A self-growing meaning-graph — the mycelium of GBC. It holds two beit-midrash on one lattice: the Torah giants (Rashi, Ramban, Ibn Ezra, Sforno, Rashbam, Kli Yakar, Or HaChaim) and the coding canon (C++, Rust, Python). Ask it what a word, root, construct, language, or giant means — it answers with the tones, lenses, and intents it has grown, not been told.
✦ See the whole living graph in 3D — live → or ask it a question below
Type one thing it knows and it shows the meanings it has grown for it — strongest first. Try a Hebrew root root:אור, a Hebrew word lemma:אהב, a gematria value gem:86, a coding idea construct:raii / lang:rust, or a commentator giant:Rashi. Not sure what to type? Tap a chip below — those are all things it has seen. Each bar is a tone/meaning it associates with your node; longer = stronger.
Describe a coding task in plain words. The code-graph (grown from real commit trajectories) predicts the skill, the file, and the symbols the edit would touch.
The mycelium's fruit: type a concept (or leave it blank for a surprise) and it surfaces things from other fields that its structure links to yours — through a bridge you can see. These are candidate connections to investigate, not proven solutions — the honest promise of a glass box; they sharpen as the graph grows.
Point it at any body of knowledge and you get a living graph you can ask three questions of:
The three questions don't care what the corpus is. Point the substrate at a field, and the same shape answers.
Point the code-graph at a model's repo → where does this change land before you grep. Point the tone-graph at papers & behaviors → find resonant approaches by meaning, not keyword collision.
Motifs are roots. Find genes that are functionally resonant — share a motif but no shared name (the paraphrase-gap = homologous function, different annotation). Tone = the pathway context; bias = how much regulation vs expression.
The 24-cell is the quaternion jet-orientation lattice (already flying in Cosmos-Infinite). Run the substrate over telemetry & maintenance logs → next-fault, and failure modes that resonate across airframes.
Formations and signals share signature-roots. Surface regions that resonate with a known structure though the surface reading differs; read a region's bias — how much fault vs fold.
The mycelium isn't a metaphor here — it's the crop. A living soil-health graph over climate/soil/yield → conditions that resonate, warnings that spread through the network like the Wood-Wide-Web it's modeled on.
It is a meaning-layer for scale: seed → feed → self-play → sharpen → query over any corpus, growing & pruning unattended. The answer isn't stored — it's grown, so it survives every reboot.
Compounds that share a mechanism-root but not a molecule (the paraphrase-gap). And the built-in severity × intent axis is made for it: read a candidate as how much efficacy vs how much risk — benefit and harm as orthogonal leans, not one score.
This runs on real compute and API costs, funded out of pocket. If it's useful to you, it grows faster with support — and every bit directly funds the graph's growth.