Many tools today surface environmental information (air quality, weather alerts, heat, UV levels) through dashboards, indices, and numeric scores. While this data is accurate, people often struggle to translate it into concrete decisions in daily life (when to go outside, whether to exercise, what precautions to take).

As we prepare to launch AskIndra, we re thinking deeply about this translation layer between data and action.

From your experience building or using products that deal with environmental, health, or real-world data:

The shift toward "Vibe Coding" feels like we ve finally moved from being construction workers to being conductors. We are spending less time fighting syntax and more time sculpting the "intent" of our software.

However, as I ve been leaning into this AI-native workflow, I ve noticed a recurring tension that I d love to get the community s take on:

1. The "Black Box" Debt: When we "vibe" our way through a feature in 20 minutes that used to take 4 hours, are we unknowingly inheriting technical debt that will haunt us when the "vibe" inevitably breaks?

New AI models pop up every week. Some developer tools like @Cursor, @Zed, and @Kilo Code let you choose between different models, while more opinionated products like @Amp and @Tonkotsu default to 1 model.

Curious what the community recommends for coding tasks? Any preferences?