This section documents my complete AI development environment — from hardware specifications to cloud integrations. It’s both a reference for my own work and a resource for others building similar setups.
Stack Overview#
🖥️ Hardware — Local development machine, server specifications, and storage solutions.
💻 Software — Local LLMs, development environments, orchestration tools, and AI frameworks.
⚡ Workflows — Automation pipelines, agent configurations, and process optimizations using tools like n8n and custom scripts.
🔗 Integrations — External services and APIs I integrate with, from ElevenLabs for voice to Pinecone for vector storage.
Philosophy#
I believe in documenting everything for two reasons:
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Personal Reference — Complex setups are easy to forget. This serves as my own technical documentation.
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Community Knowledge — Others building similar systems can learn from my configurations, mistakes, and optimizations.
Each section includes actual configurations, real performance metrics, and honest assessments of what works (and what doesn’t).
Currently exploring: Local AI model optimization and workflow automation improvements.
External Integrations This page documents all external services, APIs, and cloud platforms integrated into my AI development workflow.
AI & ML Services Language Models
OpenAI (GPT-4, Codex) - Code generation and general AI tasks Anthropic (Claude Sonnet) - Primary coding assistant via Cursor Gemini CLI - Terminal assistance and quick queries Specialized AI Services
ElevenLabs (Voice synthesis and audio processing) Runway (Video generation and editing) [Additional AI services as integrated] Data & Vector Storage Vector Databases
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Hardware Stack My AI development setup is built around local computing power for model inference, experimentation, and development. Here’s the current configuration:
Development Machine Primary Workstation
CPU: [To be documented] GPU: [To be documented] RAM: [To be documented] Storage: [To be documented] Performance notes and optimization details coming soon.
Server Infrastructure Local AI Server
Purpose: Local LLM hosting and inference Specifications: [To be documented] Models currently running: [To be documented] Storage Solutions Development Storage
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Software Stack This page documents the software tools and frameworks that power my AI development workflow.
Development Environment AI Development Tools
Cursor (with Claude integration for coding) VS Code (backup editor) Git & GitHub (version control) GitHub Apps - Secure repository integration for automation Hugo (static site generation) Local AI Models
[To be documented as I set up local LLM infrastructure] Model serving: [TBD - likely Ollama or similar] Vector database: [Evaluating Pinecone vs local options] AI Frameworks & Libraries Python Ecosystem
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Workflows & Automation This page documents the automated workflows and processes that keep my AI development environment running smoothly.
Content Creation Workflows Blog Publishing Pipeline
Hugo static site generation Git-based version control Cloudflare Pages deployment Automatic builds on push Mermaid Diagram Generation
Current: Manual Mermaid.js integration Future: AI-powered flowchart generator (in development) Development Workflows AI-Assisted Coding
Cursor + Claude Sonnet for architecture and complex reasoning Real-time code generation and debugging Automated testing and deployment pipelines Project Management
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