ddt VS Nullboard

DDT allows building custom decision trees based in a set of defined rules, programmatically or from json.

When making a decision, it allows adding a pre-processing stage to the input before comparing it with the following possible branches of the tree.

https://github.com/sgrodriguez/ddt

https://github.com/sgrodriguez/ddt a decision tree lib in golang for building custom rules decision tree. It allows adding a pre-processing stage to the input before comparing it with the following possible branches of the tree. One of the default preprocessing stages in ddt is calling a method of a struct (CallStructMethod) and getting the attribute of a struct (GetStructAttribute) using reflection, so you can have a struct user with some methods and attributes and build a tree asking question about the user.

Hello, HN! I'd like to introduce you to nbagent[0], an open-source alternative backup agent for Nullboard[1] for Unix systems. If you, like me, are a fan of Nullboard's simplicity and you've been looking for an easy way to have your data backed up, check it out.

I'd love to hear your feedback on nbagent. Please feel free to leave comments, suggestions, or questions below.

[0] https://github.com/luismedel/nbagent

[1] https://nullboard.io/preview

Surprisingly no one has mentioned nullboard (https://nullboard.io/preview) yet. Just a single file JSON to carry around the entire board.

Nullboard maybe?

minimalist kanban board, without fancy tagging/time tracking/... features. Every game has a column, and notes are player names. nullboard would be perfect if it wasn't local only;

Nullboard - cool simple project, no bug tracking

Nullboard - Kanban board with dark mode, localStorage, and thoughtful UI

I use and like https://nullboard.io/preview nullboard which runs in the browser. Because it uses local data, you need to save it or the data is lost when you close the browser window.