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If your project is unlike your normal work it can make sense to use something new.

I was doing some niche amateur cryptography. I started with numpy, but I needed high performance and support for custom fields (e.g. cyclotomic polynomial rings) so I switched to Julia.

The learning curve was worth the expressiveness and conciseness of the new language. It's important that your code matches the abstraction of your problem domain.

This is always the right answer.

Is it, though? Side projects can be a fantastic opportunity to try something new. I wouldn't have any of my current go-to stack without such experimentation.

Good point - so if the goal of the side project is to learn a new skillset then this is the wrong answer, but if the goal is to build an MVP then it is right - use whatever gets you there quicker.

the idea was to get inputs about what you guys do. Many times you feel influenced by your environment and there are probably better options you can work with. But thank you for your honest answer

This is the answer.