A faster, leaner JavaScript for scientific computing: what features should I keep?
If you want to use JavaScript for scientific computing, why not try Node.js? Node is based on Google's V8 engine with some additional functionality for interacting with the file system, writing servers and linking to C and C++ libraries (this is definitely not an exhaustive list). It sports fast execution times and provides an interactive console as well as a way to execute your programs from the command line. I found @DaveClarke's comment about Python acting as "glue code" interesting because of the wording. Here is an excerpt from the Node documentation on addons:


  Addons are dynamically linked shared objects. They can provide glue to
  C and C++ libraries.


Node also has an excellent package manager called NPM, which would make it easy to share scientific modules which you have written. The NPM repository already contains some scientific modules, such as "natural" (for natural language processing) or "clusterfck" (a clustering library). There are most definitely many other libraries out there for a variety of applications.

The great thing about this solution is that you wouldn't have to modify JavaScript or create a new language. The syntax is essentially the same, but your code does not need a browser to execute. If you plan to follow up with node, a good first step might be to start creating addons for existing open-source scientific libraries written in C and C++, or you could port them to JavaScript. I was actually thinking about using node for scientific programming today which is what led me to this question. It would be great to see more robust tools like you find in Python.

I initially had links to natural and clusterfck, but I do not have enough rep to post them. Luckily, they come up as the first results if you google "clusterfck node" and "natural node".