Ongoing and Future Development

• Installer Availability: The Ditana Assistant installer packages for macOS, Windows, and other Linux distributions (such as Debian and Fedora) are under active development. For these platforms, users are currently required to install Ditana Assistant from source using Poetry. This involves running terminal-based commands to set up a virtual environment and install dependencies as described in the Installation section. We aim to provide native installers and packages for a more seamless installation experience in future releases.

• Multimodal Integration: Future iterations of Ditana Assistant will focus on multimodal support, enabling contextual guidance by analyzing screenshots or controlling the mouse pointer for computer operation. By integrating visual recognition and advanced automation, the assistant will provide more intuitive, hands-free support. The goal of this development is to extend the capabilities of the Assistant from text-based prompts to comprehensive on-screen interaction, thereby increasing user efficiency and accuracy.

• Unit Testing: The number of unit tests in input_analyzers_ai_test.py is currently insufficient. We particularly need more tests for:

  • Cases where the assistant incorrectly identifies (or fails to identify) prompts suitable for terminal command generation.
  • Scenarios in which terminal commands are mistakenly suggested for normal prompts. We encourage users to submit pull requests with additional test cases, especially for edge cases they encounter.

• Wolfram|Alpha Integration: While basic integration is implemented for mathematical calculations and obvious real-time queries (e.g., weather information), this feature can be significantly expanded. Wolfram|Alpha offers extensive factual knowledge across various domains, which could enhance the assistant’s contextual understanding. Currently, there are no unit tests for this feature.

• Introspective Contextual Augmentation (ICA): This feature is thoroughly developed and rigorously tested. Its effectiveness has been validated through comprehensive, standardized tests for statistical significance, as detailed in the Statistical Evaluation and Optimization section. While proven effective, there are numerous ideas for further enhancements, outlined in the Experimentation and Further Development section. For a complete overview, see the Introspective Contextual Augmentation section.

We are actively working on improving cross-platform compatibility, expanding features, and enhancing overall reliability. We welcome contributions, especially in the form of testing, feedback, and pull requests for all environments and features.

Please note that while we strive for stability, Ditana Assistant is still evolving, and you may encounter bugs or unexpected behavior, particularly on less-tested platforms or with experimental features. We appreciate your patience and encourage you to report any issues you encounter.