Fedwiki and Edge Substrate

gemini

The **Fedwiki**, or Federated Wiki, is a unique wiki that operates as a network of personal, local-first websites rather than a single, centralized platform. Unlike traditional wikis that require an internet connection to a central server, each Fedwiki instance is a self-contained website that can run locally on a device, making it an excellent **edge substrate** for AI. An **edge substrate for AI** refers to a foundational environment on a local device that is capable of hosting and running AI applications directly where the data is generated, at the "edge" of the network, without a constant connection to the cloud. *** ### The Role of a Microsystem (Raspberry Pi) A **Raspberry Pi** is a powerful and affordable single-board computer, making it the perfect **microsystem** for this kind of application. Its small form factor and low power consumption allow it to be deployed in various environments—from smart home devices to industrial sensors—where it can collect and process data locally. *** ### How It Works: Key Principles By combining Fedwiki's architecture with the capabilities of a Raspberry Pi, a distributed, private, and intelligent network can be created. The synergy between these components enables AI to operate effectively at the edge through the following principles: * **Local-First Data Processing**: Fedwiki's design, where all data (pages, images, and other content) is stored locally on the Raspberry Pi, aligns perfectly with edge computing. This means AI models can be trained and run on the data without ever needing to upload it to a remote server. This approach drastically reduces latency, conserves bandwidth, and enhances data privacy. * **Federated Learning and Collaboration**: The "federated" nature of Fedwiki allows a network of Raspberry Pi devices to connect and share not the raw data itself, but rather the insights and model updates. This enables a form of **federated learning**, where each device trains an AI model on its own local data, and then only the changes to the model are aggregated with the network. This allows for a collective intelligence to emerge while keeping sensitive data private and secure on each individual device. * **Human-in-the-Loop AI**: Fedwiki's emphasis on user-created and user-edited content allows for a "human-in-the-loop" AI system. The AI can learn from a user's local wiki content, and the user, in turn, can edit or modify the wiki, directly influencing how the AI learns and behaves. This creates a feedback loop that helps the AI to be more personalized and context-aware. * **Hardware Acceleration**: While the Raspberry Pi's standard processor can handle some lightweight AI tasks using frameworks like TensorFlow Lite, it can be enhanced with an AI accelerator, such as the Raspberry Pi AI Kit, to handle more computationally intensive models. This allows the microsystem to perform real-time image recognition, natural language processing, or other complex tasks locally, turning the Fedwiki into a powerful tool for visual or conversational AI.

deepseek

## What is FedWiki as an Edge Substrate? A **federated wiki** at the edge would be a distributed, peer-to-peer knowledge base running directly on Raspberry Pi devices, serving as both: - A **local knowledge store** for AI models to reference - A **collaborative intelligence layer** that evolves through federation ## Key Implementation Patterns ### 1. Distributed Knowledge Caching for AI ```python # Example: Local knowledge retrieval for AI decision-making class EdgeAISystem: def __init__(self): self.local_wiki = FedWikiInstance() self.ai_model = load_ai_model() def make_decision(self, sensor_data): # Check local wiki for similar scenarios context = self.local_wiki.search_context(sensor_data) # Augment AI reasoning with local knowledge decision = self.ai_model.infer(sensor_data, context) # Log outcome to wiki for future learning self.local_wiki.record_outcome(sensor_data, decision, result) ``` ### 2. Federated Learning Substrate The wiki becomes a versioned repository for: - **Model parameters** and performance metrics - **Training data patterns** and annotations - **Failure cases** and corrections **Example Workflow:** 1. Raspberry Pi A improves its object detection in low light 2. It commits these learnings as wiki pages 3. Raspberry Pi B federates with A and incorporates these improvements 4. Both devices now have enhanced capabilities without centralized training ### 3. Context-Aware AI Personalization ```yaml # Wiki pages structure local context: - location_patterns: morning_light_conditions: "east_facing_shadow" common_objects: ["mailbox", "garden_gnome", "bird_feeder"] - user_preferences: alert_threshold: "high" notification_times: ["07:00", "18:00"] - device_capabilities: sensors_available: ["camera", "temperature", "motion"] processing_power: "raspberry_pi_4" ``` ### 4. Collaborative Problem Solving **Scenario: Multiple Pi-based security systems** - Each device encounters unique false positives - They share these as wiki cases: "shadows_from_tree ≠ intruder" - Federation creates a collective "experience database" - New devices bootstrap intelligence from peer knowledge ## Technical Architecture ### Local Wiki Structure on Raspberry Pi ``` /home/pi/fedwiki-ai/ ├── knowledge/ # Local wiki pages │ ├── sensor_patterns/ │ ├── model_updates/ │ └── edge_cases/ ├── models/ # AI models │ ├── current/ # Active model │ └── experimental/ # Testing improvements └── federation/ # Peer synchronization ``` ### Federation Protocol for AI Knowledge ```python class AIWikiFederation: def sync_with_peer(self, peer_address): # Exchange improved model parameters model_updates = self.get_model_improvements() peer_updates = peer.exchange_updates(model_updates) # Merge contextual knowledge self.wiki.merge_pages(peer_updates['knowledge']) # Update local model if peer has better performance if peer_updates['accuracy'] > self.current_accuracy: self.deploy_improved_model(peer_updates['model']) ``` ## Concrete Use Cases ### 1. Adaptive Home Automation ```yaml # Wiki page: "kitchen_lighting_patterns" - learned_behaviors: evening_cooking: "lights_100_brightness" movie_night: "lights_20_dim" morning_coffee: "lights_60_warm" - sensor_correlations: motion_7pm + sound_level_high → cooking_mode motion_9pm + tv_noise → movie_mode ``` ### 2. Distributed Environmental Monitoring Multiple Pi devices across a neighborhood: - Each learns local microclimate patterns - Federate to create hyperlocal weather models - Share anomaly detection knowledge (e.g., "sudden_temp_drop = open_window") ### 3. Collective Robotics Learning ```python # Pi-powered robot vacuum def improve_navigation(self): # Learn from stuck situations stuck_context = self.wiki.get_page("stuck_scenarios") new_avoidance_rules = self.analyze_patterns(stuck_context) # Share improvements self.federation.broadcast_improvement( "obstacle_avoidance_v2", new_avoidance_rules ) ``` ## Advantages of This Approach ### 1. **Resilience** - AI systems work offline using local wiki knowledge - No dependency on cloud services - Knowledge persists even if individual devices fail ### 2. **Privacy Preservation** - Personal data stays on local device - Only anonymized patterns or improvements are shared - Users control what knowledge gets federated ### 3. **Continuous Improvement** - Collective intelligence emerges from federation - Devices get smarter through peer learning - Knowledge evolves with community experience ### 4. **Context Awareness** - Each device maintains local context - AI decisions respect geographical and cultural nuances - Personalization happens naturally through local wiki content ## Implementation Challenges & Solutions ### **Challenge: Storage Limitations** **Solution:** Implement semantic compression in the wiki - store patterns rather than raw data, use efficient knowledge representations. ### **Challenge: Synchronization Conflicts** **Solution:** Use federated wiki's native conflict resolution with AI-assisted merging based on performance metrics. ### **Challenge: Knowledge Quality** **Solution:** Implement reputation systems where devices track which peer contributions actually improve local performance. ## Future Vision This creates an **"Organic IA"** ecosystem where: - Intelligence emerges from collective experience - Each device contributes to and benefits from shared knowledge - Systems become more contextually appropriate over time - No single entity controls the intelligence growth The fedwiki becomes not just a knowledge store, but a **living memory and learning system** for edge AI, enabling truly decentralized, adaptive intelligence that respects local context and user autonomy.

connections

patterning

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