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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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Arraymancer
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
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While indeed we are less people developing stuff in Nim compared to even the Julia community (which itself is of course much smaller than say Python), we do have cover a large amount of the typical needs in the scientific computing domain. And where we miss stuff it's a) easy to wrap C/C++ or b) simply call Julia, R or Python (As a personal reference I'm doing data analysis & numerical physics stuff in context of my PhD in physics and I literally do everything in Nim. The only significant C dependency {and only as a shared lib} I depend on is libhdf5 via nimhdf5).
While indeed we are less people developing stuff in Nim compared to even the Julia community (which itself is of course much smaller than say Python), we do have cover a large amount of the typical needs in the scientific computing domain. And where we miss stuff it's a) easy to wrap C/C++ or b) simply call Julia, R or Python (As a personal reference I'm doing data analysis & numerical physics stuff in context of my PhD in physics and I literally do everything in Nim. The only significant C dependency {and only as a shared lib} I depend on is libhdf5 via nimhdf5).
While indeed we are less people developing stuff in Nim compared to even the Julia community (which itself is of course much smaller than say Python), we do have cover a large amount of the typical needs in the scientific computing domain. And where we miss stuff it's a) easy to wrap C/C++ or b) simply call Julia, R or Python (As a personal reference I'm doing data analysis & numerical physics stuff in context of my PhD in physics and I literally do everything in Nim. The only significant C dependency {and only as a shared lib} I depend on is libhdf5 via nimhdf5).
While indeed we are less people developing stuff in Nim compared to even the Julia community (which itself is of course much smaller than say Python), we do have cover a large amount of the typical needs in the scientific computing domain. And where we miss stuff it's a) easy to wrap C/C++ or b) simply call Julia, R or Python (As a personal reference I'm doing data analysis & numerical physics stuff in context of my PhD in physics and I literally do everything in Nim. The only significant C dependency {and only as a shared lib} I depend on is libhdf5 via nimhdf5).
In particular for deep learning as bobsyourunkl already mentioned there is arraymancer on the one hand and also flambeau on the other. The latter is a Nim wrapper around libtorch (i.e. the PyTorch C++ backend). It is missing things (to be wrapped by adding a few lines) and has some rough edges, but if one needs to get stuff done, it's possible.
In particular for deep learning as bobsyourunkl already mentioned there is arraymancer on the one hand and also flambeau on the other. The latter is a Nim wrapper around libtorch (i.e. the PyTorch C++ backend). It is missing things (to be wrapped by adding a few lines) and has some rough edges, but if one needs to get stuff done, it's possible.