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Comment by ayhanfuat | Hacker News Reader

It seems quite exciting and all but I cannot figure out why they choose to impress people with marketing posts like "it is a billion times faster, oh no it is actually a zillion times faster" while their actual potential user base is waiting for license issues to be clarified.

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What’s unclear about it? It’s a closed source product by a for profit company

It would be nice if it were open source, but I don’t see anything that’s unclear about the IP.

Mojo team is avoiding this topic like a plague. I recently listened to whole Lex Fridman episode with Mojo's founder Chris Lattner [1] and I don't think they said a word about licensing. I still don't get it tbh.

I don't even understand how can Mojo be compared to Python without reflecting on Python Software Foundation which is arguably the main reason Python is such a wild success. We should do more PSF not less.

1 - https://lexfridman.com/chris-lattner-3

How license will evolve is unclear. I personally don't mind it starting closed to figure out a contribution model or whatever they want to sort out as long as they have clear commitment. However, the current statement for open source is so vague that I don't want to invest time on it.

I think not investing time in Mojo is the currently the correct decision. If Modular decides they have a good product and would like broad community adoption, they will make the open source and licensing decisions that would support that. They're smart and I presume aware of that dynamic, so honestly I suspect they don't want a broad community just yet.

Community management and language feature haggling is not worth the expense at an early for-profit company with an alpha language that needs to solve internal problems.

Isn’t the whole point that you don’t invest time in it but just switch over your python project and then optimize it in a ship of theseus manner?

Good. It's an early alpha product. You shouldn't invest time into it. If their current licensing model prevents people from using it from anything serious, that sounds like a win.

It's a programming language. Just playing around to see what it is capable of means serious time investment. I wasn't planning on using it on production. Anyway, that was a feedback for the creators. I'll check back if they change the license.

Yeah I believe the actual quoted figure was "35,000 times faster than Python". It's a silly metric either way.

I have never taken them seriously precisely because of this statement. They are a machine learning specific language and everyone who works in machine learning knows the speedup is cherry picked bullshit. If they're basing their marketing around an obviously false claim, why would I take a closer look?

The claim is likely not false just like a horse pulling a buggy is several orders of magnitude faster than a snail.

They have recently published a blog series which started with 35,000x and ended with 68,000x [1]. The thing is anyone familiar with performance sensitive code is not looking at Python vs the other thing. They are looking for whether or not it is in the ballpark of C, Julia, Cython, Numba, etc. Those are the contenders, not Python.

[1] https://www.modular.com/blog/mojo-a-journey-to-68-000x-speed...

I think the reason that they focus on pure Python is because a big use case would be pure Python code bases that can be incrementally ported over to Mojo for better performance, without needing to totally abandon the Python language and ecosystem.

If you read the description, they started with 35,000x, then tripled the number of cores to get 68,000x. If your triple the cores and wind up with less than twice the performance, your scaling isn't very good.

It is very good, especially if you don't have to rewrite your code.

Try to do it yourself (say take a merge sort implementation and try to make it scale), it'll be roughly this much after a few hours of work.

Now imagine the compiler is doing this for you.

Keyword is imagine, because with Mojo you do have to write your code in a new language. You cannot use the Python parts if you want speed. That's writing code in a new language.

That's the stated goal, it's not mature technology.

You can have your python parts source compatible with the new language.

Of course they are far off from that.

But not that far off if your python code is typical ML framework consumer code.

Depends on the task not all tasks scales linearly

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