fix doc links

Author: ChrisRackauckas

docs/src/basics/FAQ.md

@@ -26,7 +26,7 @@ the performance to the expected state, please open an issue and we will improve
 This is addressed in the [JuliaCon 2022 video](https://www.youtube.com/watch?v=JWI34_w-yYw&t=182s). This happens in
 a few ways:
 
- 1. The Fortran/C code that NumPy/SciPy uses is actually slow. It's [OpenBLAS](https://github.com/xianyi/OpenBLAS),
+ 1. The Fortran/C code that NumPy/SciPy uses is actually slow. It's [OpenBLAS](https://github.com/OpenMathLib/OpenBLAS),
     a library developed in part by the Julia Lab back in 2012 as a fast open source BLAS implementation. Many
     open source environments now use this build, including many R distributions. However, the Julia Lab has greatly
     improved its ability to generate optimized SIMD in platform-specific ways. This, and improved multithreading support

docs/src/index.md

@@ -23,7 +23,7 @@ Pkg.add("LinearSolve")
 ## Contributing
 
   - Please refer to the
-    [SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://github.com/SciML/ColPrac/blob/master/README.md)
+    [SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://github.com/SciML/ColPrac)
     for guidance on PRs, issues, and other matters relating to contributing to SciML.
 
   - See the [SciML Style Guide](https://github.com/SciML/SciMLStyle) for common coding practices and other style decisions.