kcsrk

Super interesting. Thanks for sharing.

I have used Jupyter notebooks with a Docker deployment in my prior courses for OCaml and Prolog, with support for auto grading (using nbgrader). Jupyter notebooks aren’t great for version control. Otherwise, it worked pretty well across multiple iterations of the course with new professors coming in and independently being able to use the infra.

Author of the post here.

I need a Linux VM to illustrate a couple of things in the last few lectures. Showing C undefined behaviour, memory safety issues and how that becomes security issues, compiling OCaml program to show test coverage, and being able to build unikernels. So can’t completely do away with Linux VMs.

For Unikernels, currently v86 is 32-bit only and OCaml 5 has dropped support for native 32-bit backends. In order to boot 64-bit unikernels, I’d need qemu. All of this could be done, but (a) this is likely to be quite slow (and the current emulation already is) and (b) the learning objectives are probably not improved a lot with the additional infrastructure. Hence, dropped it.

What’s surprised me in the last few months is that agents are great at producing OCaml 5+ and OxCaml code, not much of which is out there in the training data. OxCaml’s strong types and modes seem to serve as great testable oracles to guide the agents.

I taught a course on concurrent programming based on OCaml 5 and OxCaml where almost all of the code in the teaching materials were vibe coded. I reviewed all of the code (because I was teaching it to a class of 50+ students) and frankly the agent writes better O(x)Caml (mostly) than me.

It was overlooked because I didn't know!

Great to hear about virt-v2v. I will reach out to you by email.

It is often hard to see the shape of these things before a serious PR attempt is made. Each of the PRs reveals more of the shape of the problem being solved. Hard to skip them in practice, especially for new contributors.

You are right that the dynamic arrays story does not read like a straightforward “how to inspire contributions.” But part of what I wanted to do in the talk was to show things as they actually unfolded. In OCaml compiler development, there is a very strong emphasis on correctness and long-term stability. That can make contributions, especially to core language features, feel harder than they might in faster-moving ecosystems.

The dynamic arrays case is a good illustration. What began as a small PR grew into years of design iterations, debates about representation, performance, and multicore safety, and eventually a couple of thousand lines of code and more than 500 comments before it landed. From one perspective, that looks discouraging. From another, it shows the weight we place on getting things right, because once a feature ships, it is very hard to undo.

That tension, between wanting to be open and encouraging contributions but also needing to protect stability, is something I think we should be talking about openly. My hope is that by making the process more visible we can demystify it and help contributors understand not just what happened, but why.

I am the author of the talk here o/.

This talk is a _subjective_ take on how the OCaml programming language evolves, based on my observations over the last 10 years I've been involved with it. My aim/hope is to demystify the compiler development process and the tradeoffs involved and encourage more developers to take a shot at contributing to the OCaml compiler.

Happy to answer questions, but also, more importantly, hear your comments and criticisms around the compiler development process, ideas to make it more approachable, etc.

Right. It is hard in general to compare performance or generated code across languages as they make different trade offs. Table 3 and 5 show compilation of our memory model to X86 and ARMv7. The compilation and the related work section discusses trade offs.

To summarise, atomic read and write in OCaml is more expensive than C++ since C++ SC atomics only establish total order between SC operations on not operations that are weaker but related to SC operations by happens before. We need stronger atomics for local data race freedom.

Our non atomic are also stronger than non atomics on C++ and Java. That said, The non atomic operations are free on X86 (compiled to plain loads and stores) and involve a lightweight fence before stores on weaker architectures such as ARM, Power and RISC-V. The performance results show that extra fences before stores have a barely noticeable impact on ARM and Power.

The local types are less invasive than the full support for typed effects. In particular, they are opt-in and associated complexity is pay-as-you-go. In my initial experiments, they seemed pretty nice to program with.