# Curated Learning Paths Structured journeys through our content, designed to build your knowledge systematically. Choose a path based on your goals and experience level. <div class="callout" data-callout="info"> <div class="callout-title">How to Use Learning Paths</div> <div class="callout-content"> Each path is a curated sequence of articles designed to build knowledge progressively. Start at the beginning and work through in order for best results. </div> </div> --- ## Path Overview 1. **Getting Started with AI Agents** (Beginner → Intermediate) - 6 articles, ~45 min 2. **Building Production AI Systems** (Intermediate → Advanced) - 7 articles, ~60 min 3. **LLM Development Mastery** (Beginner → Advanced) - 6 articles, ~40 min 4. **Cutting-Edge AI Research** (Intermediate) - 6 articles, ~45 min 5. **Practical AI Implementation** (Beginner) - 6 articles, ~35 min 6. **Multi-Agent Systems Deep Dive** (Advanced) - 5 articles, ~35 min 7. **GPU ML Development** (Beginner → Intermediate) - 5 articles, ~51 min (Complete!) --- ## Path 1: Getting Started with AI Agents **Goal**: Build foundational knowledge of AI agents from basics to practical implementation **Level**: Beginner → Intermediate **Total Time**: ~45 minutes ### Your Journey <div class="topic-area"> #### Step 1: Understand the Fundamentals [[building-effective-ai-agents-openai-guide|Building Effective AI Agents: Key Insights from OpenAI's Practical Guide]] *Intermediate • 5 min* Start with OpenAI's practical framework for building agents. Learn core concepts, orchestration patterns, and best practices. #### Step 2: Learn the Tools [[cline-roo-code-quick-start|Cline and Roo Code: Quick Start Guide]] *Beginner • 10 min* Get hands-on with AI coding agents in VS Code. Learn installation, features, and optimization techniques. #### Step 3: Configure Your Environment [[mastering-clinerules-configuration|Mastering .clinerules: Advanced Configuration for AI-Assisted Development]] *Intermediate • 3 min* Master configuration for AI-assisted development with best practices and advanced patterns. #### Step 4: Optimize Your Workflow [[claude-code-best-practices|Claude Code: Best Practices for Agentic Coding]] *Beginner • 7 min* Learn to optimize your workflow with Claude Code, including setup customization and advanced techniques. #### Step 5: Understand System Architecture [[agent-architectures-with-mcp|Agent Architectures with Model Context Protocol: A Technical Survey]] *Advanced • 11 min* Technical survey of architectural patterns for implementing AI agents with Model Context Protocol. #### Step 6: Implement MCP Integration [[model-context-protocol-implementation|Implementing Model Context Protocol (MCP) Across AI Coding Assistants]] *Intermediate • 3 min* Comprehensive guide to implementing MCP across different AI coding assistants with practical examples. </div> **Next Steps**: After completing this path, explore [[#Path 2 Building Production AI Systems|Building Production AI Systems]] or dive into specific tools in [[index/by-tag#roo-code|#roo-code]] articles. --- ## Path 2: Building Production AI Systems **Goal**: Design and implement production-grade AI systems with proper architecture **Level**: Intermediate → Advanced **Total Time**: ~60 minutes ### Your Journey <div class="topic-area"> #### Step 1: Foundation - RAG Systems [[building-markdown-rag-system|Building a Markdown RAG System: A Practical Guide to Document-Grounded AI]] *Intermediate • 3 min* Start with RAG fundamentals through a practical markdown-based implementation. #### Step 2: Advanced Prompt Engineering [[advanced-prompt-engineering-oncology-ds|Advanced Prompt Engineering for Oncology Data Science: Techniques for Robust AI Systems]] *Advanced • 5 min* Learn advanced prompt engineering techniques (CoT, ReAct, Structured Outputs) for production systems. #### Step 3: Agent Architecture Patterns [[agent-architectures-with-mcp|Agent Architectures with Model Context Protocol: A Technical Survey]] *Advanced • 11 min* Deep dive into architectural patterns for production-ready AI agents. #### Step 4: System Design Case Study [[manus-im-system-architecture|Inside Manus.im: The Elegant Architecture Behind a Powerful AI Agent]] *Advanced • 8 min* Analyze a production AI agent system architecture and learn design principles. #### Step 5: Multi-Agent Orchestration [[anthropic-multi-agent-research-system|Anthropic's Multi-Agent Research System: Engineering Autonomous Scientific Discovery]] *Advanced • 5 min* Understand how to orchestrate multiple agents for complex tasks. #### Step 6: Content Automation Pipeline [[agentic-content-creation-openai-guide-case-study|Agentic Content Creation: From PDF to Polished Blog in Under a Minute]] *Intermediate • 7 min* Real-world case study of building an automated content pipeline. #### Step 7: Production Deployment [[hybrid-deployment-vercel-render-digitalocean|Deployment Dilemma: When to Use Vercel, Render, or Digital Ocean for React/Python Apps]] *Intermediate • 7 min* Choose the right deployment platform for your production AI applications. </div> **Next Steps**: Explore [[#Path 6 Multi-Agent Systems Deep Dive|Multi-Agent Systems]] or check out [[index/by-topics#system-architecture|system-architecture]] articles. --- ## Path 3: LLM Development Mastery **Goal**: Master LLM development from prompt engineering to optimization **Level**: Beginner → Advanced **Total Time**: ~40 minutes ### Your Journey <div class="topic-area"> #### Step 1: Why Prompt Engineering Matters [[unlocking-ai-value-with-prompt-engineering|Unlock AI's Full Potential: Why Prompt Engineering is Your Business Superpower]] *Beginner • 4 min* Understand the business value and fundamentals of prompt engineering. #### Step 2: Advanced Techniques [[advanced-prompt-engineering-oncology-ds|Advanced Prompt Engineering for Oncology Data Science: Techniques for Robust AI Systems]] *Advanced • 5 min* Learn CoT, ReAct, and structured outputs for robust systems. #### Step 3: Comparative Analysis [[expert-conductor-prompt-llm-comparison|The Expert Conductor Prompt: A Comparative Analysis of LLM Reasoning Patterns]] *Advanced • 9 min* Compare how different LLMs handle complex prompts. #### Step 4: Tool Integration [[elevating-prompt-engineering-with-integrated-tools|Elevating Prompt Engineering with Integrated Tools]] *Intermediate • 6 min* Build integrated tools that streamline LLM workflows. #### Step 5: Programming Framework [[dspy-programming-language-models-at-scale|DSPy: The Programming Revolution for Language Model Applications]] *Intermediate • 5 min* Learn DSPy's systematic approach to LLM development with 25-65% performance improvements. #### Step 6: Model Selection [[gpt-4-1-release-technical-analysis|GPT-4.1 Technical Analysis: API-Only Release Signals OpenAI's Agent-First Strategy]] *Intermediate • 6 min* Understand how to choose the right model for your use case. </div> **Next Steps**: Explore [[index/by-tag#prompt-engineering|#prompt-engineering]] or dive into [[#Path 4 Cutting-Edge AI Research|Cutting-Edge AI Research]]. --- ## Path 4: Cutting-Edge AI Research **Goal**: Stay current with latest AI developments and understand their implications **Level**: Intermediate **Total Time**: ~45 minutes ### Your Journey <div class="topic-area"> #### Step 1: Reasoning Models [[openai-o3-o4-mini-codex-release-analysis|OpenAI's o3 and o4-mini: Business Impact of Advanced Reasoning Models]] *Intermediate • 5 min* Understand the latest reasoning models and their business impact. #### Step 2: Think Tool Innovation [[claude-think-tool-technical-review|Claude's Think Tool: A Technical Deep Dive and Cross-Model Analysis]] *Intermediate • 8 min* Explore Claude's reflection mechanisms and how they enhance reasoning. #### Step 3: Open Source at Scale [[deepseek-v3-0324-technical-review|DeepSeek V3-0324: Business Impact of Open-Source AI at Scale]] *Intermediate • 4 min* Analyze how open-source models are reshaping enterprise AI strategies. #### Step 4: Novel Architectures [[gemini-diffusion-google-deepmind-analysis|Gemini Diffusion: What if Text Generators Worked Like Stable Diffusion for Words?]] *Advanced • 9 min* Explore Google DeepMind's breakthrough in discrete-token diffusion. #### Step 5: Collective Intelligence [[sakana-ai-ab-mcts-collective-intelligence|Sakana AI's AB-MCTS: Orchestrating Collective Intelligence in Frontier AI Models]] *Advanced • 7 min* Understand multi-model cooperation achieving 39.2% solve rate on ARC-AGI-2. #### Step 6: Future Scenarios [[analyzing-the-ai-2027-scenario|A Technical Deep Dive into the AI-2027 Scenario: Capabilities, Alignment, and Geopolitics]] *Intermediate • 7 min* Explore predictions for AI capabilities and implications through 2027. </div> **Next Steps**: Follow [[ai-task-doubling|AI Task Completion trends]] or explore [[index/by-tag#ai-reasoning|#ai-reasoning]]. --- ## Path 5: Practical AI Implementation **Goal**: Get up and running with AI tools quickly **Level**: Beginner **Total Time**: ~35 minutes ### Your Journey <div class="topic-area"> #### Step 1: Set Up Your Environment [[roo-code-github-copilot-setup|How to Set Up Roo Code with GitHub Copilot: A Technical Guide]] *Beginner • 3 min* Quick setup guide for Roo Code with GitHub Copilot. #### Step 2: Learn the Basics [[cline-roo-code-quick-start|Cline and Roo Code: Quick Start Guide]] *Beginner • 10 min* Master the fundamentals of AI coding assistants. #### Step 3: Advanced Features [[roo-code-codebase-indexing-free-setup|Supercharging Code Discovery: My Journey with Roo Code's Free Codebase Indexing]] *Beginner • 12 min* Set up professional-grade semantic code search completely free. #### Step 4: Create Custom Modes [[custom-modes-quick-start|Creating Custom Modes in Roo Code: A Quick Start Guide]] *Beginner • 3 min* Build specialized AI agents for your workflow. #### Step 5: Build a Web App [[transforming-research-into-interactive-app|Transforming AI Research into an Interactive Web Application: A Case Study]] *Intermediate • 3 min* Turn complex data into interactive applications. #### Step 6: Publish Your Work [[obsidian-publish-article-garden|Creating a Technical Article Garden with Obsidian Publish]] *Beginner • 4 min* Share your knowledge with an interconnected digital garden. </div> **Next Steps**: Explore [[claude-code-best-practices|Claude Code best practices]] or check [[index/by-tag#developer-tools|#developer-tools]]. --- ## Path 6: Multi-Agent Systems Deep Dive **Goal**: Master multi-agent system architecture and implementation **Level**: Advanced **Total Time**: ~35 minutes ### Your Journey <div class="topic-area"> #### Step 1: Foundation - OpenAI's Guide [[building-effective-ai-agents-openai-guide|Building Effective AI Agents: Key Insights from OpenAI's Practical Guide]] *Intermediate • 5 min* Build foundational understanding of agent orchestration patterns. #### Step 2: Research Systems [[anthropic-multi-agent-research-system|Anthropic's Multi-Agent Research System: Engineering Autonomous Scientific Discovery]] *Advanced • 5 min* Study a production multi-agent research system from Anthropic. #### Step 3: Comparative Analysis [[manus-im-vs-camel-ai-owl|Manus IM vs CAMEL & AI-OWL: Comparative Analysis of Multi-Agent Research Systems]] *Advanced • 5 min* Compare different approaches to multi-agent research automation. #### Step 4: System Architecture [[manus-im-system-architecture|Inside Manus.im: The Elegant Architecture Behind a Powerful AI Agent]] *Advanced • 8 min* Deep dive into elegant multi-agent system architecture. #### Step 5: Architectural Comparison [[manus-vs-mymanus-system-architecture|Manus.im vs MyManus: A Technical Deep Dive into AI Agent System Architecture]] *Advanced • 3 min* Understand how core components serve different deployment models. #### Step 6: Collective Intelligence [[sakana-ai-ab-mcts-collective-intelligence|Sakana AI's AB-MCTS: Orchestrating Collective Intelligence in Frontier AI Models]] *Advanced • 7 min* Explore cutting-edge multi-model cooperation techniques. </div> **Next Steps**: Explore [[index/by-topics#multi-agent-systems|multi-agent-systems]] or dive into [[agent-architectures-with-mcp|MCP architectures]]. --- ## Path 7: GPU ML Development **Goal**: Build production-ready ML workspaces on GPU infrastructure from scratch **Level**: Beginner → Intermediate **Total Time**: 51 minutes (Complete Series!) ### Your Journey <div class="topic-area"> #### Step 1: Design Your Workspace Structure [[building-production-ml-workspace-part-1-structure|Building a Production ML Workspace: Part 1 - Designing an Organized Structure]] *Beginner • 8 min* Start with a battle-tested workspace structure that scales from 1 to 100+ projects. Learn organizational principles for Ollama models, fine-tuning, agents, and experiments. #### Step 2: Build Documentation Systems [[building-production-ml-workspace-part-2-documentation|Building a Production ML Workspace: Part 2 - Documentation Systems That Scale]] *Beginner • 7 min* Create a three-tier documentation system that captures ML work for debugging, review, and blog content—turning experiments into shareable knowledge. #### Step 3: Master Experiment Tracking [[building-production-ml-workspace-part-3-experiments|Building a Production ML Workspace: Part 3 - Experiment Tracking and Reproducibility]] *Intermediate • 12 min* Implement MLflow tracking, reproducible workflows, and structured systems for managing ML research that scales from prototype to production. #### Step 4: Build Production-Ready Agents [[building-production-ml-workspace-part-4-agents|Building a Production ML Workspace: Part 4 - Production-Ready AI Agent Templates]] *Intermediate • 10 min* Create production-ready AI agents with standardized templates, tool integration patterns, comprehensive testing, and deployment frameworks. #### Step 5: Enable Team Collaboration [[building-production-ml-workspace-part-5-collaboration|Building a Production ML Workspace: Part 5 - Team Collaboration and Workflow Integration]] *Intermediate • 14 min* Complete your workspace with team collaboration patterns, workflow automation, version control strategies, and integration frameworks that scale. </div> **Next Steps**: Apply these patterns to your own ML infrastructure, explore [[cline-roo-code-quick-start|AI coding assistants]], or check [[index/by-tag#gpu-computing|#gpu-computing]]. <div class="callout" data-callout="success"> <div class="callout-title">Series Complete!</div> <div class="callout-content"> This complete 5-part series takes you from empty directory to full production ML workspace with team collaboration. All articles are now available! </div> </div> --- ## Custom Learning Paths Can't find the right path? Create your own: 1. **Browse by [[index/by-topics|Topics]]** to find articles in your area of interest 2. **Filter by [[index/by-difficulty|Difficulty]]** to match your experience level 3. **Explore [[index/by-tag|Tags]]** to discover related technologies 4. **Check [[by-date|Recent Articles]]** for the latest content <div class="callout" data-callout="tip"> <div class="callout-title">Learning Path Tips</div> <div class="callout-content"> - **Take notes** as you progress through a path - **Try the code** in practical articles - **Build projects** to reinforce learning - **Revisit advanced topics** after gaining experience - **Share your progress** on social media </div> </div> --- ## Quick Navigation - [[⌂ Home|Back to Home]] - [[index/by-tag|Browse by Tag]] - [[by-date|Browse by Date]] - [[index/by-difficulty|Browse by Difficulty]] - [[index/by-topics|Browse by Topics]] - [[by-series|Article Series]]