Why Antigravity is relevant here
In Session 4 we emphasized that tooling matters less than workflow discipline. Antigravity is useful in that context because it can support the same planning-first approach we discussed for Cursor: define the task clearly, explore options before execution, and keep review checkpoints tight.
For students choosing tools based on cost or availability, this makes Antigravity a practical option rather than a compromise.
How to approach Antigravity in this course
Use it as a structured coding assistant, not as an automatic code generator. Start from a clear brief, ask for a plan, and execute in small batches. If you keep that sequence, the quality of your outputs is usually much higher and much easier to verify.
This is especially important for coursework: code that runs is not enough. You also need coherent economic reasoning, transparent assumptions, and a clear link between your question, your data, and your method.
A simple first-week setup
Create a small project folder with one notebook and one script. Ask Antigravity to plan a narrow task, for example: data import and variable construction for one indicator. Review the plan before building anything. Then execute one step, check it, and continue.
This gives you a low-risk environment to establish good habits before moving to larger project structures.
Official links
- Product site: antigravity.google
- Download/install entry point: antigravity.google/download
- Announcement and overview: Build with Google Antigravity
Verification reminder
Any agent can produce confident-looking output. Treat Antigravity output as draft work that still needs your validation, especially for interpretation, causal claims, and empirical design choices.