- From: Daniel Ramos <capitain_jack@yahoo.com>
- Date: Fri, 27 Feb 2026 03:14:06 -0300
- To: public-pm-kr@w3.org
- Message-ID: <5951940d-f56d-4438-acde-c28e6bd9d3c2@yahoo.com>
Hi Christoph,
Great to hear you got it running! Moving technical discussion to GitHub.
Quick answers:
1. **Memory Tablet** = 3D UI object (apps on a surface in the scene)
2. **Datasets** = `viewer/public/*.gltf` (skill_galaxy, sleep_galaxy, etc.)
3. **Chat** = needs WebSocket server (optional, for live agent)
4-6. **Viewer evolution** = see GitHub discussion for details
**Key paradigm for PM-KR:** K3D uses "software as a space" with **AI
hosts** instead of traditional interfaces — you don't click menus, you
collaborate with AI companions in spatial semantic environments.
**Timing note:** The current viewer shows the core principles (3D
semantic space, glTF + metadata, procedural Galaxy references). I'm
currently focused on knowledge ingestion (populating the Galaxy with
foundational data from 1,952 PDFs + ollama - qwen -
augmentation/conversion). Your interest in 2D rendering and visual
layout is perfect timing — that's exactly the next evolution layer we need!
Looking forward to collaborating on the visualization/UX layer while the
knowledge base matures.
https://github.com/danielcamposramos/Knowledge3D/discussions/54
Daniel
On 2/26/26 7:31 PM, Christoph wrote:
> You have my attention!
>
> You based your answer on old explorations of mine. I have since
> significantly advanced my work with implementations that allow for
> rapid development of graph-structured modular applications where
> components can run in sandboxes.
>
> I started publishing my work in the form of an Open Development
> Project on Feb 13, 2026 and am in the process of polishing it based on
> initial feedback. I want to continue this process for a week or two
> until the presentation is proper for an initial audience like this group.
>
> Let me prepare some things to get ready given the direction you have
> set and I will reply with updated links to working code. We can rebase
> on that and explore how to best implement a K3D visualization in my
> architecture. From there we can precipitate general K3D libraries like
> you mentioned for use in other applications.
>
> My goal in my work is to provide simple enough implementations that a
> developer can follow to understand the concepts at play. Simple enough
> does not mean feature poor. At most it means entity count limited.
>
> My goal is to remove barriers to playing with advanced technologies by
> making them accessible in Javascript. One can then learn and hack
> and evolve to using large-scale implementations as needed. My vision
> is a pluggable ecosystem to go from virtual model to large-scale
> manifest system.
>
> I am very much looking forward to seeking what we can do! The timing
> is perfect.
>
> Christoph
>
>
>
> On Thu, Feb 26, 2026, at 4:49 PM, Daniel Ramos wrote:
>> Hi Christoph,
>>
>> Thank you for the kind words, and I'm thrilled to hear you've been
>> working from this perspective!
>>
>> After looking at your work (jsonrep, ccsjon, @cadorn on GitHub), I
>> think we're solving the same problem from complementary angles — and
>> there's a clear integration path.
>>
>> ## The "Map" You're Looking For = K3D's Galaxy Universe
>>
>> **Your insight:**
>> > "A Visual Canvas that can hold all entities in relation is the only
>> path I see to create such a map."
>>
>> **This is exactly what K3D's Galaxy Universe is** — a spatial
>> semantic workspace where:
>> - **Entities** = glTF nodes with JSON-LD metadata
>> - **Relationships** = spatial proximity + semantic links
>> - **Visualization** = 3D objects you navigate (spatial proximity =
>> semantic similarity)
>> - **One coherent model** = Knowledgeverse (7 regions, unified substrate)
>>
>> **Your projects already align:**
>> - **jsonrep** (JSON markup for visualization) ≈ K3D's glTF extensions
>> (JSON-LD + 3D visual form)
>> - **ccsjon** (entities, inheritance, relationships in JSON) ≈ K3D's
>> semantic graphs (RDF triples in glTF metadata)
>>
>> You're already thinking in the right direction — K3D formalizes and
>> extends these patterns.
>>
>> ## K3D-Compatible JS Components: The Backbone
>>
>> **You asked:** "What is the backbone of this architecture that I can
>> implement in JS now?"
>>
>> **Answer:** Three layers (progressive enhancement):
>>
>> ### 1. Semantic Graph Layer (JSON-LD)
>>
>> **What it is:** Entities and relationships as RDF triples
>>
>> **Format:**
>> ```json
>> {
>> "@context": "https://pm-kr.org/contexts/procedural.jsonld"
>> <https://pm-kr.org/contexts/procedural.jsonld>,
>> "@type": "MathSymbol",
>> "@id": "galaxy:math:sqrt",
>> "name": "Square Root",
>> "definition": {
>> "@type": "ProceduralProgram",
>> "program": ["DUP", "0", "GT", "ASSERT", "SQRT"]
>> },
>> "relatedTo": ["@galaxy:math:pow", "@galaxy:math:exp"]
>> }
>> ```
>>
>> **Your ccsjon already does this!** Just add `@context` for JSON-LD
>> compatibility.
>>
>> ### 2. Visual Representation Layer (glTF + K3D Extensions)
>>
>> **What it is:** JSON-LD entities embedded in glTF 3D nodes
>>
>> **Format:**
>> ```json
>> {
>> "nodes": [
>> {
>> "name": "sqrt_symbol",
>> "translation": [x, y, z],
>> "extras": {
>> "k3d_type": "semantic_node",
>> "k3d_semantic": {
>> "@id": "galaxy:math:sqrt",
>> "@type": "MathSymbol",
>> "name": "Square Root"
>> },
>> "k3d_links": [
>> {"target": "galaxy:math:pow", "relation": "inverse_of"}
>> ]
>> }
>> }
>> ]
>> }
>> ```
>>
>> **This is JSON markup for visualization** (your jsonrep concept) —
>> glTF provides the 3D visual form, `extras.k3d_semantic` provides the
>> meaning.
>>
>> **Spec:**
>> [docs/vocabulary/K3D_NODE_SPECIFICATION.md](https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/vocabulary/K3D_NODE_SPECIFICATION.md
>> <https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/vocabulary/K3D_NODE_SPECIFICATION.md>)
>>
>> ### 3. Rendering Layer (2D Canvas → 3D WebGL)
>>
>> **Progressive path:**
>>
>> **Phase 1: 2D Canvas (Start Here)**
>> ```typescript
>> // Render glTF nodes on 2D canvas (ignore Z-axis initially)
>> interface K3DNode {
>> position: [number, number, number]; // [x, y, z]
>> semantic: any; // JSON-LD metadata
>> visual: {
>> type: "circle" | "rect" | "text";
>> style: { color: string; size: number };
>> };
>> }
>>
>> function render2D(nodes: K3DNode[], canvas: HTMLCanvasElement) {
>> const ctx = canvas.getContext('2d');
>> nodes.forEach(node => {
>> const [x, y] = node.position; // Ignore Z for 2D
>> drawNode(ctx, x, y, node.visual, node.semantic);
>> });
>> }
>> ```
>>
>> **Phase 2: 3D WebGL (Scale Later)**
>> ```typescript
>> // Use Three.js or Babylon.js to render full glTF with spatial navigation
>> import { GLTFLoader } from 'three/examples/jsm/loaders/GLTFLoader';
>>
>> const loader = new GLTFLoader();
>> loader.load('galaxy.gltf', (gltf) => {
>> // K3D metadata is in node.userData.k3d_semantic
>> gltf.scene.traverse((node) => {
>> if (node.userData?.k3d_semantic) {
>> console.log('Semantic node:', node.userData.k3d_semantic);
>> }
>> });
>> scene.add(gltf.scene);
>> });
>> ```
>>
>> ## Merging Graphs of Different Concerns
>>
>> **You asked:** "How do I merge graphs of different concerns to arrive
>> at a complex interactive visualization?"
>>
>> **K3D approach:** Galaxy composition via symlink-style references
>>
>> ### Example: Math Galaxy + Reality Galaxy Merge
>>
>> **Math Galaxy:**
>> ```json
>> {
>> "@id": "galaxy:math:force_equation",
>> "@type": "MathFormula",
>> "formula": "F = ma",
>> "linkedTo": ["@galaxy:reality:newtonian_mechanics"]
>> }
>> ```
>>
>> **Reality Galaxy:**
>> ```json
>> {
>> "@id": "galaxy:reality:newtonian_mechanics",
>> "@type": "PhysicsSystem",
>> "laws": ["@galaxy:math:force_equation"],
>> "simulations": ["@program:falling_ball"]
>> }
>> ```
>>
>> **Merged visualization:**
>> - Math symbol (F=ma) appears **spatially near** physics simulation
>> (falling ball)
>> - Click math symbol → highlights related physics simulation
>> - Click physics simulation → shows underlying math formula
>>
>> **Implementation:**
>> ```typescript
>> interface SemanticLink {
>> source: string; // @id reference
>> target: string; // @id reference
>> relation: string; // "linkedTo", "partOf", "derivedFrom", etc.
>> }
>>
>> function mergeGalaxies(graphs: Graph[]): MergedGraph {
>> const allNodes = graphs.flatMap(g => g.nodes);
>> const allLinks = graphs.flatMap(g => g.links);
>>
>> // Resolve @id references across galaxies
>> const resolved = resolveReferences(allNodes, allLinks);
>>
>> // Compute spatial layout (semantic proximity = spatial proximity)
>> const layout = computeSpatialLayout(resolved);
>>
>> return { nodes: layout, links: allLinks };
>> }
>> ```
>>
>> **Key insight:** References (symlinks) instead of duplication — same
>> pattern as your ccsjon inheritance!
>>
>> ## Concrete Starting Point: K3D Viewer (TypeScript)
>>
>> **Good news:** <news:**> K3D's Viewer is already TypeScript + glTF!
>>
>> **Repository structure:**
>> ```
>> Knowledge3D/
>> ├── docs/vocabulary/
>> │ ├── K3D_NODE_SPECIFICATION.md ← glTF extensions (start here)
>> │ ├── SPATIAL_UI_ARCHITECTURE_SPECIFICATION.md ← 3D navigation
>> │ └── DUAL_CLIENT_CONTRACT_SPECIFICATION.md ← human + AI rendering
>> │
>> ├── viewer/ (TypeScript, planned)
>> │ ├── src/
>> │ │ ├── loaders/
>> │ │ │ └── K3DGLTFLoader.ts ← glTF + K3D metadata parser
>> │ │ ├── renderers/
>> │ │ │ ├── Canvas2DRenderer.ts ← 2D canvas (start here)
>> │ │ │ └── WebGLRenderer.ts ← 3D WebGL (later)
>> │ │ └── graph/
>> │ │ ├── SemanticGraph.ts ← JSON-LD entity management
>> │ │ └── GraphMerger.ts ← multi-galaxy composition
>> │ └── examples/
>> │ └── simple-2d-canvas.html ← minimal example
>> ```
>>
>> **You could contribute directly to this!**
>>
>> ## Progressive Implementation Plan (4 Phases)
>>
>> ### Phase 1: JSON-LD Semantic Graphs (You Already Do This!)
>>
>> **Goal:** Represent entities and relationships in JSON-LD
>>
>> **Your ccsjon + @context = K3D-compatible semantic graphs**
>>
>> **Example:**
>> ```typescript
>> // Your ccsjon output + K3D context
>> {
>> "@context": "https://pm-kr.org/contexts/procedural.jsonld"
>> <https://pm-kr.org/contexts/procedural.jsonld>,
>> "@graph": [
>> {
>> "@id": "entity:player",
>> "@type": "GameCharacter",
>> "inheritsFrom": "entity:base_character",
>> "hasInventory": "entity:inventory_1"
>> }
>> ]
>> }
>> ```
>>
>> ### Phase 2: glTF Wrapping (JSON-LD → Visual Nodes)
>>
>> **Goal:** Embed semantic graphs in glTF nodes for visualization
>>
>> **Tool:** Simple TypeScript converter
>>
>> ```typescript
>> function jsonLDToGLTF(semanticGraph: any): GLTF {
>> const nodes = semanticGraph['@graph'].map((entity, i) => ({
>> name: entity['@id'],
>> translation: computePosition(entity, i), // Layout algorithm
>> extras: {
>> k3d_type: 'semantic_node',
>> k3d_semantic: entity
>> }
>> }));
>>
>> return {
>> asset: { version: '2.0' },
>> scene: 0,
>> scenes: [{ nodes: [0, 1, 2, ...] }],
>> nodes: nodes
>> };
>> }
>> ```
>>
>> ### Phase 3: 2D Canvas Renderer (Minimal Interactive Visualization)
>>
>> **Goal:** Render glTF nodes on 2D canvas with click/hover interactions
>>
>> **Example:**
>> ```typescript
>> class K3DCanvas2DRenderer {
>> private ctx: CanvasRenderingContext2D;
>> private nodes: K3DNode[] = [];
>>
>> loadGLTF(gltf: GLTF) {
>> this.nodes = gltf.nodes.map(node => ({
>> x: node.translation[0],
>> y: node.translation[1],
>> semantic: node.extras.k3d_semantic,
>> visual: this.getVisualStyle(node.extras.k3d_semantic['@type'])
>> }));
>> }
>>
>> render() {
>> this.ctx.clearRect(0, 0, width, height);
>> this.nodes.forEach(node => {
>> this.drawNode(node);
>> this.drawLinks(node);
>> });
>> }
>>
>> onClick(x: number, y: number) {
>> const clicked = this.nodes.find(n => this.hitTest(n, x, y));
>> if (clicked) {
>> console.log('Clicked semantic node:', clicked.semantic);
>> this.highlightRelated(clicked.semantic['@id']);
>> }
>> }
>> }
>> ```
>>
>> **This gives you interactive semantic graph visualization in ~200
>> lines of TypeScript!**
>>
>> ### Phase 4: 3D Spatial Navigation (Full K3D Experience)
>>
>> **Goal:** Upgrade to 3D WebGL with spatial navigation (walk through
>> galaxies)
>>
>> **Tools:** Three.js or Babylon.js + K3D glTF extensions
>>
>> **Defer this until Phases 1-3 are solid** — 2D proves the concept, 3D
>> scales it.
>>
>> ## Immediate Next Steps (If You Want to Collaborate)
>>
>> ### 1. Validate K3D glTF Format with Your Data
>>
>> **Take one of your ccsjon graphs** and convert to K3D glTF format:
>>
>> ```bash
>> # Your ccsjon output
>> your-tool generate --output entities.json
>>
>> # Convert to K3D glTF (simple script)
>> node scripts/jsonld-to-gltf.js entities.json > entities.gltf
>>
>> # Validate
>> gltf-validator entities.gltf
>> ```
>>
>> **I can provide the `jsonld-to-gltf.js` converter** if you want to
>> try this!
>>
>> ### 2. Build Minimal 2D Canvas Renderer
>>
>> **Goal:** Render K3D glTF on 2D canvas (100-200 lines TypeScript)
>>
>> **Features:**
>> - Load glTF with K3D extensions
>> - Draw nodes as circles/rects (ignore 3D for now)
>> - Show semantic metadata on hover
>> - Highlight connected nodes on click
>>
>> **I'd love to see your approach** — your jsonrep experience would be
>> valuable here!
>>
>> ### 3. Define "K3D-Compatible" Interface
>>
>> **Let's collaboratively draft:**
>>
>> ```typescript
>> // K3D-compatible component interface
>> interface K3DComponent {
>> // Load semantic graph
>> loadSemanticGraph(graph: JSONLD): void;
>>
>> // Render to 2D canvas
>> render2D(canvas: HTMLCanvasElement): void;
>>
>> // Optional: Render to 3D WebGL
>> render3D?(container: HTMLElement): void;
>>
>> // Query semantic nodes
>> query(selector: string): SemanticNode[];
>>
>> // Handle interactions
>> on(event: 'click' | 'hover', handler: (node: SemanticNode) =>
>> void): void;
>> }
>> ```
>>
>> **This could become the standard interface for K3D-compatible JS
>> libraries!**
>>
>> ## Resources to Get Started
>>
>> ### K3D Specifications
>> 1.
>> **https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/vocabulary/K3D_NODE_SPECIFICATION.md
>> <https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/vocabulary/K3D_NODE_SPECIFICATION.md>**
>> — glTF extensions for semantic graphs
>> 2.
>> **https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/vocabulary/DUAL_CLIENT_CONTRACT_SPECIFICATION.md
>> <https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/vocabulary/DUAL_CLIENT_CONTRACT_SPECIFICATION.md>**
>> — how humans + AI see same data differently
>> 3.
>> **https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/vocabulary/SPATIAL_UI_ARCHITECTURE_SPECIFICATION.md
>> <https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/vocabulary/SPATIAL_UI_ARCHITECTURE_SPECIFICATION.md>**
>> — 3D navigation patterns
>>
>> ### Example Data
>> - K3D Math Galaxy (sample glTF with semantic metadata)
>> - K3D Character Galaxy (procedural fonts as glTF nodes)
>>
>> ### Tools
>> - **glTF Validator:** https://github.khronos.org/glTF-Validator/
>> <https://github.khronos.org/glTF-Validator/>
>> - **JSON-LD Playground:** https://json-ld.org/playground/
>> <https://json-ld.org/playground/>
>> - **Three.js GLTFLoader:**
>> https://threejs.org/docs/#examples/en/loaders/GLTFLoader
>> <https://threejs.org/docs/#examples/en/loaders/GLTFLoader>
>>
>> ## Closing Thoughts
>>
>> **Your work (jsonrep, ccsjon) validates K3D's approach** — you
>> independently arrived at the same insight:
>> > "Systems need visual representations with embedded semantics"
>>
>> **The gap to close:**
>> - Your tool generates JSON entities/relationships (ccsjon)
>> - K3D wraps them in glTF for 3D visualization (K3D extensions)
>> - TypeScript renderer makes it interactive (your jsonrep concepts)
>>
>> **Together, we can define the standard "K3D-compatible component"
>> interface** that others can implement in JavaScript, Python, Rust, etc.
>>
>> **Let's start simple:**
>> 1. Convert one of your graphs to K3D glTF
>> 2. Build minimal 2D canvas renderer
>> 3. Iterate on the interface
>>
>> **I'm happy to pair on this** — either async (GitHub issues) or sync
>> (video call).
>>
>> What do you think? Should we start with (1) converting your existing
>> data to K3D glTF format?
>>
>> **Daniel Ramos**
>> Co-Chair, W3C PM-KR Community Group
>> AI Knowledge Architect
>>
>> ## P.S. Your "Coherent Model" Question
>>
>> You mentioned wanting to "fit everything into one model."
>>
>> **K3D's answer:** Knowledgeverse (7 regions, one unified VRAM substrate)
>>
>> ```
>> Knowledgeverse = Unified Memory Substrate
>> ├── Region 1: Kernels (RPN VM, procedural operations)
>> ├── Region 2: Galaxy Universe (semantic graphs, visual workspace)
>> ├── Region 3: House Context (private user data)
>> ├── Region 4: World View (network collaboration)
>> ├── Region 5: TRM Weights (AI reasoning logic)
>> ├── Region 6: Audit Journal (provenance, versioning)
>> └── Region 7: Ingestion Stargate (raw data → procedural transformation)
>> ```
>>
>> **Everything fits into one model** because:
>> - Same format (JSON-LD + glTF)
>> - Same execution (RPN procedural programs)
>> - Same memory (unified VRAM workspace)
>> - Same rendering (dual-client: human visual + AI semantic)
>>
>> **Your visualization engine could become Region 4 (World View)** —
>> the interactive visualization layer for K3D!
>>
>> More in:
>> https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/vocabulary/KNOWLEDGEVERSE_SPECIFICATION.md
>> <https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/vocabulary/KNOWLEDGEVERSE_SPECIFICATION.md>
>
Received on Friday, 27 February 2026 06:14:18 UTC