Building an Agentic Personal Trainer - Part 7: LLM Provider Abstraction
Running AI locally has no API costs—just electricity. Cloud providers charge per token. I wanted to switch between local and cloud models without rewriting my agent code.
Building an MCP Agentic Stock Trading System - Part 3: The Agentic Loop
The agentic loop is where LLMs become active problem-solvers instead of passive responders. The LLM doesn't just answer once—it iteratively calls tools, analyzes results, and decides what to check next. My trading agent uses this to analyze stocks: fetch data, calculate indicators, check trends, then make a decision.
