I remember reading about this 4 years ago as the new Chris Lattner project and was super excited, though a little skeptical.

I think that nowadays with vibe/agentic coding, high performance Python-like languages become ever more important. Directly using AI agents to code, say, C++, is painful as the verbose nature of the language often causes the context window to explode.

When I first heard about Mojo I somehow got the impression that they intended to make it compatible with existing Python code. But it seems like they are very far away from that for the foreseeable future. I guess you can call back and forth between Python and Mojo but Mojo itself can't run existing Python code.

Right now majority of beginners start programming with a high-level language, say Python or JavaScript - then for more advanced system-level tasks pickup C/C++/Rust/Zig etc.

If Mojo succeeds, it could be the one language spanning across those levels, while simplifying heterogeneous hardware programming.

Very bold of them expecting people to use a language with a closed source compiler in the 2020s.

I was excited when Mojo launched and thought it might grow big quick. I don't see much traction. The pitch is compelling. What could be the issue?

As someone in ML who's interested in performance, I'm keen for Mojo to succeed - especially the prospect of mixing GPU and CPU code in the same language. But I do wonder if the changes they're making will dissuade Python devs. The last time I booted it up, I tried to do some basic string manipulation just to test stuff out, but spent an hour puzzling out why `var x = 'hello'; print(x[3])` didn't work, and neither did `len(x)` (turns out they'd opted for more specific byte-vs-codepoint representations, but the docs contradicted the actual implementation).

Hopefully they get Mojo to a good place for more general ML, but at the moment it still feels quite limited - they've actually deprecated some of the nice builtins they had for Tensors etc... For now I'll stick with JAX and check in periodically, fingers crossed.

Am I old or remembering this wrong... didn't Zuck write the first iteration of Facebook in PHP, and then spend millions to hire people to write something that converted the code to C++?

>No more choosing between productivity and performance - Mojo gives you both.

That's a very big claim.

I am actually on a lookout for a low level language which compiles to web assembly to write a (relatively small) supervised learning model which I plan to be good enough for 5 year old phone CPUs. I have a working prototype in Julia and was planning on (eventually) rewrite it in Rust mostly for the web assembly target. I come from a high level language background so the thought of rewriting in rust is a little daunting. So I was excited to learn about Mojo and find out if they had a WebAssembly target in their compiler.

But then I read this:

> AI native

> Mojo is built from the ground up to deliver the best performance on the diverse hardware that powers modern AI systems. As a compiled, statically-typed language, it's also ideal for agentic programming.

Well, no thank you. I know the irony here but I want nothing to do with a language made for robots.

Sadly for them, Nvidia didn't stay still in the meantime and created the next generation of CUDA, CuTile for Python and soon for C++, through CUDA Tile IR (using a similar compiler stack based on MLIR).

Event though it's not portable, it will likely have far greater usage than Mojo just by being heavely promoted by Nvidia, integrated in dev tools and working alongside existing CUDA code.

Tile IR was more likely a response to the threat of Triton rather than Mojo, at least from the pov of how easy is to write a decently performing LLM kernel.

I do wonder if Mojo was a great idea just a little too late to the party. Porting ‘prototypes’ from Python to lower level languages is fairly trivial now with LLMs.

Does it have the indentation thing? That would be a no go for a lot of people