Mojo 1.0 Beta

by sbt567 • 3 months ago

375 246 3 months ago

pjmlp24• 3 months ago

Still phase one, doesn't do native Windows.

Meanwhile Julia is more mature for the same purposes, and since last year NVidia is having feature parity between Python and C++ tooling on CUDA.

Python cuTile JIT compiler allows writing CUDA kernels in straight Python.

AMD and Intel are following up with similar approaches.

So will Mojo still arrive on time to gain wider adoption?

Time will tell.

dllu• 3 months ago

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.

boxed10• 3 months ago

Not to mention that C++ basically can't be made to be safe. But Rust is probably fine.

pjmlp24• 3 months ago

In theory that is the idea behind profiles, in practice it remains to be seen what will they deliver until C++29, and if matters by then.

Microsoft is invested into using AI for C++ code review, for example.

modeless• 3 months ago

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.

haskman3• 3 months ago

Really the only thing good about Python is its ecosystem.

mastermage2• 3 months ago

but that ecosystem is realy good.

coldtea2• 3 months ago

Nah, it's also a very fine language for getting an idea down quickly.

Might not have the niceties purists like, but perhaps that's exactly it's a great language for that.

It's like executable pseudocode, and unlike other languages, all the ceremony is optional.

People flocked to it way before it became a "must" for ML and CS thanks to that ecosystem becoming dominant.

ainch3• 3 months ago

In their original pitch that was definitely part of it: take Python code, add type hints, get a big speedup. As they've built it out it seems to have diverged.

melodyogonna15• 3 months ago

It was always going to be a long-term thing, if it were even possible. You can't make a compiler that can compile Python into efficient machine code in just a year (which was how long Mojo had been in development when it was announced).

The messaging was changed because people got sold too hard on that, and kept trying Mojo with the expectation that it could compile existing Python code when it couldn't. What Modular did was change the messaging to reflect what Mojo is today, and provide a roadmap[1] of what they hope it'll turn into in the future. As it evolves, the messaging will evolve with it to continue reflecting current capabilities.

1. https://mojolang.org/docs/roadmap/

samuell4• 3 months ago

The communication had me try to run some very simple python code assuming it of course should run (reading files line by line), which didn't work at all.

For me this was a big disappointment, and I wonder how much this has backfired across developers.

mastermage2• 3 months ago

That was what was originaly advertised, they wanted to be what Kotlin is to Java but for Python. They quickly turned tails on this.

That and the not completely open source development model is what has always felt very vaporwary to me.

victorio• 3 months ago

From the site:

Python interop > Mojo natively interoperates with Python so you can eliminate performance bottlenecks in existing code without rewriting everything. You can start with one function, and scale up as needed to move performance-critical code into Mojo. Your Mojo code imports naturally into Python and packages together for distribution. Likewise, you can import libraries from the Python ecosystem into your Mojo code.

kjsingh• 3 months ago

isn't that achieved by Codon?

dtj11239• 3 months ago

They also advertised a 36,000x speedup over equivalent Python if I remember correctly, without at any point clarifying that this could only be true in extreme edge cases. Feels more like a pump-dump cryptography scheme than an honest attempt to improve the Python ecosystem.

boxed10• 3 months ago

Well... the article made self deprecating fun of the click bait title, showed the code every step of the way, and actually did achieve the claim (albeit with wall clock time, not CPU/GPU time).

And it wasn't "equivalent python", whatever that means, they did loop unrolling and SIMD and stuff. That can't be done in pure python at all, so there literally is no equivalent python.

jdiaz973• 3 months ago

The modern way to advertise: lie a lot.

dtj11239• 3 months ago

Crypto*

pansa2• 3 months ago

they intended to make it compatible with existing Python code

That was the original claim, but it was quietly removed from the website. (Did they fall for the common “Python is a simple language” misconception?).

Now they promise I can “write like Python”, but don’t even support fundamentals like classes (which are part of stage 3 of the roadmap, but they’re still working on stage 1).

Maybe Mojo will achieve all its goals, but so far has been over-promising and under-delivering - it’s starting to remind me of the V language.

Certhas7• 3 months ago

If you paid very close attention it was actually clear from the start that the idea was to build a next gen systems language, taking the lessons from Swift and Rust, targeting CPU/GPU/Heterogeneous targets, and building around MLIR. But then also building it with an eye towards eventually embedding/extending Python relatively easily. The Python framing almost certainly helped raise money.

Chris Lattner talked more about the relationship between MLIR and Mojo than Python and Mojo.

pjmlp24• 3 months ago

So basically Chapel, which is actually being used in HPC.

jdiaz973• 3 months ago

They just lie a lot, they make fake blogs with fake benchmarks and then they delete them

fwip• 3 months ago

That's because Mojo told you that. https://web.archive.org/web/20231221132631/https://docs.modu...

Our long-term goal is to make Mojo a superset of Python (that is, to make Mojo compatible with existing Python programs). Python programmers should be able to use Mojo immediately, and be able to access the huge ecosystem of Python packages that are available today.

simplyvibecode2• 3 months ago

Mojo has refocused on Python interoperability vs. superset, though yes, the original idea was being a superset.

It's possible the language evolves to that in the longterm, but it's not the short term goal.

We published a Mojo roadmap on Mojolang.org that helps contextualize this: https://mojolang.org/docs/roadmap/

Note: I work at Modular

0xpgm• 3 months ago

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.

logicchains• 3 months ago

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

walterlw• 3 months ago

from what I understand the goal for now is not to get the people to use it, but for enthusiasts to try it

kstrauser7• 3 months ago

What enthusiast worth getting feedback from is going to tinker with a locked up language?

ainch3• 3 months ago

They've said they'll open source the compiler alongside the 1.0 release.

evertheylen• 3 months ago

If you're looking for a language that aims to solve the "two-language problem" like Mojo, but want something more open, more mature and less influenced by VC funding, check out Julia: https://julialang.org/

runarberg5• 3 months ago

I used Julia a lot when I was studying statistics (which I dropped out of) back in 2015, but I recently (like last weekend) came back to it to write a prototype of a supervised learning model, and I have to say, coming back to it was pure joy. And my model prototype was indeed fast enough for me.

Now I will probably rewrite the model in rust if I want to do anything with it (mostly for the web assembly target as I want this thing to run in browsers) but I will for sure be using Julia for further experimentation. Lovely language.

rvz2• 3 months ago

CUDA is closed source (and will never be open sourced) and everyone is fine with it.

Modular is giving you at least a public promise that they will open source Mojo it this year but some how here it is a problem.

Unbelievable.

kstrauser7• 3 months ago

CUDA is not a programming language. It's a package with adapters for lots of languages. IMO, it's not relevant here at all. Sure proprietary packages exist for just about every language, but most modern languages are open source. You can use them, distribute them, patch them, or whatever else.

For instance, you can write Python without using CUDA. CUDA's existence doesn't make Python less useful. But what do you do when you bump into a bug in Mojo? You have no ability to fix it yourself. At best, you can report it to the authors and hope they care enough about it to put in the work and release an update. If you run into a Python problem, you, or someone in your org, or a paid consultant, can fix it even if the Python core team doesn't care about it.

redlewel2• 3 months ago

WOW. Thanks for the comment I don't need to waste my time. So many choices for programming languages why would I go with something that isn't fully open source?

insumanth• 3 months ago

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?

tweakimp2• 3 months ago

When it was announced it was not generally available for everyone to try out. There was a waitlist phase.

samuell4• 3 months ago

As someone who would have strong reasons to invest time in Modular (simple high performant language for implementing bioinformatics scripts), I would say primarily the worry that development might be too tied to Modular, the startup behind it, which eventually might pivot into other priorities.

One would want to see either a strong community build up around it, or really hard evidence for a long-term commitment to the language from Modular. And the latter will take a long time to be assured of I think.

Also, editing tools need to catch up before very wide adoption of a language with a lot of new syntax.

williamstein• 3 months ago

Mojo is still NOT open source (the standard library is but not the compiler). Open source is table stakes for a modern programming language.

kstrauser7• 3 months ago

I have no time for or interest in proprietary compilers. The standard library is Apache 2, but the license link on their home page is to a long terms of service thing. I’d like to be wrong because it looks interesting. Until then, this doesn’t exist in my world.

I bet that’s true for a great many people. There are too many wonderful FOSS languages to bother with one you can’t fix or adapt or share.

pjmlp24• 3 months ago

- Doesn't support Windows, which is what many companies give their employees, outside Silicon Valey like culture

- The MLIR approach, which was also designed by Chris Lattner while at Google, has proven quite valuable to create Python JIT DSL

- The Python ecosystem now being taken seriously by the main GPU vendors, thanks to MLIR, as all their proprietary compilers are based out of LLVM

- Others remember Swift for Tensorflow

xiphias2• 3 months ago

It's a language that wanted to make money from the start.

For example you need to use some Mojo Max library to have fast optimized LLM kernels, but at that point you can just stay with Python that has fast optimized LLM kernels, like flashinfer.

ainch3• 3 months ago

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.

coldtea2• 3 months ago

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.

Unless it's open sourced, it's a moot point, as most Python devs wont come anyway.

Certhas7• 3 months ago

This is a bit ironic, given that people seem to have no problem using CUDA all over the place... Plus they promise to open source with the 1.0 release. We'll see...

MohamedMabrouk6• 3 months ago

I think that plan is to open source the compiler with 1.0 which is expected to be this summer. so in ~3-4 months time.

ktm5j• 3 months ago

I'm really not sure that's true.. I can't think of a single Python dev I've worked with who cared about opensource. All they cared about is the language being easy and free to use.

flakiness• 3 months ago

https://mojolang.org/docs/roadmap/#contributing-to-mojo

We're committed to open-sourcing all of Mojo, but the language is still very young and we believe a tight-knit group of engineers with a common vision moves faster than a community-driven effort. So we will continue to plan and prioritize the Mojo roadmap within Modular until more of its internal architecture is fleshed out.

I hope they stick to their original promise. And the 1.0 release would be a great time to deliver this.

sureglymop• 3 months ago

Mojo is cool but I just don't understand the python backwards compat thing. They're holding themselves back with that.

All the flaws I can think of in Kotlin are due to the Java compatibility. They could've made it work here by being more explicit but the way it currently works seems doomed.

tasuki2• 3 months ago

They coulda made it Scala!

pjmlp24• 3 months ago

Same story with C and Objective-C, C and C++, JavaScript and TypeScript, Java and Scala, Java and Clojure,.....

Yes the underlying platform they based their compatibility on, is the reason they got some design flaws, some more than other.

However that compatibility is the reason they won wide adoption in first place.

geodel• 3 months ago

All the flaws I can think of in Kotlin are due to the Java compatibility.

All the use of Kotlin in industry are due to Java compatibility. Else there would be ~0% marketshare of Kotlin.

davidatbu• 3 months ago

I'm pretty sure that they have decided that backwards-compat is not the best path for Mojo. Matter of fact, the following is the _last_ item on the roadmap on the home page:

Supporting more of Python's dynamic features like classes, inheritance, and untyped variables to maximize compatibility with Python code.

What's more, note how it says "to maximize compatibility" not "to achieve full compatibility."

boxed10• 2 months ago

Mojo is cool but I just don't understand the python backwards compat thing. They're holding themselves back with that.

In reality I think they've dropped that pretty hard. Literally you can't even get the length of a string with `len(s)` in the latest release. They also removed negative indexing, which I find baffling and frustrating. The roadmap does say they don't intend to have any "syntax sugar" until later in the implementation, but negative indexing is such a core part of what makes Python so much nicer to work with compared to say C++...

digdugdirk• 3 months ago

It does almost seem like they're trying to recreate the Nim programming language in this regard.

rao-v9• 3 months ago

I still don’t understand why we lack a language that will take uncomplicated computation heavy code and turn it into SIMD / multi thread / multiprocessing / GPU code with minimal additional syntax.

Surely this is the sort of thing compiler / language design nerds dream about?

It doesn’t have to guarantee efficiency or provide cutting edge performance in any context … it should just exist!

My understanding is that we can make such a language … but it’s not caught the fancy of someone who could do it

JBits• 3 months ago

If you're happy with NumPy's API, then surely JAX is exactly what you're looking for.

oldmanhorton• 3 months ago

Both ahead of time compilers and JIT compilers often perform autovectorization of tight loops. The problem is that lots of hot loops are not necessarily simple loops, and in particular a lot of source code is written in a way which uses sequential dependencies that can’t be modeled in SIMD code. Aside from undefined behavior in C/C++, most compilers will fail to autovectorize because doing so would very slightly change the behavior of your code in a very hard to understand way.

teleforce• 2 months ago

I still don’t understand why we lack a language that will take uncomplicated computation heavy code and turn it into SIMD / multi thread / multiprocessing / GPU code with minimal additional syntax.

It's already (partly) existed called D language, by default it's garbage collected (GC), can also be program without it or hybrid. It's a modern, backward compatible with C and it's included in GCC.

The linear algebra system in D or Mir GLAS is standalone BLAS implementation written directly in D [1]. It's already proven faster than the other widely existing conventional BLAS like OpenBLAS back in 2016, about ten years ago!

This popular OpenBLAS include Fortran based LAPACK (yes you read it right Fortran) and it is being used by almost all data processing languages currently Matlab, Julia, Rust and also Mojo [2].

Interestingly there is a very early stage of standalone BLAS implementation written directly in Mojo namely mojoBLAS similar to Mir GLAS just started very recently [3].

Surely this is the sort of thing compiler / language design nerds dream about?

You can say this again.

Especially on the GC side of the programming language since this SIMD / multi thread / multiprocessing / GPU can be abstracted away.

Actually someone recently proposed VGC or virtualized garbage collector for Python in C++ for heteregenous GC [4],[5]. However, the current evaluation excludes JIT compilation, AOT optimization, SIMD acceleration, and GPU offloading.

[1] OpenBLAS:

https://en.wikipedia.org/wiki/OpenBLAS

[2] Numeric age for D: Mir GLAS is faster than OpenBLAS and Eigen:

http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/...

[3] mojoBLAS:

https://github.com/shivasankarka/mojoBLAS

[4] Virtual Garbage Collector (VGC): A Zone-Based Garbage Collection Architecture for Python's Parallel Runtime:

https://arxiv.org/abs/2512.23768

[5] VGC-for-arxiv:

https://github.com/Abdullahlab-n/VGC-for-arxiv

tosh• 2 months ago

Still a bit early but I'm working on kiwi, a k-dialect that can lower to Apple MLX.

Currently supports CPU and GPU on macOS and CPU on linux.

https://kiwilang.com

https://github.com/kiwi-array-lang/kiwi

Kiwi runs computations on small dense arrays in its own runtime, when they are larger it will lower to MLX CPU and eventually to MLX GPU when it is worth it.

As user you don't have to change any code, you just write k.

I'm sure there are other languages designed to take advantage of modern GPUs.

But even with SIMD you can get quite far with array oriented code and many array language implementations will make use of it (BQN, ngn/growler/k, goal, ktye k has a version with SIMD support, …)

mappu• 2 months ago

Intel Ispc is a compiler for a C superset language that targets CPU SIMD and GPUs.

QuadmasterXLII• 2 months ago

julia StaticCompiler.jl is the closest to doing this but its buggy, the error messages are terrible, theres no documentation and its ambiguous whether its still in development. but ive looked, nothing better exists.

noduerme• 3 months ago

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++?

dtj11239• 3 months ago

https://en.wikipedia.org/wiki/Hack_(programming_language)

CodeArtisan• 3 months ago

Hack came after. Noduerme is referring to https://en.wikipedia.org/wiki/HipHop_for_PHP

DeathArrow2• 3 months ago

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

That's a very big claim.

runarberg5• 3 months ago

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.

kstrauser7• 3 months ago

I’ve written Python for the past 25 years or so. I dig it. But I don’t think I’ve started a new Python project since starting to experiment with Rust. A lot (not all!, but a lot) of Rust patterns look a lot like Python if you squint at it just right. I also think that writing lots of Rust has made me better at writing Python. The things Rust won’t let you get away with are things you shouldn’t be doing almost anywhere else.

Go on, give it a shot. It stops being intimidating soon! And remember that the uv we all love was heavily influenced by Cargo.

frizlab• 3 months ago

If you’re searching for a language that has the same strong memory safety than rust but is a bit easier to write, you should give Swift a go.

runarberg5• 3 months ago

I actually have written Rust, but it has been a minute. I think my last project (a backend for a massive online multiplayer theremin jam session [site no longer up; but HN discussion still exists: https://news.ycombinator.com/item?id=10875211] 10 years ago).

I remember Rust very fondly in fact. And I had the same experience as you, learning Rust made me a better Javascript programmer. Lets see if a little neural network can be as fun.

pjmlp24• 3 months ago

I can't go get coffee so many times per day, there are better compiled languages to chose from, while offering Python like ergonomics.

Certhas7• 3 months ago

Mojo has been suffering in their communication from targeting VCs rather than users. They never actually had a clear "Mojo extends Python" MVP or even strategy to get to an MVP anytime soon. And the language started developing before AI Agents were a thing and has more to do with building around state of the art LLVM tooling than AI Agents. But I guess "easier lifetime semantics than Rust and native access to MLIR intrinsics" doesn't raise money...

FacelessJim• 3 months ago

Might want to take a look at WasmTarget.jl it’s rather new and experimental but could be what you’re looking for!

runarberg5• 2 months ago

Too much vibe code for my taste.

fibonacci112358• 3 months ago

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.

brcmthrowaway2• 3 months ago

Interesting, how big impact is CuTile?

melodyogonna15• 3 months ago

People keep mistaking Mojo as good syntax for writing GPU code, and so imagine Nvidia's Python frameworks already do that. But... would CuTile work on AMD GPUs and Apple Silicon? Whatever Nvidia does will still have vendor lock-in.

pjmlp24• 3 months ago

Indeed, but Intel and AMD are also upping their Python JIT game, and in the end Mojo code isn't portable anyway.

You always need to touch the hardware/platform APIs at some level, because even if the same code executes the same, the observed performance, or in the case of GPUs the numeric accuracy has visible side effects.

pjmlp24• 3 months ago

And for not staying behind, Intel and AMD are doing similar efforts, and then we have the whole CPython JIT finally happening after so many attempts.

Not to mention efforts like GraalPy and PyPy.

And all these efforts work today in Windows, which is quite relevant in companies where that is the assigned device to most employees, even if the servers run Linux distros.

I keep wondering if this isn't going to be another Swift for Tensorflow kind of outcome.

IshKebab3• 3 months ago

The CPython JIT has barely had any impact on its performance. CPython is always going to be dog slow.

Conscat2• 2 months ago

My understanding from speaking with a few Tile IR devs on dates is that its primary motivation was providing better portability for programming tensor cores than PTX offers. Nobody ever told me they saw it as a response to anything other than customer feedback.

chrismsimpson• 3 months ago

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.

msaelices2• 3 months ago

Modern LLMs are perfectly capable to learn the syntax of a new language on the fly and write great Code. E.g. there is an official Mojo syntax skill you can use and works well

thefounder2• 3 months ago

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

IceDane• 3 months ago

Only incredibly inexperienced people think indentation in python is a problem.

vga12• 3 months ago

I have tons of experience with python, possibly more actual work experience than any other language, and I do think the indentation is a bit of a problem. Obviously not a huge one, but still something I wished they had done differently. Because I like to have a robust format-on-save wired into my editor, and you just cannot quite have that when indentation is meaningful.

tasuki2• 3 months ago

Yes, indeed, indentation is one of the very few things in Python which aren't problematic!

redlewel2• 3 months ago

Yes, I remember having a problem with python indentation. For some reason tabs and spaces were causing my code to fail to run!! This was when I was first learning programming and didn't know anything. Once I understood the syntax of the language it hasn't been a problem ever since. Its like being upset that your yaml doesn't work because you have mixed spaces and tabs.

nothinkjustai• 3 months ago

Seems just as valid as any of the other syntactical nitpicks of other languages, except it touches every line of code you write and it’s unavoidable.

thefounder2• 2 months ago

I must admit that I’ve tried python in my early days and indentation was the main reason why I just didn’t pursue the language further. It felt just brittle and hard to read. Of course if I had be forced I would have probably managed to master it but I had the freedom to use whatever I wanted as programming language and python was just not an attractive option.(despite being seduced by its “zen” and all that. Felt like form over function design.

The “build”/run/deploy system was the other major issue. All the python versions, virtual env etc seemed like a mess. A compiled language is so much better(I.e Go, Rust etc) IMHO.

Petersipoi• 2 months ago

How the fuck could you possibly even possibly begin to validate such a claim. Lol. Asshole.

smartmic• 3 months ago

Advertising prominently with "AI native" seems necessary today, at least for some folks. To me, that's kind of off-putting, since it doesn't really say anything.

Can anyone of the AI enthusiasts here explain, why, or, what is meant by

As a compiled, statically-typed language, it's also ideal for agentic programming.

rmnclmnt• 3 months ago

Because a coding agent (when instructed well) will try to make a piece of code work in a loop. Static typing and compilation help in the process (no more undefined variables discovered at runtime for instance). But that’s not bullet proof at all as most of us know

jpnc• 3 months ago

It's been really interesting to see all the desperation on hero pages for all these products and services ever since AI came into prominence. I think the funniest for me was opening IBM DB2 product page and seeing it labeled as 'AI database'. Hysterical.

why, or, what is meant by More errors caught at compile time means an agent can quickly check their work statically without unit and other tests.

Reubend• 3 months ago

I don't know what they meant by it, and I share your opinion that "AI native" is somewhat meaningless for a programming language like this.

Regarding compilation and static typing, it's extremely helpful to be able to detect issues at compile time when doing agentic programming. That way, you don't run into as many problems at runtime, which of course the agent has more difficulty addressing. Unit tests can help bridge the gap somewhat but not entirely.

What's not stated on their website is that Mojo is likely a bad choice for agentic programming simply because there isn't much Mojo training data yet.

boxed10• 3 months ago

I've recently used Claude to write quite a bit of mojo (https://github.com/boxed/TurboKod) and I can quite confidently say that Claude will write deprecated mojo syntax a lot, but the compiler tells it and it fixes it pretty fast too. The only reason I notice is that I look at Claude while it's working and I see the compilation warnings (and sometimes Claude is lazy and doesn't compile so I have to see it).

But yea, to write mojo 1.0 code even after getting errors might take a new training round, so next or even next-next models.

chillfox• 3 months ago

I don’t really consider myself an “AI enthusiasts”, but I do use it.

So, agents tend to do better the more feedback they can get. Type checking is pretty good for catching a bunch of dumb mistakes automatically.

The point is more hints for the agent is more better most of the time.

phyrog• 3 months ago

So just like for humans...

melodyogonna15• 3 months ago

https://mojolang.org/docs/tools/skills/

kstrauser7• 3 months ago

It’s the new “…on the blockchain”.

Python+ruff+pycheck and TypeScript are compiled to bytecode instead of machine code. They’re not statically typed in the Rust sense. And yet, I’ve watched model crank out good, valid in both of those without needing to be either strictly “compiled” or “statically typed”. Turns out AI couldn’t care less about those properties as long as you have good tooling to quickly check the code and iterate.

fuzztester2• 3 months ago

It’s the new “…on the blockchain”.

yes, except it's more ... on the same lines, just to hammer the point home:

it's web 2, it's SaaS, it's the latest weekly, er, sorry, daily, hottest JS framework, its the latest rap / punk / hippie / dreadlock / crewcut / swami / grunge/ guru hairstyle, it's agile, it's functional programming, it's OOP, it's OOAD, it's UML, its the Unix philosophy, its Booch notation, it's CASE tools, ... going back even further, it's structured programming, it's high-level languages, it's assemblers, its veganism, it's the keto diet, it's the Atkins diet, it's the paleo diet, it's cholesterol is bad, no, it's good, etc etc etc.

Derbasti• 3 months ago

Current LLMs have been trained on extensive libraries of past code. Therefore, LLMs will for the foreseeable future work better for established languages than new ones. Especially languages with a lot of open source code available, like Python. That's a big problem for incumbents without any existing code to train LLMs on.

Thus this desparate "AI native" marketing is probably necessary to even be considered relevant in an "agentic" world. Whether it's enough, only time will tell.

Timot052• 3 months ago

I’m relatively new to programming but I wish they had used a functional language syntax rather than an object oriented one as the basis for mojo.

From my experience, AI revolves a lot around building up function pipelines, computing their derivatives, and passing tons of data through them; which composability and higher order functions from functional programming make it a breeze to describe.

I also feel that other fields than AI are moving towards building up large functional pipelines to produce outputs, which would make mojo suitable for those fields as well. I’m building in the space of CAD for example and I’d love to use a “functional mojo” language.

arikrahman• 3 months ago

I'm in the same boat, this would've been in the family of the first language that neural nets and AI were created with back decades ago, Lisp. Coming from the awesome project of Swift, which to their credit, was a massive undertaking to convince Apple execs, I was still hoping for a functional language approach like Haskell with the practicality of Clojure.

Revanche1367• 3 months ago

The vast majority of real world ML code today is written in languages like Python and C++. Relatively few people outside of academia and online forums are functional language enthusiasts. The industry is also looking like most actual coding is going to be done by LLMs going forward, so it makes little sense to design new languages with a niche potential user base since LLMs need a ton of training data. I’m think that was a factor in deciding to base mojo on Python along with the other reasons they state.

Timot052• 3 months ago

agree with all of this. Though i'd say: since the language is mostly read by humans rather than written, in my opinion, it makes even more sense to have a language syntax that actually matches intent. In the case of Machine Learning, it's mostly connecting functions together and acting on them, which matches functional syntax. LLMs are also already very effective at writing ML-inspired syntax (like ocaml or f#) as they have plenty of data to train on, making llms effective from day one if a similar syntax was chosen.

armchairhacker• 3 months ago

We have committed to open-sourcing Mojo in Fall 2026.

https://docs.modular.com/mojo/faq/#will-mojo-be-open-sourced

jlundberg2• 3 months ago

Good catch in the noise. Thanks!

dismalaf• 2 months ago

Nice. I'd love to see the source of an actual state of the art MLIR program.

tkocmathla• 2 months ago

HEIR [1] is a homomorphic encryption compiler built on modern MLIR.

IREE [2] is very actively developed ML compiler + runtime, also MLIR-based.

[1] https://github.com/google/heir

[2] https://github.com/iree-org/iree

sriram_malhar• 3 months ago

Doesn't anyone here have _one_ kind word to say about its features? Every one seems to be starting with "on the other hand".

pjmlp24• 3 months ago

Many of us were already around during Swift for Tensorflow.

pjmlp24• 3 months ago

Julia is more mature for the same purposes, and since last year NVidia is having feature parity between Python and C++ tooling on CUDA.

Python cuTile JIT compiler allows writing CUDA kernels in straight Python.

AMD and Intel are following up with similar approaches.

If Mojo will still arrive on time to gain wider adoption remains to be seen.

adev_4• 3 months ago

Python cuTile JIT compiler allows writing CUDA kernels in straight Python.

It is currently not straight Python and will never be.

All these "Performance friendly" python dialects (Tryton, Pythran, CuTile, Numba, Pycell, cuPy, ...) appears like Python but are nothing like Python as soon as you scratch the surface.

They are DSL with a python-looking syntax but made to be optimized, typed and inferred properly. And it feels like it when you use it: in each of them, there is many (most?) python features you simply can not use while you still suffer of inherent python issues.

Lets not lie to ourself: Python is inherently bad for efficiency and performance.

And that goes way beyond the GIL: dynamic typing, reference semantics, monkey patching, ultra-dynamic object model, CPython ABI, BigInt by default, runtime module system, ... are all technical choices that makes sense for a small scripting language but terribly sucks for HPC and efficiency.

The entire Numpy/scipy ecosystem itself is already just a hack around Python limitations for simple CPU bound tensor arithmetics. Mainly because builtin python performance sucks so much that a simple for loop would make Excel looks like a race horse.

Mojo is different.

Mojo tries to start from a clean sheet instead of hacking the existing crap.

And tries to provide a "Python like experience" but on top of a well designed language constructed over past language design experience (Python is >30y old)

And just for that, I wish them success.

pjmlp24• 3 months ago

I love when dialects for C and C++ count as being proper C and C++, are even argued as being more relevant than ISO standards by themselves, but anyone else that does the same, it is no longer the same language.

As for Python not being the ideal, there we agree, but the solutions with proper performance already exist, Lisp, Scheme, Julia, Futhark,...

Heck maybe someone could dig out StarLisp.

kstrauser7• 3 months ago

All these "Performance friendly" python dialects (Tryton, Pythran, CuTile, Numba, Pycell, cuPy, ...) appears like Python but are nothing like Python as soon as you scratch the surface.

Which is the whole point. Python has properties that make it bad for massive, fast number twiddling. However, it’s exceptionally nice for doing all the command line parsing and file loading and setup and other wrapping tasks required to run those pipelines.

Fortran’s fantastic at math stuff. I’d sure hate to have to write all the related non-math stuff in it.

And yes, Python’s slower than other languages. But in production, most Python code spends a huge chunk of its time waiting for other code to execute. It takes more CPU for Python to parse an HTTP request or load data files than an AOT language would take, but it’s as efficient sitting there twiddling its thumbs waiting for a DB query or numeric library to finish.

jdiaz973• 3 months ago

Mojo tries to start from a clean sheet instead of hacking the existing crap.

Their whole original pitch was to be a superset of Python btw.

loglog2• 3 months ago

on top of a well designed language constructed over past language design experience

While I believe that Chris Lattner is a great compiler designer, his language design record has been less stellar. Swift bidirectional type inference for instance feels like it was implemented because they had a compiler algorithm that they wanted to use, rather than a genuine need, and is just a completely avoidable problem. Trying to make a HPC language that is also Python compatible was doomed from the start. Hopefully the damage from going into this direction will remain limited.

tveita• 3 months ago

Is there any project that showcases Mojo for running neural network models on the GPU - like ideally something like llama.cpp that could run one or more existing models to showcase the readability and performance?

melodyogonna15• 3 months ago

Something like this https://github.com/tairov/llama2.mojo?

DeathArrow2• 3 months ago

As a compiled, statically-typed language, it's also ideal for agentic programming.

Since there is not much Mojo code in the wild so the LLMs were trained on it, I wonder how it will work in practice.

Probably the agents will make lots of mistakes and you will spend 10x the tokens compared to using a language the model are well versed in.

taylorallred• 3 months ago

I know Mojo is aimed at ML, but I'm actually really interested in trying it for game development :)

totalperspectiv4• 3 months ago

Me too! I've been using it for bioinformatics related work, and it is absolutely fantastic. I can't wait for it to hit fully open source status so it can be easily recommended.

simplyvibecode2• 3 months ago

Full open source Mojo 1.0 coming this fall!

momojo2• 3 months ago

I work in bioimaging. What kind of bioinformatics are you doing that requires mojo level power?

coppsilgold• 3 months ago

Python is basically the master glue language at this point.

If more than a few percent of execution time is spent in Python you are probably doing it wrong.

Personally I don't even understand why Cython is a thing, just write performance critical functions in other languages:

<https://pypi.org/project/rustimport/>

<https://pypi.org/project/import-zig/>

Note that you can even start threads in those languages and use function calls as pseudo-RPC. All without an overly complex build system.

physicsguy4• 3 months ago

Cython and PyBind and Nanobind are good for wrapping an existing library written in C++ and crafting an interface that doesn’t feel like it’s a C++ one. They were a big step from ctypes and SWIG

pjmlp24• 2 months ago

Plus, even if it has a Perl like feeling to it, C++26 reflection will make this even easier.

Already available on GCC 16.

MohamedMabrouk6• 3 months ago

Mojo aims to be this (other language) arguably with easier programming model that rust, familiar syntax to python devs, and a modern design in general. Its stated goal now, is the easiest way to extend python. it provides the same interface for zero-hassle import of .mojo files

sirfz• 3 months ago

Cython is a no-brainer really. You write the same language with immense speedup (matching what the "other languages" can achieve at much less effort).

Also tools like numba can beat them all at way less effort.

Imho, dropping into other languages should be the last resort in any project.

fiedzia2• 2 months ago

If more than a few percent of execution time is spent in Python you are probably doing it wrong.

Every program that starts with 1% of Python writes more Python and gets to 20,40, 60 and than 99% of it.

totalperspectiv4• 3 months ago

Having written a lot of Mojo over the last two year, just for fun, it's a really cool language. Ownership model adjacent to Rust, comptime that is more powerful than Zig, Rich type system, first class SIMD support, etc.

Performance wise it's the first language in long time that isn't just an LLVM wrapper. LLVM is still involved, but they are using it differently than say, Rust or Zig.

Very excited for Mojo once it's open sourced later this year.

tapirl2• 2 months ago

..., comptime that is more powerful than Zig

It would be great if you can elaborate more here. I can't make the conclusion from Mojo's docs now.

melodyogonna15• 2 months ago

Not OP, but see the entire section on metaprogramming: https://mojolang.org/nightly/docs/manual/metaprogramming/. Mojo's compile-time programming is influenced by Zig, but has features that are not in Zig e.g traits, generics (real generics), and constraints, that work together.

AbuAssar• 3 months ago

AI native

What’s that supposed to mean?

jjice• 3 months ago

My guess is that they expose first party skills and maybe other agent friendly docs? Not positive though.

csvance• 2 months ago

Mojo looks neat but I'm pretty satisfied with Julia at this point for high performance numerical computing across CPU, GPU, etc. I can't help but feel this niche is already mostly solved beyond having Python like syntax. Even Python has things like Numba and Triton that are effective for less complicated / more self contained type problems.

bobajeff• 2 months ago

I've been keeping my eye on mojo. Honestly though the thing I least like about Python is it's syntax.

Someone else here is bringing up Julia. Which I think is a fine language but the compiler error messages and the library documentation are not what I would want in a language as far along as it is. I'm also worried about the correctness issues I've read about in a blog awhile back. Also I don't feel like I can make the kind of Python module I want with it (because of binary size and time to first x)

That being said I'm only hoping that Mojo can become an option. But I really like to use a REPL and I like the dynamicness of Python. So I might not ever get around to doing anything outside of maybe Numpy for performance.

Recurecur• 2 months ago

I’m a big fan of Mojo’s design. It isn’t comparable to Julia since it has deterministic memory management.

I also think Mojo is more focused on being an industrial strength language. I was shocked to see the first iteration of Julia ahead of time compilation did not provide file I/O.

archargelod• 2 months ago

the thing I least like about Python is it's syntax.

For me it's the opposite - the only thing I like about Python is it's syntax. That's why I really like Nim - you get C speed, "comptime", metaprogramming, powerful type system, memory safety and code is often short and elegant.

Mojo seems interesting too, but so far they're mostly focused on ML stuff and not general programming. And I believe compiler is still not open-source?

jadar• 2 months ago

Congrats to Chris Lattner and crew! Seems like a neat project. I listened to him talk about Mojo on some podcast a while back and was really impressed. Swift was a runaway success, and I hope he can pull it off again with Mojo! :)

rienbdj• 2 months ago

Does anyone know if Mojo is more suitable for functional programming that Python?

Things like optimizing away object allocations, pure function inlining, tail call optimization?

andriamanitra• 2 months ago

The changelog looks reasonable, seems like the language is moving in the right direction. I'm excited to try it out once it's open sourced. The dependency on glibc is an unfortunate limitation as it means you can't install Mojo on musl-based systems.

[1] https://mojolang.org/releases/v1.0.0b1/

maxloh• 2 months ago

Modular is going to open source the entire mojo SDK later this year, including the compiler.

Mojo 1.0 will be finalized later this year, along with opening the compiler and providing language stability.

https://www.modular.com/blog/modular-26-3-mojo-1-0-beta-max-...

VerifiedReports• 2 months ago

Is what?

Mojo 1.0 Beta

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