of) - Superforecasting
      -    (is the topic/theme of) - Thinking in Systems

Three books. You asked about a topic; the graph also noted that Superforecasting cites Thinking Fast and Slow — surfaced by the same query, indented under the book it connects. You did not ask for the citation. It came along because it sits one hop away from something you did ask about.

The full version of this reading list is at examples/reading-list.n4l and is the running example in Your first story.

Install

About five minutes from a fresh checkout: Install in 5 minutes.

Background reading

A book on the conceptual background (Smart Spacetime, Mark Burgess) is available. It is conceptual background, not a tutorial.

Medium essays for deeper context:

Discussion: SSTorytime LinkedIn Group.