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.
Building an MCP Agentic Stock Trading System - Part 2: The MCP Servers and Tools
MCP servers are like USB hubs for AI—they provide standardized tools that any agent can plug into. My trading system has two: one fetches market data, the other calculates technical indicators. Write them once, use them with Claude, local LLMs, or even traditional code.
Building an MCP Agentic Stock Trading System - Part 1: The Architecture
I wanted to experiment with Model Context Protocol (MCP) and compare local LLMs against API-based ones. So I built a stock paper-trading system with three brains: a rules-based trader, Claude API, and a local Llama model in LM Studio. Same market data, different decision-making approaches.
Adding nano-banana 3 Support to My CLI Wrapper
Twenty-four hours after Google dropped nano-banana 3, I shipped support for it. New model, new resolutions (4K!), new features. This is what building with AI is like at this point in November 2025.
