AI tools are now the horsepower of computer programming. They are generally great for writing glue-code and integration tasks, probably less than ideal for complex problems on complicated programming settings. What could be the reasons that prevent generators on full-scale adoption?

I am implementing a declarative programming language that facilitates the synergy between automatons and humans on software development by forcing AI tools to generate intuitive code and to allow human operators to understand what is in there. It is an attempt to lower the barriers by simplifying the programming experience.

In the presentation, I will touch various aspects that includes cognitive aspects of problem-solving applied to programming and the role of AI tools such as LRMs on software development. I will provide arguments for stating that the accidental complexity of the programming system may worsening the performance of AI generators as well as it affects human developers.

Started as a data programming language for pairing database queries with programming controls, Kubrick may evolve in an advance integrated programming environment with the footprint of a Jupiter notebook and the immediacy of a spreadsheet. It lets agents to focus on what they want to achieve making easy things easily without expressivity losses.

To mitigate the lack of solution productivity in generators - the way that an optimizer generates deriving combinations of solutions from a set of given axioms - I extended the language for helping agents to cover combinatorial problems. Think of answer set programming (ASP) but with an eye on integration and usability.

Research Engineer