[tannhaeuser]

Ok but is the argument now just because it's by Google with its media presence it's not niche? "Constraint solving" is an overly broad term that can encompass most of computer science but at least finite domain solving, interval propagation, and SAT solving as specific algorithmic approaches with very different applications. Going by your post, potential users looking for planning, scheduling, or optimization problems to solve will jump to very specific implementation techniques having won synthetic benchmarks in basically unrelated domains for showcasing "something with constraints". Classic OR is about optimizing systems of linear (in-)equations, but discrete optimization problems and most financial investment planning problems don't come in this flavor and really need very laborious encodings (like into equational systems with 1000s of artificial variables) to fit OR model formulation requirements or SAT checkers. Great, now you're prematurely solving idiosyncratic representation problems; your consultants surely will rub their hands. But I stand by my opinion that Prolog is, by far, a much better starting point for the kind of explorative programming required in this domain. Making Prolog fast/scaling on mainstream cloud hardware (like Quantum Prolog and SICStus are doing) has very much to offer to users, and is behind many or even most real-world scheduling and optimization applications.