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The primary goal of this project is to develop an easy to use, personalized and continually improving digital assistant which analyzes software usage for repetitive patterns and learns to automate these procedures. The assistant should;


  1. Record all data necessary to be able to extract procedural patterns in software operation
  2. Provide contextually aware autocompletion predictions across any software program
  3. Execute predicted autocompletion procedures in any software context
  4. Provide interfaces to analyze performance and submit hints to assist in the discovery of patterns
  5. Operate seamlessly across operating systems
  6. Be able to automate and assist without voice commands (silent operation)


The plan is to develop the assistant in stages, incrementally building towards achieving all the goals outlined above. The currently planned releases target the following feature sets;


  1. Basic data collection and analysis
  2. Expanded data collection
  3. Real time predictions and inference of operational context using pre-trained deep neural nets
  4. Procedure triggers and conditional execution
  5. Integrated deep net training pipeline
  6. Third party assistant/RPA integrations
  7. HID control autocompletion
  8. Scene based reasoning in 3D and robotic environments


Version 0.1 – Basic data collection and analysis is in beta testing.

Version 0.2 – Expanded data collection is in development, targetting Q3 2019 for release


WebSocket API

A local, password-protected websocket interface is available for client application to authorize with via the user to query and modify the knowledge graph. API documentation for valid endpoints can be made available at request.


Getting set up

To install Cyte, simply unzip your download, move to your applications folder, and open

You’ll see a confirmation dialog informing you that your knowledge graph and language models will be set up, and to wait a few minutes for Cyte to start up.