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A note before we begin: This is the ninth lesson in a 16-lesson experiment. I am doing every lesson alongside you, on the same tools, with the same constraints. Some lessons will land cleanly. Some will lead to dead ends and need rework. We’ll figure out what works, together.
If you are catching up, here’s what came before today’s lesson:
Intro: Build Your Own Investment Idea Engine
Lesson 1: How LLMs Work, and How to Defend Against Hallucinations
Lesson 2: Prompt Patterns That Outperform Casual Prompting
Lesson 3: Tools, Agents, and Structured Output
Lesson 4: SEC EDGAR, the Primary Source
Lesson 5: FRED Macro and Sector Data, the Free Read
Lesson 6: FMP API Key and the First Checked Data Pull
Lesson 7: Other Data Sources, and Idea Engine Formats
Lesson 8: Spaces, Projects, and Connectors
Why This Matters for Investors
In the last lesson we built three durable rooms across Perplexity and Claude. That fixed the “blank window every morning” problem at the room level: the system prompt, the connectors, and the reference files now persist between sessions. Today we go one layer deeper. Memory is how the assistant carries durable facts about us as an investor (our style, our market-cap band, our forbidden behaviors) across every room and every thread. Sub-agents are how that same assistant runs multiple bounded research tracks in parallel, instead of taking one question at a time.
Together they change the shape of the work. A single chat is one analyst answering one question. A room with memory plus sub-agents is closer to a small research operation: a lead who already knows our preferences, two or three junior agents pulling on bounded threads at the same time, and a final hand-off back to us for the part that actually matters, which is judgment about whether the table in front of us is even answering the right question. We still own that judgment. The point of today is to make the support around it materially better.
There is a softer reason as well. As investors we accumulate small, durable preferences over years (the kinds of businesses we trust, the kinds of balance sheets we avoid, the writing voice we prefer, the geographies we read fluently). Most of that detail evaporates inside a generic chat session, so we end up restating it every time. Stored well, those preferences become a quiet compounding asset across the engine.
Let’s launch into today’s lesson.
