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Comment by aeonflux | Hacker News Reader

Does this project have any meaningful traction? Seemed like a cool idea, but I wasn't even sure what problem does it solves. Looks like its completely missing from the regular discussion / news outlets. Every 1-2 years some barely visible post/article reminds me it even exists...

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I found that a big issue with it in practice is that the numbers are just not great, and the repo is very vibe-coded. When the numbers are great, it's sometimes cheating with the setup. For example some GEMMs only compute each 8th value and reach close to peak TFLOPs because of this, but it's a vibe-coded reward hack and the verifier does not check all numbers of the target.

Also, compilers have a lot to prove in 2026. Why not just hill-climb a Triton or Cuda kernel if you need perf?

> But I wasn't even sure what problem does it solves. Looks like its completely missing from the regular discussion / news outlets. Every 1-2 years some barely visible post/article reminds me it even exists...

At this point "developers" these days sound a lot more like consumers than those who actually do research on a tool that solves a problem. Ocaml is barely mentioned in the news and rarely HNers here use that language, but it is Jane Street that maintains and uses it.

Judging by hype isn't a great way of evaluating a language. I am not going to check the entire Nvidia stack, from CUDA, to CUTLASS to cuDNN and even on PyTorch's side just to solve a runtime error that could have originated from either place when Mojo solves all of that.

> At this point "developers" these days sound a lot more like consumers than those who actually do research on a tool that solves a problem.

No need for snarky comments. These days tools/frameworks/languages/libraries/etc are popping all the time. Do you expect people to research every simple signal they catch in the wild?

How noob do you have to be to really feel this way lol

Edit: a language is by definition an ecosystem. Learning/using a language that no one else uses is completely pointless.

The interesting opportunity in 2026 for languages is that an LLM doesn't complain if there's an ecosystem or whether any other LLMs or people are using a language, they'll just write the code in whatever language you tell it to. Already authors are writing languages with features aimed at making it more more attractive for AI to write than people. Also great for working with research languages. I just used AI to write a Halide program and I wouldn't have bothered to do so before, for the reasons you state.

Jane Street's use of OCamel gives them a comparative advantage in hiring. Many devs put a premium on the tools they use. Same story with Anduril/Mercury using Haskell. You can run a successful prop trading firm/weapons design shop/startup-banking(?) platform without these things. They differentiate you from your competitors by being cool, not by being the best technical solution. If anything they were otherwise unprove at scale and out of pure engineering consideration a gamble. Developers are consumers.

I think the compiler not being open-source hurt with getting people to check it out. Today, people are accustomed to most of it regarding PL development being out in the open, and Mojo was an outlier here.

the main value proposition of mojo is being a (modern language for heterogenous compute). when writing code for the CPU, it is a nice language. it looks a bit like python, have arguably one of the best SIMD abstraction, a bit easier than Rust .. etc. but in my opinion, that does not justify deep investment in learning the language vs rust or zig. what sets Mojo apart is that it was designed from the grounds up to natively supprt other types of hardware (starting with GPUs). I have been in the mojo community from the beginning and almost every other project presentation starts with (I wanted to do XYZ in my domain and I could match or get close to Rust, C++ perf but then I wanted to see if I can make work on the GPU and it was much easier than expected and now I can solve my problem 10x faster). this is massive advantage that is underutilized outside of the AI/LLM and few other niches. running natively and being portable to NVIDIA/AMD/Apple GPUs means that you can transform any arbitrary workload you have in mind to the GPU and you won't need to worry about the separate CUDA/ROCm/Metal stacks, slightly different programming models, building headaches. you will be using the same language and the same compiler for CPUs and GPUs (all of them). or you won't need to coerce your problem in the shape of Triton to make it portable. If you have a problem in mind and don't like what you see? write your own GPU abstractions, containers, data moving and pipelining logic, work partitioning .. etc. this is extremely powerful and it sets Mojo apart from any other mainstream systems language that I have seen. that's why bigger mojo projects (even pre 1.0) like NuMojo and Marrow are all dabbling with GPU support even before the first stable release.

and now with support for TPUs and Trainium chips it is becoming even better. some problems will work better on some architectures and you will be able to split your problem to the best hardware doing some work on the CPU and some work on the accelerator(s) of choice, all with a modern language and one compiler with good tooling. that seems to be a good enough value proposition for me.

The "problem" it might solve for me, personally, is having a systems-level language that matches my personal style a bit better than Rust or other more modern systems level languages.

I know they're putting a lot of weight on GPU programming, which is fine and probably solves problems, but this is the part that would motivate me personally.

I'm in the same category, but GPU support is definitely interesting too. The idea of offloading intensive processing to the GPU has its merits.

You can read a lot more about it here: https://mojolang.org/docs/vision/

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