CONTEXT.md

# context

the winning default context for language models

## the idea

every model starts empty. context is the only lever between raw capability and useful intelligence. most contexts are ad-hoc — a system prompt, some examples, maybe a document dump. this repo builds something different: a knowledge graph about intelligence itself, ranked by its own relevance algorithm, packed to fit any context window

the source is [cyber](https://github.com/cybercongress/cyber) — a knowledge graph for planetary superintelligence. ~2300 pages covering: the cybergraph (a directed authenticated multigraph over content-addressed nodes), the tri-kernel (diffusion + springs + heat operators that compute collective focus), consensus, cryptography, epistemology, game theory, information geometry, and the full protocol specification

every page carries its own tri-kernel score in the frontmatter:

```yaml
diffusion: 0.030264  # PageRank — where probability flows
springs: 0.000951    # neighbor equilibrium — structural constraints
heat: 0.010356       # multi-scale smoothing — context at resolution τ
focus: 0.017489      # composite: 0.5D + 0.3S + 0.2H
gravity: 342          # inbound links
density: 10.07        # outbound links per KB

the packer uses these scores to select the most valuable pages for each context budget. highest focus first, greedy knapsack until full

sizes

usage

drop any size file into your model's system prompt or context window:

# claude code
cat 200k.md | claude --system-prompt -

# api
{"system": "<contents of 128k.md>", "messages": [...]}

# local llm
llama-cli -m model.gguf --system-prompt-file 32k.md

build

requires cyber repo with analizer/context.nu

nu build.nu                                # all sizes
nu build.nu --sizes [128 500]              # specific sizes
nu build.nu --cyber-path ~/my/cyber        # custom path

why this context wins

1. self-describing: the knowledge graph contains its own theory of knowledge, attention, and relevance. a model reading it understands what it is reading and why

2. mathematically ranked: pages are selected by the same tri-kernel algorithm the protocol uses for consensus. not curated by hand — ranked by graph structure

3. compositional: every page uses wiki-links. the model sees the full link topology and can reason about relationships between concepts

4. dense: the graph covers mathematics, cryptography, game theory, information theory, biology, economics — all unified under one protocol. maximum knowledge per token

5. recursive: the context describes the process that generated the context. the model can verify and improve the ranking

license

Don't trust. Don't fear. Don't beg.