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@@ -53,7 +53,7 @@ Julia compiles to machine code that is routinely as fast as C/FORTRAN due to its
 
 Julia has software organizations for seemingly every scientific area. From high energy physics to economics, it is likely that there is a _developer_ (not just user) community around the field you work on already, with native Julia software that match up to alternatives in other languages. Moreover, these organizations already contain some of the best-in-class software, that is, the most featureful, most performant, and most accessible tool for a particular task. Examples are [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl), [DynamicalSystems.jl](https://github.com/JuliaDynamics/DynamicalSystems.jl), [Turing.jl](https://github.com/TuringLang/Turing.jl), [NeuralPDEs.jl](https://github.com/SciML/NeuralPDE.jl), [Distributions.jl](https://github.com/JuliaStats/Distributions.jl), [Makie.jl](https://github.com/MakieOrg/Makie.jl), [JuMP.jl](https://github.com/jump-dev/JuMP.jl), and many more. Besides, even if there is some tool that you need that is not available in Julia, that is not a problem due to its composability with other languages (see below).
 
-_We should point out how surprising this is. Julia is a more recent programming language, has much less users, and has received much less large scale funding, when compared to Python, the currently most popular programming language for scientist. The fact that Julia matches Python's library ecosystem, and in many fields it surpasses it, is a testament to 1) how easy it is to develop, or contribute to, software in Julia, and 2) the amount of software engineering talent that Julia has attracted._
+_We should point out how surprising this is. Julia is a more recent programming language, has much less users, and has received much less large scale funding, when compared to Python, the currently most popular programming language for scientists. The fact that Julia matches Python's library ecosystem, and in many fields it surpasses it, is a testament to 1) how easy it is to develop, or contribute to, software in Julia, and 2) the amount of software engineering talent that Julia has attracted._
 
 ### Extensibility/Composability