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This might be of interest to others: Last night I stumbled across Hornbeam, a library in a similar vein from the author of Gunicorn that handles WSGI / ASGI apps as well as a specific wrapper for ML inference

https://erlangforums.com/t/hornbeam-wsgi-asgi-server-for-run... https://github.com/benoitc/hornbeam

We also had a similar use case, so I built Snex[0] - specifically for Elixir-Python interop. Elixir-side spawns interpreters with Ports managed by GenServers, Python-side has a thin asyncio runtime to run arbitrary user code. Declarative environments (uv), optimized serde with language-specific objects (like `%MapSet{}` <-> `set`), etc. Interpreters are meant to be long lived, so you pay for initialization once.

It's a very different approach than ex_cmd, as it's not really focused on the "streaming data" use case. Mine is a very command/reply oriented approach, though the commands can flow both ways (calling BEAM modules from Python). The assumption is that big data is passed around out of band; I may have to revisit that.

[0]: https://github.com/kzemek/snex

Similar use case as well. I use erl ports to spawn a python process as well. Error handling is a mess, but using python as a short scripting language and elixir for all the database/application/architecture has been very ideal

I have one vibecoded ml pipeline now and I'm strongly considering just clauding it into Nx so I can ditch the python

Is this part of a web server or some other system where you could end up spawning N python processes instead of 1 at a time?

Honestly you saved yourself major possible headaches down the line with this approach.

At my work we run a fairly large webshop and have a ridiculous number of jobs running at all times. At this point most are running in Sidekiq, but a sizeable portion remain in Resque simply because it does just that, start a process.

Resque workers start by creating a fork, and that becomes the actual worker.

So when you allocate half your available RAM for the job, its all discarded and returned to the OS, which is FANTASTIC.

Sidekiq, and most job queues uses threads which is great, but all RAM allocated to the process stays allocated, and generally unused. Especially if you're using malloc it's especially bad. We used jemalloc for a while which helped since it allocates memory better for multithreaded applications, but easiest is to just create a process.

I don't know how memory intensive ML is, what generally screwed us over was image processing (ImageMagick and its many memory leaks) and... large CSV files. Yeah come to think of it, you made an excellent architectural choice.

that's easy with https://hexdocs.pm/pythonx/Pythonx.html and https://hexdocs.pm/lua/Lua.html and works well too

btw, a lot of postgres envs are not going to have pgmq, so just use Oban and don't reinvent the wheel like I did ;)

So if your app is 99% elixir but 1% is easier in Python because a lib you should rewrite the whole app? Makes no sense. Do you think Python devs rewrite everything in C if they have a small part that needs to use C instead of Python?

It's 2026 and the LLMs score high on elixir, just write it in python and patch it over to elixir gradually

So I know these are just benchmarks, but apparently Elixir is one of the best languages to use with AI, despite having a smaller training dataset: https://www.youtube.com/watch?v=iV1EcfZSdCM and https://github.com/Tencent-Hunyuan/AutoCodeBenchmark/tree/ma...

Furthermore, it's actually kind of annoying that the LLMs are not better than us, and still benefit from having code properly typed, well-architected, and split into modules/files. I was lamenting this fact the other day; the only reason we moved away from Assembly and BASIC, using GOTOs in a single huge file was because us humans needed the organization to help us maintain context. Turns out, because of how they're trained, so do the LLMs.

So TypeScript types and tests actually do help a lot, simply because they're deterministic guardrails that the LLM can use to check its work and be steered to producing code that actually works.

> Succinctness, functionality and popularity of the language are now much more important factors.

Not my experience at all. The most important factor is simplicity and clarity. If an LLM can find the pattern, it can replicate that pattern.

Language matters to the extent it encourages/forces clear patterns. Language with more examples, shorter tokens, popularity, etc - doesn't matter at all if the codebase is a mess.

Functional languages like Elixir make it very easy to build highly structured applications. Each fn takes in a thing and returns another. Side effects? What side effects? LLMs can follow this function composition pattern all day long. There's less complexity, objectively.

But take languages that are less disciplined. Throw in arbitrary side effects and hidden control flow and mutable state ... the LLM will fail to find an obviously correct pattern and guess wildly. In practice, this makes logical bugs much more likely. Millions of examples don't help if your codebase is a swamp. And languages without said discipline often end up in a swamp.

> I don't see the point of Elixir now. LLMs work better with mainstream languages which make up a bigger portion of their training set.

I can't say if it works better with other languages, but I can definitely say both Opus and Codex work really well with Elixir. I work on a fairly large application and they consistently produce well structured working code, and are able to review existing code to find issues that are very easy to miss.

The LLM needs guidance around general patterns, e.g. "Let's use a state machine to implement this functionality" but it writes code that uses language idioms, leverages immutability and concurrency, and generally speaking it's much better than any first pass that I would manually do.

I have my ethical concerns, but it would be foolish of me to state that it works poorly - if anything it makes me question my own abilities and focus in comparison (which is a whole different topic).

LLMs work great with Elixir. Running tsc in a loop while generating code still catches type errors introduced by an LLM and it’s faster than generating additional tests. Elixir is also succinct and highly functional. If you can’t find a specific library it’s easier than ever to build out the barebones functionality you need yourself or use NIFs, ports, etc.

https://dashbit.co/blog/why-elixir-best-language-for-ai

> Succinctness, functionality and popularity of the language are now much more important factors.

No. I would argue that popularity per se is irrelevant: if there are a billion examples of crap code, the LLMs learn crap code. conversely know only 250 documents can poison an LLM independent if model size. [Cite anthropic paper here].

The most important thing is conserve context. Succinctness is not really what you want because most context is burned on thinking and tool calls (I think) and not codegen.

Here is what I think is not important: strong typing, it requires a tool call anyways to fetch the type.

Here is what I think is important:

- fewer footguns - great testing (and great testing examples) - strong language conventions (local indicators for types, argument order conventions, etc) - no weird shit like __init__.py that could do literally anything invisible to the standard code flow

Your code doesn’t run anywhere? Running on the BEAM is extremely helpful for a lot of things. Also, I review my LLM output, I want that experience to be enjoyable.

I'm starting to see a new genre of post here in the AI bubble, where people go to topics that aren't about AI at all, and comment something like, "this doesn't matter because it's not AI". This is the third I've seen in a week.

It’s almost as though choosing a single-threaded, GIL-encumbered interpreted scripting language as the primary interface to an ecosystem of extremely parallelized and concurrent high-performance hardware-dependent operations wasn’t quite the right move for our industry.

Strange opinion. Plenty of apps have more than one language. I might end up using this.

Why? Because my app is built in Elixir and right now I’m also using a python app that is open source but I really just need a small part of the python app. I don’t wanna rewrite everything in Elixir because while it’s small I expect it to change over time (basically fetching a lot of data sources) and it will be pain to keep rewriting it when data collections needs to change (over a 100 different sources). Right now I run the python app as an api but it’s just so overkill and harder to manage vs just handling everything except the actually data collection in Elixir where I am already using Oban.

Sometimes the "right tool for the job" philosophy leads to breaking down a larger problem into two small problems, each which has a different "right tool".

Choosing a single tool that tries to solve every single problem can lead to its own problems.

I disagree, using python for a web-server and something like celery for background work is a pretty common pattern.

My reading of this is it more or less allows you to use Postgres (which you're likely already using as your DB) for the task orchestration backend. And it comes with a cool UI.

What leads you to this conclusion