Systems Architecture · P-real-26

Full Knowledge Map Of All Things

Atoms of Knowledge, Built Into Blocks

A Very Big Question, opened with “we're going to Look Up here”:

The Prompt

Hey, we're going to Look Up here, and we're going to take on a Very Big Question- how do we pre-build a Map of All Knowledge, that we will build a system to fill in. Right now we have the small bunch of KPs that we have started with, but there is a MUCH MUCH bigger knowledge graph. I want you to throw out what we have right now, in terms of the graph we've built with our KPs, and start over. You have a table in front of you with a vertical divider down the middle- on the left are little tiny blocks- the basic ATOMS of our Knowledge- all of it- All the Things Humans Have Learned- how much water weights, the temperature that taffy melts, Taylor Swift's birthday, the reasons human babies are born when and how they are, and who the last nine princes of Austria are. All of It. Your job is to build those atoms up into bigger blocks of an appropriate size and scope so we can then take THOSE blocks and assemble our Full Knowledge Map of All Things, that we will then endeavor to fill. When I ask you "what are the next 100 KPs to make", I want that answer to be easily derived from our understanding of the Full Knowledge Map of All Things. Seems like right now we are Reading when it comes to thinking of the next 100, when we should be Deriving. Burn the rest of this session just thinking of that and post hand off docs. We will pick this conversation up after the hand off. Nice work. This is important work.

— Shea Gunther · operator drop, 10.2006

This prompt A pre-built map of all knowledge that a system then fills in — throw out the ad-hoc KP graph, start from the atoms of everything humans have learned, build them into right-sized blocks, and assemble the blocks into a map whose empty cells name the next KPs to make.

The aim The Full Knowledge Map (v2 — twelve domains, one hundred sixty-two fields).

Atoms → blocks → map12 domains, 162 fieldsDerive the next 100 KPsReading → Deriving

A stated aim that shipped — the Full Knowledge Map (PiF, permanent project resident): the content-planning layer that turns “what should we build next?” from a brainstorm into a read off the map's empty cells.

The Moment

The image carries the whole architecture. A table with a vertical divider: on the left, the atoms of knowledge — how much water weighs, the temperature taffy melts, Taylor Swift's birthday, why human babies are born when and how they are, the last nine princes of Austria — all of it. The job is to build those atoms up into blocks of the right size and scope, then assemble the blocks into a Full Knowledge Map of All Things that the system will then set out to fill. The operator asks for the existing KP graph to be thrown out and rebuilt from the atoms up.

The load-bearing line is the last one about method. Today, choosing the next hundred KPs is an act of Reading — brainstorming, generating ideas. With the map, it becomes an act of Deriving — the empty cells are the answer; you read the next hundred off the gaps. “Burn the rest of this session just thinking of that and post hand off docs.” It is a prompt that asks for a planning instrument, not a plan.

The wider frameA good map turns generation into reading. “What should we build next?” stops being a brainstorm and becomes a lookup — the empty cells are the backlog, already sorted by the structure of knowledge itself.

The Anatomy

“on the left are little tiny blocks- the basic ATOMS of our Knowledge- all of it … build those atoms up into bigger blocks of an appropriate size” — The construction rule: bottom-up from atoms to right-sized blocks. The map isn't imposed from the top; it's assembled from the smallest true units of knowledge upward.

“when I ask you 'what are the next 100 KPs to make', I want that answer to be easily derived” — The acceptance criterion, stated as a use case: the map is good if it makes the next-hundred question a derivation. That's the test the finished instrument has to pass.

“Seems like right now we are Reading … when we should be Deriving” — The whole point in one contrast. Reading is generating ideas; Deriving is reading them off a structure. The map exists to move corpus planning from the first to the second.

Computational Profile

Words in the prompt≈ 277

ProvenanceAim — shipped as the Full Knowledge Map

Scale12 domains · 162 fields

The shiftReading → Deriving the next 100 KPs

CategorySystems Architecture

Reading the Operator's Shorthand

The prompt is written in the working vocabulary of the methodology. Here is what the shorthand means:

KP: Knowledge Pack — a self-contained reference document the board can pull in to ground a discussion in a specific domain.
look up and down: An instruction to check the work at every scale at once — the small detail and the whole-system shape — before moving.

P-real-26 · Systems Architecture