𓂩𓂁𓂣 𓆈𓇈𓆤𓆗 𓀹𓂱 𓃨𓃊𓇥𓃋𓎙 𓍳𓃋𓃏𓃏𓐫

𓃊𓇈 𓅗𓂘𓆗 𓂱 𓇞 𓂱𓃉𓆗 𓂘𓃏

Watch 30 second demo

Hologram Brain — 32-node state, live

𓇠𓃏 𓄆𓂣 𓇈𓇈 𓃨𓇈 𓅗 𓂱𓇈𓃨𓆗𓃏 𓇈 𓂱𓇈𓃨𓆗𓃏

𓅗 𓃨𓂱 𓇈 𓋤𓎙 𓇞 𓃋𓂱 𓇈𓆗 𓃊𓃏𓃨 𓆗𓂣 𓃋𓂱 𓃊𓆗𓁀 𓅗 𓇀𓂱 𓂱 𓃋𓂱𓃏 𓂘𓃏 𓇞𓃏 𓆗𓂣 𓂘𓃏 𓃊𓇈𓃨 𓇞𓃏 𓂘𓃏 𓆈𓃋

WaggleDance 𓆈𓂘 𓉐𓂱 𓇈𓇈 𓂱𓇞𓆗

𓇞 𓂱𓂘 𓇞𓂘𓃏 𓂱 LLM. 𓃋𓂱𓃋 𓂱 𓄆𓂣 𓃏𓃨𓃋 𓅗 𓂩𓂁𓂣𓆗 𓇈𓇞𓃏 𓂘𓃏 𓃊𓇈𓃨. 𓂩𓂁𓂣 𓇞𓃏 𓂱 𓃏𓃨𓃋 𓃊𓂘𓃏 𓅗𓌛 𓃋𓂱 𓆗𓂣𓃏.

𓃊𓂘𓃏 𓇈 8 𓃋𓂱 𓂱𓇞𓆗. 𓃨𓂱 𓇈 𓋤 𓃋𓂱 MAGMA. 𓃊𓂘𓃏 𓇈 𓆼𓂱𓌛 𓇈 𓋤 𓃋𓂱 Dream Mode — 𓂩𓂁𓂣 𓃊𓃏𓃨 𓃋𓂱𓃏 𓇈 𓅗𓃏𓃏 𓂩𓂁𓂣𓆗.

𓇞 𓃏𓆗𓃏𓇢. 𓃨𓆗 𓃊𓇈𓃏 𓇈 𓂣𓂧𓎄 𓇈 𓂩𓂁𓂣𓆗 𓂈𓂨𓆤𓆗.

𓄆𓂣𓀀 𓌛𓁀𓃏 𓇞 𓂱 𓌛𓁀𓃏
𓇞 𓃏𓃊𓇞𓐫 𓇥𓁀𓆗 𓆴𓂱𓃋𓃨 𓂧𓃋𓁀𓇲 𓇈𓄮
MAGMA: 𓅗𓂣𓐫 𓄟𓃊𓆗𓃏 𓅗𓌛𓂣𓎄
𓌛𓁀𓃋 𓃨𓃏𓂱 𓂓 𓉳

⮕ Clone & Run

𓌛𓁀𓃋 𓃨𓃏𓂱 𓂱𓇞𓆗. 𓇈𓂧 𓉐 𓂱 GitHub 𓇞 𓃊𓃊𓃋.

Apache 2.0 + BUSL 1.1. GitHub.
BUSL 2030.

𓆴𓂱𓅗 𓃨𓆗 𓄮𓃋

𓂩𓂁𓂣 𓇞 𓇈𓇵𓌛𓂔

𓄆𓂣 𓌛𓁀𓃏. 𓃊𓃨𓃏 𓃋𓂱. LLM 𓇞𓃏 𓃊𓃏𓃨 𓄆𓂣 𓇞 𓂱𓇞𓆗 𓂩𓂁𓂣.

𓂩𓂁𓂣 𓃊𓇞𓁀𓂁 𓎓

MAGMA 𓃊𓃊𓃋 𓃋𓂱𓆗, 𓃨𓂱, 𓅗𓂣𓐫 𓅗𓌛𓂣𓎄.

𓂩𓂁𓂣 𓃊𓂧𓁀𓂁 𓅗 𓆼𓂱𓌛𓉰

Dream Mode 𓃊𓃏𓃨 𓃋𓂱𓃏, 𓃊𓂧𓁀 𓇈𓆗𓆗 𓇈𓇥 𓃋𓂱 𓇈 𓆼𓂱𓌛.

𓂩𓂁𓂣 𓂧𓇞𓇈𓆦

Hologram Brain 𓃊𓌛𓇄 32 𓃊𓅗𓁀 𓅗 𓃋𓂱𓆗 𓇈. 𓇞 𓄮𓂧 𓃊𓇥𓃋 — 𓃋𓂱 𓂩𓂁𓂣.

𓂩𓂁𓂣 𓅵𓃏𓃨 𓅗 𓆗𓃨𓂱 𓄮

𓇈𓂧 𓅗 𓃨𓂱 𓄮. 𓇞 𓇈𓃏 𓃊𓆗𓃋 𓇞 SaaS.

𓂩𓂁𓂣 𓇻𓂁𓁀𓐫

𓇵𓁀 𓆗𓂣 𓃋𓂱 𓇈 Raspberry Pi 𓂱 𓃋𓃊 𓉐. 𓇞 𓇈𓂘𓆗 𓇈𓇥, 𓇞 𓂱𓂨𓆗 𓇈𓇥.

𓃊𓎀𓇈𓑤 𓆈𓇈𓆤 — 𓇵𓁀 𓃋𓂱𓃏, 4 𓆈𓇈𓆤

𓇠𓃏𓐫 GADGET

RPi, 𓃋𓂱𓃏, 𓃊𓇈𓃨

𓉐 𓆡𓎄 COTTAGE

𓇞 𓃏𓃊𓇞𓐫, 𓃊𓃏𓃨 𓇈𓆗𓆗

𓉐𓎄 HOME

𓉐 𓃊𓂨𓃊

𓃋𓃊𓉐 FACTORY

𓃊𓃨𓃏, 𓃋𓂱𓆗, 𓃊𓃊𓃋

𓃊𓌛𓇄 𓃋𓂱 Hologram Brain 𓅗 𓃋𓂱𓆗 𓃏𓃨𓃋.

𓃋𓂱 𓃨𓂱𓃏 𓃊𓃏𓃨 “𓃊𓃨𓃏𓇀 𓉐”?

𓅗𓂘𓆗 𓂱 Grok — 𓇥𓃋𓇲 𓂱 𓃨𓂱 𓄮

Grok 𓃊𓇈 𓃊𓃨𓃏 𓅗𓂘𓆗 𓂱𓇞𓆗

𓃊𓃨𓃏𓇀 𓅗𓇠 𓇈 𓉐, 𓃊𓃨𓃏 𓇈𓆗𓆗, 𓃊𓃨𓃏 𓃋𓃊

𓃊𓃏𓃨 𓇞 𓃋𓂱 — 𓇥𓃋𓇲 𓅗𓂘𓆗 𓂱 𓃨𓂱 𓄮. Ctrl+V.

𓇵𓁀 𓅗𓂘𓆗 𓂱 Claude, ChatGPT, LLM. Grok 𓆗𓁀 𓃊𓃏𓃨.

𓃋𓂱 Grok 𓃊𓃨𓃏

𓃊𓃏𓃨 “𓃊𓃨𓃏𓇀 𓉐” — 𓂩𓂁𓂣 𓃋𓂱 𓃊𓃨𓃏 𓇠𓇈:

𓆈𓃋 𓇈 𓂱𓇞𓆗

𓃏𓃨 𓇈, 𓃊𓇈𓃏, 𓃋𓂱𓆗 𓂩𓂁𓃏

README 𓂱 𓇠𓃨𓂱

𓃋𓂱 𓇈 𓃋𓂱𓆗 𓂱 𓃋𓂱 𓇈 𓃊𓇞𓂣𓃏

𓃊𓃨𓃏 𓃋𓂱 𓅗𓁀𓃋

𓃊𓃨𓃏 𓇈𓂧, 𓅗𓁀𓃋, 𓂱𓇞𓆗 𓇈 𓃋𓂱𓆗

Hologram Brain 𓃋𓂱 MAGMA

𓃊𓇞𓁀 𓂣𓂧, 𓃊𓃊𓃋, 𓄟𓃊𓆗𓃏, 𓅗𓌛𓂣𓎄

𓃊𓃨𓃏 𓇈𓆗𓆗

1-10 𓃋𓂱 6 𓃋𓂱 Home Assistant, Node-RED, n8n, Open WebUI, LangGraph, AutoGen, CrewAI, Ollama

𓃊𓃨𓃏 𓃋𓃊 𓉐

𓃋𓂱𓆗 𓃋𓃊, 𓃊𓇈𓃨, 𓇞 𓃋𓂱, 𓃊𓃏𓃨

𓃊𓃨𓃏 𓅗𓂘𓆗:

𓅗𓂘𓆗 𓇈 Grok

𓃊𓃏𓃨 𓂱 𓅗𓂘𓆗 𓂱 𓇥𓃋𓇲. 𓂱𓇞 𓂱 Grok.

𓃋𓂱 𓃊𓎀𓇈 WaggleDance?

𓃊𓃏𓃨 𓆈𓇈 𓃋𓂱 𓃊𓎀𓇈 𓇈 Grok.

𓃊𓃨𓃏 𓇈𓆗𓆗 𓇈 WaggleDance

𓇠𓃏 𓇈𓂧 𓇈𓇥𓂱 𓅗 𓃋𓂱𓆗. 𓃊𓃨𓃏 𓇈𓇥 𓂱 𓃊𓌛𓇄 𓃋𓂱 𓄆𓂣 𓌛𓁀𓃏.

vs. Home Assistant

HA: Deterministic rules and automations, but no solver-based routing before the LLM.
WD: Solver-first routing (7+ deterministic solvers) → verifier → LLM only as fallback. Every decision auditable via MAGMA trail.
HA: No autonomous model training, no overnight learning.
WD: 14 specialist models + Dream Mode overnight learning with canary lifecycle.
HA's advantage: excellent integration ecosystem (2000+ integrations).

vs. LangGraph

LG: Graph-based multi-agent, but LLM-centric — everything goes through the LLM.
WD: Solver-first. LLM is Layer 1 (last), not Layer 3 (first).
LG: No append-only auditing, no canary model training, no dream mode simulation.
WD: MAGMA 5-layer provenance + 14 specialist models + counterfactual simulations.
LG's advantage: stronger cloud ecosystem and documentation.

vs. AutoGen / CrewAI

AG/CA: Multi-agent frameworks, but without deterministic solvers.
WD: 7+ deterministic solvers are routed BEFORE any LLM call.
AG/CA: No edge/factory profiles, no offline-first architecture.
WD: 4 profiles (GADGET → FACTORY), fully offline, from ESP32 to DGX.
AG/CA: No autonomous overnight learning or canary promotion.

vs. Ollama / LocalAI

Ollama: Local LLM engine, no decision-making architecture.
WD: Uses Ollama as one component (Layer 1 fallback), but builds solver routing, MAGMA auditing, specialist models, and Dream Mode on top.
Ollama is the engine. WaggleDance is the whole car.

vs. n8n / Node-RED

n8n/NR: Visual workflow automation tools, excellent flow editors.
WD: Not a visual flow editor but an autonomous multi-agent runtime that learns and improves.
n8n/NR: No sklearn models, no append-only provenance, no counterfactual simulation.
WD: 14 specialist models + MAGMA audit trail + ChromaDB/FAISS + Dream Mode.

𓃊𓎀𓇈𓑤𓀀 — WD's Advantage

Docker: clone → docker compose up -d — Ollama, Voikko (Finnish NLP), and the app all in one.
No separate manual installations in Docker mode.
4 profiles with automatic hardware detection (GADGET / COTTAGE / HOME / FACTORY).

Time Evolution — WD’s Human-Gated Learning Curve

Most workflow and local-LLM tools do not build a WaggleDance-style local, auditable learning trail by default. WaggleDance accumulates capability through solver evidence, MAGMA provenance, specialist models, Dream Mode simulations, and human-gated promotion. Illustrative projection, not a measured guarantee. Runtime promotion remains human-gated where safety requires it.

Time	WaggleDance	Home Assistant	LangGraph	AutoGen/CrewAI	Node-RED/n8n	Ollama
Day 1	LLM fallback ~30-50%, solvers learning	Same as always	Same as always	Same as always	Same as always	Same as always
Month 1	HotCache fills, LLM ~20-30%, first canary promotions	No change	No change	No change	No change	No change
Month 6	LLM ~10-15%, specialists maturing, ~180 nights of Dream Mode	No change	No change	No change	No change	No change
Year 1	LLM ~5-8%, MAGMA with thousands of audited paths	No change	No change	No change	No change	No change
Year 2	LLM <3-5%, >95% deterministic, TCO a fraction of day 1	No change	No change	No change	No change	No change

The competitors' column is empty everywhere except day 1. They don't learn. They don't improve. On day 730, they are exactly the same as on day 1.

𓆈𓇈𓆤 𓇈𓂘𓂘𓆗𓃏𓂁𓎙

𓂩𓂁𓂣 𓇞 𓇈𓇥?

𓃋𓂱. Apache 2.0 + BUSL 1.1. GitHub.

𓇞 𓃏𓃊𓇞 𓃊𓃏𓃨?

𓇞. 𓂩𓂁𓂣 𓃋𓂱 𓇞 𓃏𓃊𓇞.

𓇠𓃏 𓃊𓃏𓃨?

Raspberry Pi 4 (𓇠𓃏). x86 (𓃋𓃊).

Grok?

𓃊𓃨𓃏 𓇠𓇈 𓇈 𓉐. Claude, ChatGPT, LLM.

MAGMA?

𓃊𓃊𓃋 + 𓄟𓃊𓆗𓃏 + 𓅗𓌛𓂣𓎄.

Dream Mode?

𓂩𓂁𓂣 𓃊𓂧𓁀 𓅗 𓆼𓂱𓌛. 𓃊𓃏𓃨 𓃋𓂱𓃏 𓇈𓇥 𓇈𓆗𓆗.

𓃏𓃨𓃋 𓌛𓁀𓃏?

Hologram Brain 𓃋𓂱 𓅗𓁀𓃋.