Emerging Technology Landscape Agent
A completed course project on its own GitHub branch. Not deployed.
A dated landscape analyst that compares market claims, independent evidence, competing alternatives and concrete monitoring triggers by application.
- Why
- I wanted evidence of real maturity, not a list of headlines about agentic AI.
How I built it
- I assembled a six-source tracking portfolio and documented source types.
- I compared hypothetical retail support and consulting research uses.
- I made hype-sensitive claims and independent counter-signals explicit.
- I revised the agent record and reran response and frozen-core validation.
- To copy it you need
- Start with a dated six-source portfolio, including independent research, and separate broad trends from the exact application.
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 Emerging Technology Landscape Agent from his What Are We Building? profile. It is a course project, so treat it as a study exercise. It compares market claims with independent evidence and competing alternatives for one application, and sets concrete triggers for what to watch. It starts from a dated list of six sources, including independent research. His gotcha: his first output left the evidence list and the hype-sensitive claims too vague, and he revised it after critique. Start by asking which technology and application I mean, then help me build my own dated six-source list. 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.
