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Comment by rdedev | Hacker News Reader

I really don't understand why they do this. I was really excited for the compilation and type checking features but this whole speedup thing is pretty dumb. And I know the people developing mojo knows that too. Kind of seems like some marketing team is pushing them to do all this but their core customers are people who knows what python and numpy is and this kind of talk puts just feels weird

I think that mojo in it's current form is not on par with numpy performance (if it was they would be saying that). Even if the performance is same I would still give it a try. Their whole marketing though is making me reconsider

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They do this because they want to showcase writing code in single language that can do things that other languages can't. Other langauges are either low level or slow, but they are high level and fast.

Naive question, but how do they distinguish themselves from Julia, which is also in that space?

There is large overlap with julia, yes. Both are addressing two-language problem.

Mojo is python syntax first where they want to be proper superset, which gives them access to wide python ecosystem and community. If executed well, this alone can absorb community similarly to how ie. typescript absorbed javascript community. Also similar thing happened with objective-c -> swift - also led by Chris, which gives a lot of credibility to the whole initiative.

Julia is proper new language you need to learn, use new tooling around it, ecosystem is quite academia skewed, ie. writing web services is probably not the best idea etc.

Additionally Julia suffers from "time to first plot" problem, which alienates a lot of newcomers who are not familiar or simply don't want to switch to programming mode where it becomes less of a problem (repl/notebook style where runtime is always active).

Both are very interesting languages, but mojo's starting point and trajectory seem to be at different level, ie. adoption may be very sharp.

Julia can cache JIT code between executions nowadays.

Julia is a language that was developed by academics, with not the best aesthetics in the implementation. I see a lot of value in starting with a clean, well engineered layered approach. If anything this is something I would expect Lattner to be able to deliver, whereas Julia while certainly powerful is tremendously messy.

Python was also developed by academics.

Primarily by compatibility with Python, which is the de facto standard in deep learning.

I think there's a Julia forum thread where they are casually benchmarking comparisons between the two. Not sure if it is still active though.

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