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A note before we begin: This is the twelfth 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
Lesson 9: Memory, Sub-Agents, and Parallel Research
Lesson 10: Wide Research for Screening at Scale
Lesson 11: Iterating Prompts and Structured Idea Write-Ups
Why This Matters for Investors
So far we have asked assistants to do things for us. Today we start asking an assistant to build small tools that do those things on our behalf, on our schedule, without us in the room. This matters because everything we have built to date (the Spaces, the Projects, etc.) was research scaffolding. It is excellent at one-off questions and it improves our judgment in real time. What it cannot do is run while we sleep, screen overnight, hand us a triage pack at 7 a.m., or remember last week’s pull date when it builds this morning’s. That is what the next five lessons are for, and Claude Code is the workshop where we build it.
The temptation, when a non-coder opens a terminal, is to either freeze or pretend. Instead, we will walk the middle path: ask Claude Code to plan before it edits, ask it to explain every line in plain English, and refuse to keep any script we could not change later. The non-coder’s superpower is not that we suddenly write Python. It is that we know how to interrogate a thing we did not write until we trust it the same way we trust a sell-side model only after we have verified the inputs.
There is one more reason to take Claude Code seriously today rather than treating it as a developer’s toy. The investment-research artifacts we care about (a screener that runs daily, a watchlist monitor that pings us only when something material changes, a wide-research pass that produces a CSV every Friday morning) are exactly the kind of small, durable tools that hand-coding makes expensive and pair-programming with an assistant makes nearly free. The cost of building the first one used to be a developer relationship. The cost now is thirty minutes, a folder, and the willingness to read every line back.
By the end of today we will have Claude Code installed, a small Python script that pulls a recent 10-K from SEC EDGAR and prints the first 500 words of the risk-factors section for any ticker we choose, a line-by-line plain-English explanation of that script, and the start of a habit we will use for the rest of the bootcamp: plan first, smallest working version next, explain in English, keep only what we understand.
Let’s launch into today’s lesson.
