the winning default context for language models — the cybergraph ranked by tri-kernel and packed to fit any token budget
why cyber is the winning context
the cybergraph is self-describing: it contains its own theory of knowledge, attention, and relevance. a model reading it understands what it is reading and why. every page carries its focus score in frontmatter — the model sees the tri-kernel output directly
six fields per page:
| field | operator | meaning |
|---|---|---|
diffusion: |
$\mathcal{D}$ | PageRank — where probability flows |
springs: |
$\mathcal{S}$ | neighbor equilibrium — structural constraints |
heat: |
$\mathcal{H}_\tau$ | multi-scale smoothing — context at resolution $\tau$ |
focus: |
composite | $\lambda_d \mathcal{D} + \lambda_s \mathcal{S} + \lambda_h \mathcal{H}_\tau$ |
gravity: |
— | inbound wiki-links |
density: |
— | outbound links per KB |
sizes
| tokens | pages | coverage | target |
|---|---|---|---|
| 8K | 11 | 0.5% | local 7B |
| 32K | 30 | 1.3% | GPT-4, local 13-32B |
| 128K | 54 | 2.3% | Claude Haiku, Gemini |
| 200K | 104 | 4.4% | Claude Sonnet |
| 500K | 340 | 14.3% | large context |
| 900K | 780 | 29.0% | Claude Opus 1M |
| 1.4M | 1836 | 68.4% | 2M window, full graph + subgraphs |
build pipeline
# 1. compute tri-kernel, write to frontmatter
nu analizer/trikernel.nu ~/git/cyber
# 2. build all context sizes
nu ~/git/context/build.nu --cyber-path ~/git/cyber
# 3. use
cat ~/git/context/200k.md | claude --system-prompt -
see cyber/context packing for the ranking algorithm. see tri-kernel for the three operators. see focus for the composite measure
from subgraph cyber/context
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 — 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:
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
| file | tokens | target models |
|---|---|---|
8k.md |
8K | local 7B, GPT-3.5 |
32k.md |
32K | GPT-4, local 13-32B |
128k.md |
128K | Claude Haiku, Gemini, GPT-4 Turbo |
200k.md |
200K | Claude Sonnet |
500k.md |
500K | Claude with room for dialogue |
900k.md |
900K | Claude Opus 1M |
1400k.md |
1.4M | 2M context windows, full graph + subgraphs |
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
-
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
-
mathematically ranked: pages are selected by the same tri-kernel algorithm the protocol uses for consensus. not curated by hand — ranked by graph structure
-
compositional: every page uses wiki-links. the model sees the full link topology and can reason about relationships between concepts
-
dense: the graph covers mathematics, cryptography, game theory, information theory, biology, economics — all unified under one protocol. maximum knowledge per token
-
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.