CellNet V5.0


CellNet is an experiment to try to achieve a faster, more efficient way to handle and organize a large amount of files.

How it works: Program creates a link or shortcut to every file stored within the program. The links are stored within cells. Let's say you added an image. The image would be linked automatically. A unique identity would then be assigned to the image and it would be stored in a special folder. By clicking the link, you can now view the image. Copying or moving the image requires only to move the link, the image will always remain in place. What are the benefits of this? It is much more efficient to use links instead of files when you have thousands of files/images to organize. 

The program allows you to organize terabytes of data/files/videos/images/documents/audio/URLs/shortcuts and more and move them instantly. The maximum number of cells is 100,000, and each cell can hold 100,000 links. There is a maximum 10,000,000,000 storage space available for any file on your computer, including program shortcuts and URLs. The program also includes a video player and an audio player and plenty of a.i. stuff.

Source code is available. The program can be modified in any way you like. The program has been tested for over two years and is fully functional. Fork back the changes so they can be added to the project. The source code was written with C++ on Qt Creator 4.14.0 based on Qt 5.15.2 (MSVC 2019, 64 bit). 


CellNet main window

CellNet main menu

Thumnail image editor

Cell editor

Playlist menus


How it works:

When you click on the "Ask CellNet" button, the microphone voice recorder will start recording for a few seconds. Speech to text translation is then performed in the cloud using the recording. As soon as a reply is received, the text is checked with the speech cells in CellNet for commands or responses. In the absence of a match, the text is checked with the learning cells. The vision cells are then checked based on the context of the text. After checking for errors, the text is sent to ChatGPT-3. ChatGPT-3 response text is sent to the cloud for TTS text to speech translation. Learning cells receive the original text and response, and output speakers receive the response-mp3. It takes only a few seconds to complete the process.




Speech action menu


Face recognition training database

How it works:

The face recognition training database will give CellNet the ability to recognize human faces. Three or four images can be added using the webcam or any still image that contains one or more humnan faces. Each face added should have a slightly different angle as well contrast and brightness can be adjusted and a face name added before going to the main database for training and reconstructing. After a few seconds training, the confidence score can be checked. A low score may mean that a couple more images need to be added. A face name should be identical for each face added. The trained vision cells maybe accessed by other parts of CellNet when requied to recognize a face.


Vision settings

Object recognition and character recognition

How it works:

Common objects and animals can be detected with any webcam connected. Questions can asked about what is seen with the speech action menu along with the main speech settings and a microphone. The webcam can be setup to record a short video clip on object detection, furthmore an audio message describing the object will be sent to the speakers. Perhaps an ideal security cam. Character recognition is possible by holding up an image containing text and asking a particular question. The system will reply with what it reads in the image. Character recognition is also available in the edit menu for saved images.


Object detection

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