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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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DifferentialEquations.jl
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
I've done some programming in kotlin and really liked this idea of decoupled language frontend and backend. I wish Julia had a compiled and interactive backends instead of JIT.
The latency has improved with recent releases, but interestingly there aren’t any compiler improvements mentioned in NEWS.md. So it might be my faith in Julia making me feel it’s faster :-)
It's not faith, and it's not all from Julia itself. https://github.com/SciML/DifferentialEquations.jl/issues/785 should reduce compile times of what OP mentioned for example.