Automation / n8n
Applied research and hands-on builds focused on n8n workflow automation, agentic AI architecture, and multi-model deployment. Key areas include designing secure agentic workflows with privacy-first data routing, benchmarking local vs. cloud inference for token and cost efficiency, and refining context and memory management.
AI Research & Setup
Research Focus:
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Multi-Agent Orchestration: Modality-agnostic, agent-to-agent workflows focused on security and privacy.
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Privacy-First Local AI: On-device open-weight models and local inference designed to secure sensitive financial and healthcare data.
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Context & Memory Management: Hybrid memory retrieval system for context-aware user personalization.
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Hybrid Inference Routing: Token efficiency, latency, and costs between cloud-based APIs and local environments.
Setup:
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IDEs & Code Editors: Cursor, VS Code, Claude Code, Replit (next)
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Agent Workflows & Integrations: Claude Code, n8n, MCP (Model Context Protocol) Servers, custom APIs; Google ADK, LangChain/LangGraph (next)
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Model Runtimes & Inference APIs: Claude API, Perplexity API, Google Gemini API/ AI Studio (cloud); Ollama, Docker, OpenRouter (local)
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AI Models: Claude, Perplexity, Google Gemini (proprietary); Distinct AI’s self-quantized models, and low-parameter/MoE models (Gemma, Cohere, and similar model families)
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Prototyping & Ideation: Figma, Lovable, Google AI Studio, Stitch, Nano Banana, Distinct AI
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Client UI & Knowledge Management: Open WebUI, Obsidian, Notion, Agentic RAG, RAG, Vector Database, Supabase






