You have 10 inbound lead emails sitting in your inbox right now. Some are hot — the right company size, the right pain. Some are noise. How long does it take you to triage them today?
Too long. I either spend an hour reading every one, or I guess and miss the good ones.
By Day 28, a Python agent classifies all 10 by fit tier, extracts company name and deal-size estimate into a structured list, and returns a ranked call queue. Before we build that, rate yourself honestly on six AI skills. Where you actually are today.
I've used ChatGPT. But building AI tools feels like a CTO thing.
ChatGPT is a chat interface. This track is the API layer underneath — the same function call, but you control the input, the output shape, and who it runs for. Rate honestly. Day 30 shows the gap.
Thirty days. One real agent: an AI sales pipeline. By Day 28 you'll have Python that takes a list of inbound-lead emails, classifies each by fit tier, extracts company name and deal-size estimate, and returns a sorted call list.
| Week | Focus | Founder framing |
|---|---|---|
| 1 | Basic agents, system prompts, chaining | "Summarise a customer email in one sentence." |
| 2 | Structured output, Pydantic, Literal | "Extract lead name, company, and deal size into a clean record." |
| 3 | Pipelines, batch processing | "Run the classifier on 10 leads at once." |
| 4 | Web search, tools, capstone | "Build the full AI sales agent with ranked output." |
Every lesson runs real AI models — results come from live inference, not mocked responses.
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You have 10 inbound lead emails sitting in your inbox right now. Some are hot — the right company size, the right pain. Some are noise. How long does it take you to triage them today?
Too long. I either spend an hour reading every one, or I guess and miss the good ones.
By Day 28, a Python agent classifies all 10 by fit tier, extracts company name and deal-size estimate into a structured list, and returns a ranked call queue. Before we build that, rate yourself honestly on six AI skills. Where you actually are today.
I've used ChatGPT. But building AI tools feels like a CTO thing.
ChatGPT is a chat interface. This track is the API layer underneath — the same function call, but you control the input, the output shape, and who it runs for. Rate honestly. Day 30 shows the gap.
Thirty days. One real agent: an AI sales pipeline. By Day 28 you'll have Python that takes a list of inbound-lead emails, classifies each by fit tier, extracts company name and deal-size estimate, and returns a sorted call list.
| Week | Focus | Founder framing |
|---|---|---|
| 1 | Basic agents, system prompts, chaining | "Summarise a customer email in one sentence." |
| 2 | Structured output, Pydantic, Literal | "Extract lead name, company, and deal size into a clean record." |
| 3 | Pipelines, batch processing | "Run the classifier on 10 leads at once." |
| 4 | Web search, tools, capstone | "Build the full AI sales agent with ranked output." |
Every lesson runs real AI models — results come from live inference, not mocked responses.