# Practical Applications <div class="callout" data-callout="info"> <div class="callout-title">Category Overview</div> <div class="callout-content"> Hands-on guides, real-world implementations, and practical tools for AI practitioners. </div> </div> --- ## Topics Covered <div class="topic-area"> ### Setup Guides - Tool installation - Configuration - Environment setup - Integration </div> <div class="topic-area"> ### Implementation - Web development - Deployment strategies - Case studies - Workflows </div> <div class="topic-area"> ### Knowledge Management - Documentation - Publishing - Digital gardens - Content creation </div> --- ## Articles in This Category **26 articles** • Sorted by most recent <div class="latest-articles"> ### [[auto-updating-claude-code-cli-tools|Auto-Updating CLI Tools for Claude Code Skills]] 🆕 *intermediate • 8 min read* • 2026-01-12 Build an automated system that discovers CLI dependencies from Claude Code skill metadata and keeps them updated weekly via LaunchAgent. ### [[syncing-twitter-bookmarks-to-obsidian|Auto-Sync Twitter Bookmarks to Obsidian with Bird CLI]] *intermediate • 12 min read* • 2026-01-12 Build a lightweight automation that syncs your Twitter bookmarks to Obsidian every 2 hours using Bird CLI and a simple Python script. ### [[my-personal-ai-assistant-clawdbot-seneca|My Personal AI Assistant Lives Everywhere: Building with Clawdbot]] *intermediate • 9 min read* • 2026-01-10 How I built Seneca, a personal AI assistant with memory and soul that lives across Mac, Telegram, WhatsApp, and my entire infrastructure using Clawdbot. ### [[debugging-claude-code-with-claude|Debugging Claude Code with Claude: A Meta-Optimization Journey]] *intermediate • 15 min read* • 2026-01-10 Using Claude to analyze its own debug logs and session data reveals hidden performance bottlenecks and provides a systematic approach to optimizing AI development tools. ### [[when-launchagents-attack-100-dollar-api-crash-loop|When LaunchAgents Attack: A $100 API Crash Loop Story]] *intermediate • 8 min read* • 2026-01-09 How three duplicate LaunchAgents, a port conflict, and missing cost monitoring created 4,590 restart attempts and $100 in overnight API charges. ### [[Knowledge/Blog-Obsidian/Practical Applications/claude-skills-vs-mcp-servers|Claude Skills vs MCP Servers: Why Context Efficiency Matters]] *intermediate • 12 min read* • 2025-11-06 Learn how Claude Code skills provide a lightweight alternative to MCP servers with 30% better context efficiency while maintaining flexibility. ### [[building-ai-research-night-shift|My AI Research Assistant Works the Night Shift (A Claude Code Skill Story)]] *intermediate • 10 min read* • 2025-11-06 How I built a Claude Code skill that researches AI developments overnight using intelligent automation that adapts, prevents duplicates, and provides instant answers. ### [[prompt-build-ai-landscaping-skill|Prompt for Claude Code: Build AI Landscaping Skill]] 🆕 *intermediate • 8 min read* • 2025-11-06 Copy-paste prompt for Claude Code to build a complete AI research intelligence skill with duplicate prevention, structured storage, and instant retrieval. ### [[medical-llm-fine-tuning-70-to-92-percent|How I Delegated a 9-Day Medical AI Experiment (and Learned When to Step In)]] *intermediate • 14 min read* • **DGX Lab Chronicles Part 6** • 2025-10-28 Delegating a complex 60-hour ML experiment to Claude revealed when to intervene and when to trust. Learn the decision points that turned 70% accuracy into 92.4%. ### [[dgx-lab-benchmarks-vs-reality-day-4|DGX Lab: When Benchmark Numbers Meet Production Reality - Day 4]] *intermediate • 10 min read* • **Series Part 4** • 2025-10-26 NVIDIA's DGX Spark benchmarks show 82,739 tokens/sec for training and sub-1% accuracy degradation with FP4. After 6 days of intensive ML workloads, I reveal what the benchmarks don't tell you about GPU inference failures, memory fragmentation, and production workarounds. ### [[three-days-to-build-ai-research-lab-dgx-claude|Three Days to Build an AI Research Lab: My DGX + Claude Experiment]] *intermediate • 7 min read* • **Series Part 1** • 2025-10-21 From hardware delivery to production ML experiments in 72 hours, building an AI research lab with Claude Code as a thought partner and documenting the entire journey. ### [[dgx-lab-supercharged-bashrc-ml-workflows-day-2|DGX Lab: Supercharge Your Shell with 50+ ML Productivity Aliases - Day 2]] *beginner • 10 min read* • **Series Part 2** • 2025-10-20 Transform your default shell into a productivity powerhouse with GPU monitoring shortcuts, smart aliases, and custom functions—setup in 5 minutes, benefit forever. ### [[dgx-lab-intelligent-gateway-heuristics-vs-ml-day-1|DGX Lab: When Simple Heuristics Beat ML by 95,000x - Day 1]] *intermediate • 14 min read* • **Series Part 1** • 2025-10-20 Building an intelligent AI gateway that routes requests 95,000x faster than ML while maintaining 90% accuracy—proving that smart heuristics can outperform deep learning. ### [[syncing-claude-code-configs-across-machines|Syncing Claude Code Configurations Across Multiple Machines: A Practical Guide]] *intermediate* • 15 min • 2025-10-20 Learn how to intelligently sync Claude Code configurations across Mac, Pi, and DGX boxes while preserving machine-specific settings like model endpoints and API keys. ### [[building-production-ml-workspace-part-5-collaboration|Building a Production ML Workspace: Part 5 - Team Collaboration and Workflow Integration]] 🎉 *intermediate • 14 min read* • **Series Part 5/5 - Complete!** Complete your production ML workspace with team collaboration patterns, workflow automation, version control strategies, and integration frameworks that scale. ### [[building-production-ml-workspace-part-4-agents|Building a Production ML Workspace: Part 4 - Production-Ready AI Agent Templates]] *intermediate • 10 min read* • **Series Part 4/5** Build production-ready AI agents with standardized templates, tool integration patterns, comprehensive testing, and deployment readiness frameworks. ### [[building-production-ml-workspace-part-3-experiments|Building a Production ML Workspace: Part 3 - Experiment Tracking and Reproducibility]] *intermediate • 12 min read* • **Series Part 3/5** Master experiment tracking with MLflow, implement reproducible workflows, and build structured systems for managing ML research that scales from prototype to production. ### [[building-production-ml-workspace-part-2-documentation|Building a Production ML Workspace: Part 2 - Documentation Systems That Scale]] *beginner • 7 min read* • **Series Part 2/5** Build a three-tier documentation system that captures ML work for debugging, review, and blog content—turning your experiments into shareable knowledge. ### [[building-production-ml-workspace-part-1-structure|Building a Production ML Workspace: Part 1 - Designing an Organized Structure]] *beginner • 8 min read* • **Series Part 1/5** Learn how to design a scalable ML workspace structure that handles Ollama models, fine-tuning, agents, and experiments without becoming chaotic. ### [[roo-code-codebase-indexing-free-setup|Supercharging Code Discovery: My Journey with Roo Code's Free Codebase Indexing]] *beginner • 12 min read* Set up professional-grade semantic code search using Roo Code's codebase indexing with completely free tools - Qdrant Cloud and Google Gemini. ### [[unlocking-ai-value-with-prompt-engineering|Unlock AI's Full Potential: Why Prompt Engineering is Your Business Superpower]] *beginner • 4 min read* Master prompt engineering techniques to unlock better, more reliable AI outputs that drive real business results. ### [[elevating-prompt-engineering-with-integrated-tools|Elevating Prompt Engineering with Integrated Tools]] *intermediate • 6 min read* Building integrated prompt refinement tools transforms how developers interact with LLMs, streamlining workflows through concept elevation. ### [[hybrid-deployment-vercel-render-digitalocean|Deployment Dilemma: When to Use Vercel, Render, or Digital Ocean for React/Python Apps]] *intermediate • 7 min read* Practical guide to choosing the right deployment platform for React/Python applications across Vercel, Render, and Digital Ocean. ### [[cline-roo-code-quick-start|Cline and Roo Code: Quick Start Guide]] *beginner • 10 min read* Get started with Cline and Roo Code AI coding agents in VS Code, covering installation, features, and optimization techniques. ### [[roo-code-github-copilot-setup|How to Set Up Roo Code with GitHub Copilot: A Technical Guide]] *beginner • 3 min read* Step-by-step guide for setting up Roo Code with GitHub Copilot, leveraging Claude 3.7 Sonnet while maintaining enterprise compliance. ### [[transforming-research-into-interactive-app|Transforming AI Research into an Interactive Web Application: A Case Study]] *intermediate • 3 min read* Transform complex AI research output into an interactive web application using modern web technologies and Roo Code. ### [[obsidian-publish-article-garden|Creating a Technical Article Garden with Obsidian Publish]] *beginner • 4 min read* Learn how to set up Obsidian Publish for technical documentation and create an interconnected knowledge base that grows over time. </div> --- ## Navigation ### Browse Content - 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