ĝeštug-niĝ₂ gal-gal-e niĝ₂-gig dili mu-un-ak-eš — gu₃ dili nu-zu

LLM eme-diĝir-ra igi-bi igi-še₃ mu-un-il₂-eš; inim-bi gi-na-am₃ ḫe₂-en-na-ab-be₂-eš.

Watch 30 second demo

Hologram Brain — ki 32-bi u₄-da-am₃ igi-bar

ukkin-e inim mu-un-igi-bar — kaskal nam-til₃-la-bi mu-un-pa₃.

an-ki-e u₄ ul-la-ta niĝ₂-gig-bi mu-un-zu. e₂ nim-lal₃-ka, nim-lal₃ igi-bar-ra inim mu-un-tum₂. ki-e-ne-di-bi mu-un-ak: gu₂-bi kaskal, u₄-sud-bi kaskal-sud, du₁₀-ga-bi niĝ₂-kal. e-ne-di-bi inim dili nu-um-ma-ab-du₁₁. nin₉-ne-ne igi-bar-re-eš; šu tag-ge-eš; gu₃ mu-na-an-gi₄-gi₄. gu₃-kud-da e-ne-di-bi ba-an-kud. inim ukkin-na-ke₄ gi-na ba-an-tuku-a, kaskal-bi ba-an-gar.

WaggleDance-bi kaskal-bi-ta ba-du₃.

inim-bi LLM-ra nu-mu-un-šum₂. lu₂-šid gi-na-ra igi-še₃ ba-an-du. eĝer-ba lu₂-igi-bar-re-eš inim-bi mu-un-igi-bar-re-eš. LLM ud-da-bi-ta gu₃ mu-un-de₂: lu₂-šid gi-na nam-ba-da-an-til₃-la-a. dub MAGMA-ka ka-aš-bar-bi, ki-bi, ki-še₃-gi₄-bi, gi-na-bi sar-ra-am₃.

ki-e-ne-di-a kaskal lu₂-šid-da ba-an-tum₂. e₂ nim-lal₃-ka ša₃-bi MAGMA ĝeštug-dub-ba ba-an-kur₂. ge₆ nim-lal₃-ka Dream Mode-a ba-an-kur₂: u₄ kiĝ₂ hul-bi igi-bar, kaskal lim-gal-gal mu₂-mu₂, ud-zal-la ĝeštug gibil-da ed₂.

ne-en-bi ni₂-bi-da-am₃ — ne-en-bi ĝiš-ḫur ukkin ĝeštug-niĝ₂-ka.

lu₂-šid igi-še₃ — chat eĝer-še₃
e₂-ša₃-ka — bar-še₃ nu-ed₂
MAGMA — dub, ki-bi, ki-še₃-gi₄, gi-na
clone, run, ĝal₂-tak₄

⮕ Clone & Run

clone ak, fork ak, ki-zu-a run ak. dub-sar dul₃-bi GitHub-ka ĝal₂. mu-zu nu-tum₃-mu.

Apache 2.0 + BUSL 1.1 (open core + source-available protected modules). GitHub.
BUSL bal-bal-bi: 2030-03-19.

a-na-še₃ ba-da-kur₂

ĝeštug-niĝ₂ nam-maš₂-šu-gid₂-gid₂ nu-ak

lu₂-šid-meš igi-še₃ kiĝ₂ ak-eš. lu₂-igi-bar-re igi-bar. LLM ud-da-bi-ta ku₄: lu₂-šid gi-na nu-til₃-la-a.

ĝeštug-niĝ₂ dub dul₃-bi mu-un-tuku

MAGMA-e ka-aš-bar-meš, ki-bi-ta-bi, ki-še₃-gi₄-bi, gi-na-bi dub-ba mu-un-sar. a-na ba-du₃-a, a-na-še₃, kaskal-bi-da igi-bar.

ĝeštug-niĝ₂ ge₆-da zu-zu

Dream Mode-e u₄-bi-ta hul-bi mu-un-igi-bar; kaskal gibil-meš mu-un-mu₂-mu₂; ud-zal-la-bi du₁₀-bi-da mu-ni-ib-il₂.

ĝeštug-niĝ₂ ki-tuš-bi mu-un-pa₃

Hologram Brain-e ki 32-bi u₄-da-am₃ mu-un-pa₃ — ki-tum₂-ma-zu nu-tuku: kiĝ₂ ak-a-bi igi-zu-še₃ gub.

ĝeštug-niĝ₂ e₂-ša₃-ka al-gub

niĝ₂-na-me e₂-ša₃-ka al-ĝal₂. cloud nam-ba, prompt-data bar-še₃ nu-ed₂, SaaS nam-ba.

ĝeštug-niĝ₂ gal-bi mu₂-mu₂

Raspberry Pi-ta e₂-kiĝ₂ dul₃-bi-še₃. demo dili nu, framework dili nu: runtime-am₃.

ki-us₂-sa — runtime dili, e₂ limmu-bi

niĝ₂-kiĝ₂ tur GADGET

RPi, edge, sensor

e₂-tur COTTAGE

e₂-ša₃-ka, kaskal šub-šub

e₂ HOME

e₂-a automation ki-a

e₂-kiĝ₂ FACTORY

igi-bar, niĝ₂-gig-pa₃, audit dub

Dashboard u₃ Hologram Brain šu-du₈-a eĝer-bi-ta ĝal₂. profile, hardware, full/stub mode-bi-ta igi-bar ša₃-gub-ba-bi ba-da-kur₂.

“Repository igi-bar” a-na-am₃?

inim-gar Grok-ra ba-an-tum₂; šu-zu-še₃ ba-an-gar.

Grok-e ka mu-un-bad-de₃, igi-bar mu-un-til-le.

Repository igi-bar, competitor-da gaba-ri, factory ki-us₂ igi-bar.

niĝ₂ nu-du-da, inim-gar Grok-še₃ gar-ra (Ctrl+V).

Claude, ChatGPT, LLM gal dul₃-bi-da inim-gar-bi al-du. Grok-bi page-a-ka default-am₃.

Grok-e a-na mu-un-igi-bar

“Repository igi-bar” mu-un-tag-ga-da — ĝeštug-niĝ₂-e mu-un-igi-bar zi-da:

codebase u₄-da-bi

main branch, architecture, modules, latest commits

README gaba-ri reality

a-na ba-du₃, a-na ĝiš-ḫur-a

tests u₃ maturity

test coverage, production readiness

Hologram Brain u₃ MAGMA

memory model, audit, provenance, trust

competitor gaba-ri

1–10 scoring: Home Assistant, Node-RED, n8n, Open WebUI, LangGraph, AutoGen, CrewAI, Ollama

factory ki-us₂

industrial use cases, gaps, risks, offline blockers

inim-gar igi-bar-bi:

Grok-ra inim-gar-meš

inim-gar šu-tag-ga-da, šu-zu-še₃ ba-an-gar. Grok-še₃ gar-ra.

WaggleDance ki-us₂-bi a-na-gin₇?

ki-tuš-zu pa₃-da, Grok-e kaskal ki-us₂-sa-zu mu-un-mu₂.

WaggleDance gaba-ri

ĝeštug-niĝ₂ dul₃-bi a₂-bi-da-bi al-tuku. ki-ta-am₃ ba-da-kur₂-bi mu-šid mu-šid. ka-aš-bar ukkin-na-ke₄ — diĝir dili nu-mu-un-du₁₁.

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.

ki-us₂ — 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.

inim-meš ḫe₂-tag

WaggleDance ḫe₂-šub-bu? ḫe₂-ĝal₂-tak₄?

ge-en-bi-da. Apache 2.0 + BUSL 1.1. dub-sar dul₃-bi GitHub-ka ĝal₂.

e₂-ša₃-ka al-ĝal₂?

ge-en-bi-da. ĝeštug-niĝ₂ ĝiš-tukul-zu-bi-da-bi al-ĝal₂. cloud nam-ba.

a-na ĝiš-tukul niĝ₂-gig-am₃?

Raspberry Pi 4 (GADGET). x86 (FACTORY).

Grok a-na-am₃?

en bar-še₃ dub-mu igi-bar-ra. Claude, ChatGPT, LLM gal dul₃-bi al-ĝal₂-eš.

MAGMA a-na-am₃?

MAGMA — audit u₃ provenance ĝiš-ḫur. Every agent decision is written so replay, traceability, and trust can be inspected.

Dream Mode a-na-am₃?

ĝeštug-niĝ₂-e ge₆-da ni₂-bi-da igi mu-un-bar; kaskal gibil-meš mu-un-mu₂-mu₂; ud-zal-la-bi du₁₀-bi-da mu-un-il₂.

šu-du₈-a eĝer-bi-ta a-na ba-du₃?

Hologram Brain-e ki 32-bi u₄-da-am₃ mu-un-pa₃.