Introduction – Daniel Ramos (Knowledge3D spatial KR & sovereign training)

Hi all,

I’m Daniel Ramos, an Electrical Engineer and long‑time IT consultant now 
working full‑time on Knowledge3D (K3D), an open‑source spatial knowledge 
representation and AI architecture project.

My background is in Electrical Engineering (UNICSUL, CREA‑DF engineer, 
FE/EIT candidate in Canada) and 20 years of hands‑on systems work 
(infrastructure, CAD, power systems, disaster‑recovery for tax law 
firms). I live nowadays in a favela in Brasília and have been 
self‑funding my research through consulting while building EchoSystems 
AI Studios as a vehicle for what I call “symbiotic intelligence”: humans 
and AI as partners, not tools.

On the AI side, I’ve been developing:

  * Knowledge3D (K3D) – a glTF‑based 3D KR substrate where Houses and
    Rooms are semantic spaces, Galaxies are 3D RAM for embeddings, and
    each K3D node co‑locates geometry, embeddings, RDF/OWL‑style
    metadata, and executable RPN programs (math/logic/physics).

    Repo: https://github.com/danielcamposramos/Knowledge3D

  * A sovereign Three‑Brain architecture (Cranium/Galaxy/House) with a
    PTX‑native RPN math core, SleepTime consolidation, and Reality
    Enabler for physics/chemistry/biology.
    We recently reached 46.7% accuracy in a private ARC‑AGI 2 run with a
    7M‑parameter, fully procedural training loop (no large LLMs, no
    cloud dependencies).

  * MVCIC (Multi‑Vibe Code In Chain) – a human‑orchestrated, multi‑agent
    development workflow where all AI interactions (Codex/Claude/others)
    are logged as plain‑text chain files for replayable, auditable
    reasoning.


Reading through CogAI’s work, I see a strong resonance:

  * Your chunks & rules abstraction and digital‑twin demos map very
    naturally onto K3D’s domains of discourse (galaxies) and House
    instances; I’d like to explore how chunks/rules could live as
    executable K3D nodes.

  * The Plausible Knowledge Notation (PKN) is exactly the kind of
    plausibility/argumentation layer I want to run on top of the spatial
    KR substrate, especially for adequacy and confidence gating.

  * The Immersive Web vision (WebGPU/WebXR/WebNN, avatars, intent‑based
    behaviours) aligns very closely with K3D’s “software as space” model
    (Houses, Galaxy Universe, Tablet) and our Universal Accessibility
    spec (blind/deaf users navigating the same Houses via audio,
    Braille, sign).

  * Your work on small, understandable WebNN models mirrors my focus on
    small, transparent engines; I’d love to see how K3D could host model
    cards / NNM models as first‑class spatial artifacts and how CogAI’s
    Sentient AI ideas might integrate with our episodic Galaxy/House
    memory structure.

My interest in joining CogAI is to:

  * contribute K3D’s spatial KR, neurosymbolic integration, and
    sovereign training results as concrete testbeds for CogAI abstractions;

  * learn from, and potentially align with, your work on chunks & rules,
    PKN, Immersive Web, and small WebNN frameworks;

  * help move toward an open, inspectable, spatial AI stack that can
    eventually serve as a standard substrate for Web 4.0 / AI‑KR, with
    accessibility and inclusion built in.

Happy to share specific specs (e.g., K3D Node, Three‑Brain System, 
Reality Enabler, Universal Accessibility) or demos if that would be 
useful. I’m very glad to be here and looking forward to collaborating.

Best regards,
Daniel Campos Ramos
Knowledge3D / AI‑RLWHF / EchoSystems AI Studios
https://github.com/danielcamposramos/Knowledge3D