# Motivation from legendary Julia people

**URL:** <https://discourse.julialang.org/t/motivation-from-legendary-julia-people/117496>\
**Category:** Offtopic\
**Created:** [July 26, 2024, 1:07am UTC](https://discourse.julialang.org/t/motivation-from-legendary-julia-people/117496 "2024-07-26T01:07:48Z")\
**Posts on this page:** 1\
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**Author:** ![Tarny\_GG\_Channie](https://avatars.discourse-cdn.com/v4/letter/t/3bc359/32.png) [@Tarny\_GG\_Channie](https://discourse.julialang.org/u/Tarny_GG_Channie)\
**Post date:** [July 26, 2024, 1:26am UTC](https://discourse.julialang.org/t/motivation-from-legendary-julia-people/117496/2 "2024-07-26T01:26:22Z")

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Oh, another thing is that comparing mindset for me coming to Julia, for me, it’s like… “Oh nice! This problem is easy now! I’m done!”

> [@Noise with gradient works!](https://discourse.julialang.org/t/noise-with-gradient-works/114178):
>
> In this video, it was explained why the gradient of noise was needed. [[Better Mountain Generators That Aren't Perlin Noise or Erosion] ](https://www.youtube.com/watch?v=gsJHzBTPG0Y) In most programming languages, you’re talking about either writing your own noise to add the gradient feature or diving deep into the library to make it work. Not here! using CoherentNoise using ForwardDiff using StaticArrays sampler = opensimplex2\_2d(seed=1) function sample\_gradient\_forwarddiff(sampler,x,y) arr = @SVector [x,y] f = a…

Whereas others might be like “Let’s tackle more challenging problems we couldn’t have tackled before with it.”

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