Technology Diffusion Agent
A completed course project on its own GitHub branch. Not deployed.
A specialist that analyzes diffusion through adopter categories and the five innovation characteristics while separating social uptake from firm adoption.
- Why
- I wanted to stop treating attention, enthusiasm and adoption as equivalent.
How I built it
- I added Rogers-based diffusion criteria to the specialist.
- I separated broad diffusion indicators from application-specific evidence.
- I tested a contrast scenario and one case with weak evidence.
- I validated the structured response and kept limits visible.
- To copy it you need
- Start with one technology and an identifiable user group, then assemble dated adoption evidence.
Try it yourself
Copy this prompt and paste it into ChatGPT or Claude to try it yourself.
I want to build a small version of Jovan Diaz's Technology Diffusion Agent from his What Are We Building? profile. It is a course project, so treat it as a study exercise. It looks at adopter categories and five innovation characteristics, and it keeps social uptake apart from adoption by a company. His gotcha: hype, surveys and marketing claims are weak evidence of lasting adoption. Start with one technology and one identifiable user group. Ask me which ones, then help me gather dated adoption evidence and label what is only attention. Ask me for anything you need.
Tools I used for this project
ChatGPT: I used ChatGPT to draft, test, critique or work through the workflow.
Codex: I used Codex to help build or revise repository files and tests.
Python: I used Python scripts or validation tools to process and check structured outputs.
GitHub: GitHub holds all of my repositories: code, instructions, changes and review history.
