The idea is to explore the technologies and the various components to realize some AI to predict pitfalls in source code which can potentially generate run-time misbehaviours.

I'd like a way to have a device on my desk which lights up to indicate that I have something I should be paying attention to. Initially, I'd like this to be for Office365 calendar events and GitHub mentions, but ideally it should support arbitrary messages. The plan is to assign specific colors (ideally "patterns" consisting of a sequence of colors and time) to specific message types.

I'd like to add a 433MHz receiver transmitter module to my raspberry pi - I know there are some kits, but this will require some hardware poking and soldering and I'm looking forward to it. The goal will be to receive the signal of some remote temperature and humidity sensors, decode the signal - understand their data structure and display and log them accordingly.

Needing to learn git, I thought it would be a good time to clean up the code and create a git project for it.

I freshly bought a small and cheap laptop ASUS 200H based on Cherrytrail. My plan is to install openSUSE on it, and bring it along with my vacation in the following week :)

Mozilla's DeepSpeech project[1] is using TensorFlow and some paper from Baidu to make an open source speech to text system, based on deep learning (TensorFlow). The current project allow the training for own local datasets, but also there is a pre-trained model that can be used during the development.

There are various email synchronization programs like offlineimap or mbsync, which have some inherent limitations: