Tvap Story A mobile application on Flutter with a custom text editor
Challenge
For the Tvap Story project, we have developed a cross-platform mobile platform for authors. The key result was a high-performance text editor created from scratch. It allows you to combine long texts with photos, audio and video in a single ribbon with support for dragging blocks.
Task:
The entrepreneur came up with the idea of an application for working with long texts ("stories"), where the author can add an unlimited number of photos, audio and video in any order. A previous attempt to implement the project in Telegram failed due to platform limitations.
The main requirements included:
- Creating a stable text editor for working with large amounts of data (up to 180 MB).
- Support for all media formats.
- Intuitive interface with the function of dragging blocks.
- Ensuring smooth operation (60 fps) even with large texts.
- Supports 8 interface languages, including Chinese and Japanese.
For the Tvap Story project, we have developed a cross-platform mobile platform for authors. The key result was a high-performance text editor created from scratch. It allows you to combine long texts with photos, audio and video in a single ribbon with support for dragging blocks.
Task:
The entrepreneur came up with the idea of an application for working with long texts ("stories"), where the author can add an unlimited number of photos, audio and video in any order. A previous attempt to implement the project in Telegram failed due to platform limitations.
The main requirements included:
- Creating a stable text editor for working with large amounts of data (up to 180 MB).
- Support for all media formats.
- Intuitive interface with the function of dragging blocks.
- Ensuring smooth operation (60 fps) even with large texts.
- Supports 8 interface languages, including Chinese and Japanese.
Solution
The result was a custom text editor based on Flutter, since the ready-made solutions did not provide the required performance and flexibility.
Key decisions:
- For maximum performance, a treebased data model and deep optimization are used (processing 100 KB of text - <1 ms).
- Stable text input and intuitive drag-and-drop interface with behavior synchronization on iOS and Android are implemented.
- FFmpeg integration for working with video and lazy media loading protect the device's memory.
- Testing with texts up to 180 MB confirmed the fault tolerance of the system.
The result was a custom text editor based on Flutter, since the ready-made solutions did not provide the required performance and flexibility.
Key decisions:
- For maximum performance, a treebased data model and deep optimization are used (processing 100 KB of text - <1 ms).
- Stable text input and intuitive drag-and-drop interface with behavior synchronization on iOS and Android are implemented.
- FFmpeg integration for working with video and lazy media loading protect the device's memory.
- Testing with texts up to 180 MB confirmed the fault tolerance of the system.
Results
The cross-platform app has been successfully released in the App Store. The editor demonstrates stable 60 fps and reliable operation with any volume of text. The functionality includes intuitive dragging of blocks, combining text with media, and support for 8 languages. Scalability is provided by Selectel S3 cloud storage for unlimited user content.
Technology Stack:
Python, Selectel S3, Flutter
The cross-platform app has been successfully released in the App Store. The editor demonstrates stable 60 fps and reliable operation with any volume of text. The functionality includes intuitive dragging of blocks, combining text with media, and support for 8 languages. Scalability is provided by Selectel S3 cloud storage for unlimited user content.
Technology Stack:
Python, Selectel S3, Flutter