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docs(edge): highlight self-learning capabilities in intro

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- Update heading to "Free Self-Learning AI Swarms at the Edge"
- Emphasize self-optimizing agents that get smarter over time
- Mention LoRA fine-tuning, EWC++ continual learning, ReasoningBank
- Bump to v0.1.7

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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examples/edge/pkg/README.md
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[![WASM](https://img.shields.io/badge/wasm-364KB-purple.svg)]()
[![Tests](https://img.shields.io/badge/tests-60%20passing-brightgreen.svg)]()
## Free Edge-Based AI Swarms
## Free Self-Learning AI Swarms at the Edge
**Build and deploy AI agent swarms that run entirely in web browsers, mobile devices, and edge servers - without paying for cloud infrastructure.**
**Build and deploy self-optimizing AI agent swarms that run entirely in web browsers, mobile devices, and edge servers - without paying for cloud infrastructure.**
Imagine having dozens of AI agents working together - analyzing data, routing tasks, making decisions - all running directly in your users' browsers. No API costs. No server bills. No data leaving your network. That's what @ruvector/edge makes possible.
Imagine having dozens of AI agents working together - analyzing data, routing tasks, making decisions, and getting smarter with every interaction - all running directly in your users' browsers. No API costs. No server bills. No data leaving your network. That's what @ruvector/edge makes possible.
This library gives you everything you need to build distributed AI systems: cryptographic identity for each agent, encrypted communication between them, lightning-fast vector search for finding the right agent for each task, and consensus protocols so your agents can coordinate without a central server. It's all compiled to a tiny 364KB WebAssembly binary that runs anywhere JavaScript runs.
This library gives you everything you need to build distributed AI systems: cryptographic identity for each agent, encrypted communication between them, lightning-fast vector search for finding the right agent for each task, consensus protocols so your agents can coordinate without a central server, and self-learning neural networks that continuously optimize agent routing based on real-world outcomes. It's all compiled to a tiny 364KB WebAssembly binary that runs anywhere JavaScript runs.
**The key insight:** Instead of paying cloud providers to run your AI infrastructure, you use the computing power that's already there - your users' devices. Each browser becomes a node in your swarm. The more users you have, the more powerful your system becomes, and it still costs you nothing.
**The key insight:** Instead of paying cloud providers to run your AI infrastructure, you use the computing power that's already there - your users' devices. Each browser becomes a node in your swarm. The more users you have, the more powerful your system becomes - and with built-in self-learning capabilities (LoRA fine-tuning, EWC++ continual learning, ReasoningBank experience replay), your swarm gets smarter over time while still costing you nothing.
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examples/edge/pkg/package.json
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{
"name": "@ruvector/edge",
"version": "0.1.6",
"version": "0.1.7",
"type": "module",
"description": "Free edge-based AI swarms in the browser - P2P, crypto, vector search, neural networks. Install @ruvector/edge-full for graph DB, SQL, ONNX embeddings.",
"main": "ruvector_edge.js",