Knowledge Base¶

This section contains deep dives into the technologies, protocols, and conceptual frameworks that power the AI Hub.

Start By Goal¶

Build A Website Or App

Start with AI Builder Index, then use the Free AI Website Playbook.
Build An AI-Driven Company Stack

Start with AI Company Starter Stack.
Understand The Full Ecosystem

Start with AI Tooling Landscape — 2026 Overview.
Choose An Assistant Or Agent

Start with AI Tool Access Matrix.
Choose The Right Model

Start with Model Routing Guide.
Review The Strongest Repos From GitHub Stars

Start with Starred AI / Agent Repositories Over 10K Stars.

Getting Started with the KB¶

The Knowledge Base (KB) is designed to take you from conceptual understanding to production-ready implementation. To get the most out of this resource, we recommend the following tiered approach:

1. Orientation & Landscape¶

Begin by understanding the "Big Picture" of the current AI ecosystem. - Review: AI Tooling Landscape for a categorical overview. - Matrix: Consult the AI Tool Access Matrix to see which tools have the permissions (Gmail, Files, etc.) you need.

2. Strategic Selection¶

Once oriented, choose the specific models and stacks that fit your goals. - Model Choice: Use the Model Routing Guide to determine if you need Haiku, Sonnet, or an O-series reasoning model. - Default Stacks: If building a company, start with the AI Company Starter Stack.

3. Implementation Patterns¶

Transition from "what to use" to "how to build" using canonical patterns. - Foundational: Master the RAG Pattern and Agentic Workflows. - Advanced: Explore Data Copilot Agentic RAG for complex data synthesis.

4. Safety & Governance¶

Ensure your AI systems are resilient and secure before deployment. - Security: Audit your LLM Security & Privacy and Trust Boundaries. - Quality: Review the Standards to ensure your own documentation and workflows remain high-confidence.

Curated Guides¶

AI Builder Index - Discovery-oriented entry point for building websites, products, operations, and internal AI systems.
Free AI Website Playbook - Which website types fit free tiers, which hosts to choose, and how to prompt an LLM to build them.
AI Company Starter Stack - Opinionated default stack for building a company where AI is part of daily operations.
AI Tooling Landscape — 2026 Overview - High-level map of the entire AI tooling ecosystem in this repository.
AI Tool Access Matrix - Capability matrix for assistants, coding agents, workflow tools, self-hosted workspaces, and agent frameworks.
Model Routing Guide - Clear defaults and escalation rules for Haiku, Sonnet, Opus, GPT-5.4 effort levels, and GPT-5.3 Codex.
Starred AI / Agent Repositories Over 10K Stars - Practical ranking and usage guide based on your starred GitHub repos.

Deep Dives¶

Model Classes - Understanding the different types of LLMs (MoE, Reasoning, Multimodal, etc.).
System Prompts - Foundational instructions for frontier models and "high engineering" persona design.
Model Comparison and Evaluation - Guide to LLM leaderboards, benchmarks, and metrics.
LLM Security & Privacy - Deep dive into agentic security, SQL guardrails, and privacy patterns.
Agent Protocols - Deep dive into MCP (Model Context Protocol) and ACP (Agent Control Protocol).
API Pricing & Free Tier Matrix - Canonical tracker for provider pricing links and current free-tier availability.
AI Signal Sources - Curated company and independent technical blogs worth monitoring.
Essential AI Reading List — A curated guide to high-signal blogs, newsletters, and podcasts.
Architecture & Flows - High-level system design.

Implementation Patterns¶

RAG Pattern - Canonical implementation for Retrieval Augmented Generation.
Agentic Workflows - Designing loops and multi-agent systems.
Tool Calling & MCP - Native vs MCP-hosted tool patterns.
LLM Trust Boundaries - Security patterns for handling untrusted data.
Data Copilot MCP Tooling - Leveraging the Model Context Protocol for tool discovery and data synthesis.
Data Copilot Agentic RAG - Advanced RAG patterns using multi-agent orchestration for data tasks.
n8n Error Handling - Building resilient automation workflows.

Learning Paths¶

For Developers¶

For Operations¶

For Researchers¶

Infrastructure Research¶

Invisible Kubernetes - Patterns for zero-ops cluster management.
Google Axion - High-performance ARM-based AI compute.
Talos vs Ubuntu - Node OS comparison for K3s.
Real-time Sync Engines - CRDT and local-first architecture.

🚀 Purpose¶

The knowledge base serves as the "theory" section of the repository, providing the necessary context to effectively connect and configure the tools in the Tool Catalogue. It is designed to be a living resource that evolves alongside the frontier of AI capabilities.

🛠️ Contribution¶

We welcome deep dives into new technologies. Please follow the Contributing Guide. When adding new articles, ensure they follow the Standards and include relevant Architecture cross-links.

Knowledge Maintenance¶

This KB is maintained through automated "Ralph-loops" that: - Audit document quality and structural compliance. - Update model capability matrices based on new releases. - Deepen "Medium Confidence" documents into "High Confidence" references. - Verify cross-link integrity across the repository.

Sources / References¶

Contribution Metadata¶

Last reviewed: 2026-05-30
Confidence: high