Now, the same framework helps me with real estate: comparing neighborhoods, checking flood risk, weather patterns, school zones, old vs. new builds, etc. It’s a messy, multi-variable decision—which turns out to be a great use case for AI agents.
Instead of ChatGPT or Grok 4, I use mcp-agent, which lets me build a persistent, multi-agent system that pulls live data, remembers my preferences, and improves over time.
Key pieces: • Orchestrator: picks the right agent or tool for the job • EvaluatorOptimizer: rates and refines the results until they’re high quality • Elicitation: adds a human-in-the-loop when needed • MCP server: exposes everything via API so I can use it in Streamlit, CLI, or anywhere • Memory: stores preferences and outcomes for personalization
It’s modular, model-agnostic (works with GPT-4 or local models via Ollama), and shareable.
Let me know what you all think!
axezing121321•6mo ago
It’s symbolic only (no LLMs), designed for alignment auditing, law/policy frameworks, and decision explainability.
If anyone wants an example, I can post a breakdown here.
pizzathyme•6mo ago
What actual trades were made by the user/creator? What was the ROI? How did profitability compare to their returns before using this tool?
With today's LLM's it's easy for anyone to generate a 20-page "report" with a analysis about investments. But a report that, when followed, actually gives you above-average returns? No one has shown evidence of that yet.
axezing121321•6mo ago