Quest AI or Continual? A Comprehensive Review

Generates React components from Figma

Exports clean

extendable code

Built-in support for MUI

Chakra UI

Integrated animation library

Allows state and variant animations

Automatically pushes code to GitHub

Custom breakpoints for responsive design

Intelligent separation of presentation layer and business logic

Cloud-based predictive modeling

Uses SQL for app creation

Works with BigQuery

Snowflake

Redshift

and Databricks

No need for complex infrastructure

Models improve continually

Data and models stored on warehouse

Easily accessible to operational and BI tools

Restricted to Figma designs

Reliance on specific design systems

Limited to React components

No non-React code generation

Doesn't support all design systems

No built-in version control

No offline mode

Limited animation library

Custom breakpoints required

Limited nested components support

SQL-centric

Limited to cloud data platforms

Dependency on modern data stacks

No MLOPS infrastructure

Limited extensibility (Python only)

Dependent on dbt compatibility

Not suitable for traditional data management systems

Data must be on the same warehouse

No mention of multilingual support

Dependent on continuous access to data warehouse