Module 1: Overview of Vibe Coding on AI Application Development
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AI Agent fundamentals • Tools Setup (Grok / Cursor / Claude Code / GitHub)
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Application solution framework • Spec Driven Design
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Vibe Coding Anti-Patterns (over-prompting, context poisoning, accepting first generation, etc.)
Module 2: Guiding Program Logic with Vibe Coding
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How to direct the AI when generating data structures, Pandas, loops, conditionals & functions
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Verification patterns & test-driven vibe coding • When to force specific libraries vs. invent
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Use case with vibe coding + Spec + Evaluation Checklist
Module 3: Workflow Automation
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LLM API + Frontend + Backend Design
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Use case: OCR processing + Expense/Budget Monitoring Agent
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Ends with Spec + Evaluation Checklist (success criteria, error handling, basic observability)
Module 4: Web Scraping as an Agent Capability
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Web scraping techniques with Python (vibe coding)
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Treating scrapers as tools that agents can call
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Integration patterns so scraping becomes a reusable capability rather than a standalone skill
Module 5: Web Services Integration and Automation
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Webhooks / Database / Web Scraping / Telegram
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Use Case: News / Sentiment Analysis engine
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Ends with Spec + Evaluation Checklist
Module 6: Claude Code CLI
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Overview and setup
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Skills & subagents (high-leverage daily workflow)
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Practical patterns for living inside Claude Code long-term
Module 7: AI Agent Deployment with MCP Servers
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n8n introduction
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Building MCP servers with Python and vibe coding
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Connecting Claude Code Skills to MCP tools
Module 8: AI Agent Development with Hermes Agent
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Setup of Hermes Agent and common use cases
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Trade-offs: when to stay in Claude Code + MCP vs. moving to a dedicated agent framework
Module 9: Multi-agent Deployment
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Multi-agent deployment (e.g. Hermes Agent / Claude Code / Google ADK/ n8n)
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Explicit decision framework: Claude Code + MCP vs. dedicated multi-agent frameworks — when each is the right choice
Module 10: Cloud Solution Deployment + Production Readiness
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AWS / Azure Cloud Solution Deployment
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Evaluation, Observability & Cost Control (latency, token spend, tool-call success, hallucination rate, model routing, caching)
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Cost-aware deployment patterns • Capstone: multi-agent production system combining earlier use cases