Moar links! (caveat: I am involved in many things) 1. You can implement some minor flavor of Doop in differential dataflow (https://github.com/TimelyDataflow/

Moar links! (caveat: I am involved in many things) 1. You can implement some minor flavor of Doop in differential dataflow (https://github.com/TimelyDataflow/differential-dataflow/tree/master/doop https://github.com/TimelyDataflow/differential-dataflow/tree...). The fragment came from Yannis which he described as a minimal non-trivial analysis (vs other, even smaller fragments), but it still uses some 900 dataflow operators (updates in 10s of ms when inputs change, though). The DD impl isn't meant to be understood, I'm afraid. 2. Many ex-LB folks are now at relational.ai doing .. advanced weird tech like they did with LB. I'd watch them (I do). 3. Polonius (https://github.com/rust-lang/polonius https://github.com/rust-lang/polonius) defines the Rust borrow checker as Datalog rules, and went from differential dataflow to datafrog (https://github.com/rust-lang/datafrog https://github.com/rust-lang/datafrog). 4. If you want to check out other incremental Datalog environments, in addition to IncA, there are https://github.com/comnik/declarative-dataflow https://github.com/comnik/declarative-dataflow https://github.com/ryzhyk/differential-datalog https://github.com/ryzhyk/differential-datalog https://github.com/TimelyDataflow/differential-dataflow https://github.com/TimelyDataflow/differential-dataflow Good times for Datalog, imo.