ruvector/crates/ruvector-learning-wasm/pkg/package.json
rUv 890ff45075 feat(wasm): add 5 exotic AI WASM packages with npm publishing
WASM Packages (published to npm as @ruvector/*):
- learning-wasm (39KB): MicroLoRA rank-2 adaptation with <100us latency
- economy-wasm (182KB): CRDT-based autonomous credit economy
- exotic-wasm (150KB): NAO governance, Time Crystals, Morphogenetic Networks
- nervous-system-wasm (178KB): HDC, BTSP, WTA, Global Workspace
- attention-unified-wasm (339KB): 18+ attention mechanisms (Neural, DAG, Graph, Mamba)

Changes:
- Add ruvector-attention-unified-wasm crate with unified attention API
- Add ruvector-economy-wasm crate with CRDT ledger and reputation
- Add ruvector-exotic-wasm crate with emergent AI mechanisms
- Add ruvector-learning-wasm crate with MicroLoRA adaptation
- Add ruvector-nervous-system-wasm crate with bio-inspired components
- Fix ruvector-dag for WASM compatibility (feature flags)
- Add exotic AI capabilities to edge-net example
- Update README with WASM documentation
- Include pkg/ directories with built WASM bundles

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-01 06:31:11 +00:00

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{
"name": "@ruvector/learning-wasm",
"type": "module",
"collaborators": [
"rUv <ruvnet@users.noreply.github.com>"
],
"author": "RuVector Team <ruvnet@users.noreply.github.com>",
"description": "Ultra-fast MicroLoRA adaptation for WASM - rank-2 LoRA with <100us latency for per-operator learning",
"version": "0.1.29",
"license": "MIT OR Apache-2.0",
"repository": {
"type": "git",
"url": "https://github.com/ruvnet/ruvector"
},
"bugs": {
"url": "https://github.com/ruvnet/ruvector/issues"
},
"files": [
"ruvector_learning_wasm_bg.wasm",
"ruvector_learning_wasm.js",
"ruvector_learning_wasm.d.ts",
"ruvector_learning_wasm_bg.wasm.d.ts",
"README.md"
],
"main": "ruvector_learning_wasm.js",
"homepage": "https://ruv.io",
"types": "ruvector_learning_wasm.d.ts",
"sideEffects": [
"./snippets/*"
],
"keywords": [
"lora",
"machine-learning",
"wasm",
"neural-network",
"adaptation",
"ruvector",
"webassembly",
"ai",
"deep-learning",
"micro-lora"
]
}