It's the second time today when I see that the higher number of LoC is served as something positive. I would put it strictly in "Ugly" category. I understand the business logic that says that as long as you can vibe code away from any problems, what's the point of even looking at the code.
Higher LOC means the code will be able to handle a wider range of different input conditions. In the next couple years we should be able to 10x or even 100x the amount of code we can manage with LLMs, to the extent that our systems' world models are effectively indistinguishable from the real world.
Yes, as we all know, when evaluating which programming language to use, you should get a line count of the compiler's repo. More lines = more capabilities.
Why would I ever want a language with less capabilities?
> In Elixir tests, each test runs in a database transaction that rolls back at the end. Tests run async without hitting each other. No test data persists.
And it confuses Claude.
This way of running tests is also what Rails does, and AFAIK Django too. Tests are isolated and can be run in random order. Actually, Rails randomizes the order so if the are tests that for any reason depend on the order of execution, they will eventually fail. To help debug those cases, it prints the seed and it can be used to rerun those tests deterministically, including the calls to methods returning random values.
I thought that this is how all test frameworks work in 2026.
I did too, and I've had a challenging time convincing people outside of those ecosystems that this is possible, reasonable, we've been doing it for over a decade.
That's easier said than done. Simple example: API that returns a count of all users in the database. The obvious correct implementation that will work would be just to `select count(*) from users`. But if some other test touches users table beforehand, it won't work. There is no uuid to latch onto here.
I can attest to everything. Using Tidewave MCP to give your agent access to the runtime via REPL is a superpower, especially with Elixir being functional. It's able to proactively debug and get runtime feedback on your modular code as it's being written. It can also access the DB via your ORM Ecto modules. It's a perfect fit and incredibly productive workflow.
Some MCP's do give the models superpowers. Adding playwright MCP changed my CC from mediocre frontend skills, to really really good. Also, it gives CC a way to check what it's done, and many times correct obvious errors before coming back at you. Big leap.
Opus 4.5 with Elixir has been remarkably good for me. I've been writing Elixir in production since ~2018 and it continues to amaze me at the quality of code it produces.
I've been tweaking my skills to avoid nested cases, better use of with/do to control flow, good contexts, etc.
They could've been sorted with precise context injection of claude.md files and/or dedicated subagents, no?
My experience using Claude suggests you should spend a good amount of time scaffolding its instructions in documents it can follow and refer to if you don't want it to end in the same loops over and over.
It seems like the 100% vibe coded is an exaggeration given that Claude fails at certain tasks.
The new generation of code assistants are great. But when I dogmatically try to only let the AI work on a project it usually fails and shots itself in its proverbial feet.
If this is indeed 100% vibe coded, then there is some magic I would love to learn!
It's interesting that Claude is able to effectively write Elixir, even if it isn't super idiomatic without established styles in the codebase, considering Elixir is a pretty niche and relatively recent language.
What I'd really like to see though is experiments on whether you can few shot prompt an AI to in-context-learn a new language with any level of success.
It's certainly helpful, but has a tendency to go for very non idiomatic patterns (like using exceptions for control flow).
Plus, it has issues which I assume are the effect of reinforcement learning - it struggles with letting things crash and tends to silence things that should never fail silently.
I tried different LLMs with various languages so far: Python, C++, Julia, Elixir and JavaScript.
The SOTA models come do a great job for all of them, but if I had to rank the capabilities for each language it would look like this:
JavaScript, Julia > Elixir > Python > C++
That's just a sample size of one, but I suspect, that for all but the most esoteric programming languages there is more than enough code in the training data.
I've used CC with TypeScript, JavaScript and Python. Imo TypeScript gives best results. Many times CC will be alerted and act based on the TypeScript compile process, another useful layer in it's context.
You can accurately describe elixir syntax in a few paragraphs, and the semantics are pretty straightforward. I’d imagine doing complex supervision trees falls flat.
I expect the costs at source will go down even if model performance doesn’t improve much, and hopefully that will offset the unraveling of subsidisation. I’d be happy enough with that outcome, I don’t really need them to be any better although of course it would be nice. I would love for them to be faster and cheaper.
The imperative thing is so frustrating. Even the latest models still write elixir like a JS developer, checking nils, maybe_do_blah helper functions everywhere. 30 lines when 8 would do.
More LOC means the code is more adaptable. In the past, increased LOC were difficult for humans to manage. However, as technological progress marches forward and AI takes over the job of code management, it will actually become beneficial to have increased LOC. In the next few years, AI code producers will generate literally superhuman codebases, which will contain vast quantities of simple, interrelated evaluations, creating software with such comprehensive subject area modelling that it would have been literally impossible for humans to manage in the old days. We are entering a new era, and the old assumptions no longer apply.
You're not missing much. Seems to me like they wrote 150k lines of code for some glorified photo app with ChatGPT in the backend for image processing. Oh and some note-taking it seems.
https://genius.com/Jorge-luis-borges-on-exactitude-in-scienc...
Why would I ever want a language with less capabilities?
Just in case not, consider whether the short function
Handles a wider range of input conditions than the higher LOC functionAnd it confuses Claude.
This way of running tests is also what Rails does, and AFAIK Django too. Tests are isolated and can be run in random order. Actually, Rails randomizes the order so if the are tests that for any reason depend on the order of execution, they will eventually fail. To help debug those cases, it prints the seed and it can be used to rerun those tests deterministically, including the calls to methods returning random values.
I thought that this is how all test frameworks work in 2026.
That's easier said than done. Simple example: API that returns a count of all users in the database. The obvious correct implementation that will work would be just to `select count(*) from users`. But if some other test touches users table beforehand, it won't work. There is no uuid to latch onto here.
I've been tweaking my skills to avoid nested cases, better use of with/do to control flow, good contexts, etc.
We don't 100% AI it but this very much matches our experience, especially the bits about defensiveness.
Going to do some testing this week to see if a better agents file can't improve some of the author's testing struggles.
They could've been sorted with precise context injection of claude.md files and/or dedicated subagents, no?
My experience using Claude suggests you should spend a good amount of time scaffolding its instructions in documents it can follow and refer to if you don't want it to end in the same loops over and over.
Author hasn't written on whether this was tried.
The new generation of code assistants are great. But when I dogmatically try to only let the AI work on a project it usually fails and shots itself in its proverbial feet.
If this is indeed 100% vibe coded, then there is some magic I would love to learn!
What I'd really like to see though is experiments on whether you can few shot prompt an AI to in-context-learn a new language with any level of success.
It's certainly helpful, but has a tendency to go for very non idiomatic patterns (like using exceptions for control flow).
Plus, it has issues which I assume are the effect of reinforcement learning - it struggles with letting things crash and tends to silence things that should never fail silently.
The SOTA models come do a great job for all of them, but if I had to rank the capabilities for each language it would look like this:
JavaScript, Julia > Elixir > Python > C++
That's just a sample size of one, but I suspect, that for all but the most esoteric programming languages there is more than enough code in the training data.
What if it doesnt? What if LLMs just stay mostly the same level of usefulness they are now, but the costs continue to rise as subsidization wears off?
Is it still worth it? Maybe, but not worth abandoning having actual knowledge of what you’re doing.
- https://github.com/agoodway/.claude/blob/main/skills/elixir-...
- https://github.com/agoodway/.claude/blob/main/agents/elixir-...
- https://github.com/agoodway/.claude/blob/main/agents/elixir-...
Getting pretty good results so far.
- Silently closes the tab, and makes a remark to avoid given software at any cost.