> ... unless you train ChatGPT on a bunch of code examples and contexts then it can only get so close.

How do you do this?

Ah yes, truly impressive, Pong. A game that countless textbooks et al have recreated numerous times. There's a mountain of training data for something so unoriginal.

I don't know if you're working with modern models. Grok 4 doesn't really know much about assembly language on the Apple II but I gave it all of the architectural information it needed in the first prompt of a conversation and it built compilable and executable code. Most of the issues I encountered were due to me asking for too much in a prompt. But it built a complete, albeit simple, assembly language game in a few hours of back and forth with it. Obviously I know enough about the Apple II to steer it when it goes awry, but it's definitely able to write 'original' code in a language / platform it doesn't inherently comprehend.

Yep I program in some niche languages like Pike, Snobol4, Unicon. Vibe coding is out of the question for these languages. Forced to use my brain!

I've noticed the error rate doesn't matter if you have good tooling feeding into the context. The AI hallucinates, sees the bug, and fixes it for you.

Even with typescript Claude will happily break basic business logic to make tests pass.

> The models

The models don’t have a model of the world. Hence they cannot reason about the world.

Completely agree. I’m a professional engineer, but I like to get some ~vibe~ help on person projects after-work when I’m tired and just want my personal project to go faster. I’ve had a ton of success with go, JavaScript, python, etc. I had mixed-success with writing idiomatic Elixir roughly a year ago, but I’ve largely assumed that this would be resolved today, since every model maker has started aggressively filling training data with code, since we found the PMF of LLM code-assistance.

Last night I tried to build a super basic “barely above hello world” project in Zig (a language where IDK the syntax), and it took me trying a few different LLMs to find one that could actually write anything that would compile (Gemini w/ search enabled). I really wasn’t expecting it considering how good my experience has been on mainstream languages.

Also, I think OP did rather well considering BASIC is hardly used anymore.

I've had a similar problem with WebGPU and WGSL. LLMs create buffers with the wrong flags (and other API usage errors), doesn't clean up resources, mix up GLSL and WGSL, write semi-less WGSL (in template strings) if you ask them to write semi-less [0] JS...

It's a big mess.

0. https://github.com/isaacs/semicolons/blob/main/semicolons.js

Yes and if you work with a plarform that has been arround for long time like .net you will most definitely get a mix of really outdated deprecated code mixed with the latest features.

I find for these kinds of systems, if I pre-seed Claude Code with a read of the language manual (even the BNF etc) and a TLDR of what it is, results are far better. Just part of the initial prompt: read this summary page, read this grammar, and look at this example code.

I have had it writing LambdaMOO code, with my own custom extensions (https://github.com/rdaum/moor) and it's ... not bad considering.

Here's an example of a recent experience.

I have a web site that is sort of a cms. I wanted users to be able to add a list of external links to their items. When a user adds a link to an entry, the web site should go out and fetch a cached copy of the site. If there are errors, it should retry a few times. It should also capture an mhtml single file as well as a full page screenshot. The user should be able to refresh the cache, and the site should keep all past versions. The cached copy should be viewable in a modal. The task also involves creating database entities, DTOs, CQRS handlers, etc.

I asked Claude to implement the feature, went and took a shower, and when I came out it was done.

Sure it did ok with examples that are easily found in a text book like drawing a circle.

The "Yo dawg, I heard..." memes are writing themselves today.

I'm thinking Gemini CLI because of the context. He could add some information about the programming language itself in the project. I think that would help immensely.

Fantasy.

They'll never be fit for purpose. They're a technological dead-end for anything like what people are usually throwing them at, IMO.

Clearly the issue is that you are going from Erlang/Elixir to Java, rather than the other way around :)

Jokes aside, they are pretty different languages. I imagine you'd have much better luck going from .Net to Java.

I just wished the LLM model providers would realize this and instead would provide specialized LLMs for each programming language. The results likely would be better.

I’m curious what your process was. If you just said “rewrite this in Java” I’d expect that to fail. If you treated the llm like a junior developer or an official project, worked with them to document the codebase, come up with a plan, tasks for each part of the code base and a solid workflow prompt- I would expect it to succeed.

Claude Code / 4o struggle with this for me, but I had Claude Opus 4 rewrite a 2,500 line powershell script for embedded automation into Python and it did a pretty solid job. A few bugs, but cheaper models were able to clean those up. I still haven't found a great solution for general refactoring -- like I'd love to split it out into multiple Python modules but I rarely like how it decides to do that without me telling it specifically how to structure the modules.

If you try to ride a bicycle, do you expect to succeed at the first try? Getting AI code assistants to help you write high quality code takes time. Little by little you start having a feel for what prompts work, what don't, what type of tasks the LLMs are likely to perform well, which ones are likely to result in hallucinations. It's a learning curve. A lot of people try once or twice, get bad results, and conclude that LLMs are useless. But few people conclude that bicycles are useless if they can't ride them after trying once or twice.

Yep, thought the same thing. I guess people have very different expectations.

> I seriously don't understand why these articles are upvoted so much

It confirm a bias for some, it triggers others who might have the opposite position (and maybe have a bias too on the other end).

Perfect combo for successful social media posts... literally all about "attention" from start to finish.

Short while ago it was like "you are still using <X>? Why not the new 4o?!"

A coding model doesn't seem to produce better results: https://news.ycombinator.com/item?id=44624222

Exactly this. I'm a self taught PHP/jQuery guy that learned it well enough to make an entire saas that enough companies pay for that it's a decent little lifestyle business.

I started another project recently basically vibe coding in PHP. Instead of a single page app like I made before, it's just page by page single loading. Which means the AI also only needs to keep a few functions and the database in its head, not constantly work on some crazy ui management framework (what that's called).

It's made in a few days what would have taken me weeks as an amateur. Yet I know enough to catch a few 'mistakes' and remind it to do it better.

I'm happy enough.

Not only is it a useful constraint to ask for mode 7, but making sure the context contains domain-expert technology puts the LLM in a better spot in the sampling space.

Just for fun I asked ChatGPT "How would you ask an LLM to write a drawing program for the ATARI?" and it asked back a bunch of details to which I answered "I have no idea, just go with the simplest option". It chose the correct graphics mode and BASIC and created the program (which I didn't test).

I still agree with you for large applications but for these simple examples anyone with a basic understanding of vibe coding could wing it.

This is a description of a “tool”. Anyone can use a hammer and chisel to carve out wood, but only an artist with extensive experience will create something truly remarkable.

I believe many in this debate are confusing tools with magic wands.

In this case I asked ChatGPT without the part specifying mode 7 and it replied with a working program using mode 7, with a comment at the top that mode 7 would be the best choice.

Previous generations would have simply read something like the circle drawing writeup by Jeffrey S. McArthur in chapter 4 of COMPUTE!'s Third Book of Atari which as a matter of fact is available in scrapable text. (-:

* https://archive.org/details/ataribooks-computes-third-book-o...

* https://atariarchives.org/c3ba/page153.php

Fun fact: Orson Scott Card can be found in chapter 1.

Exactly! If you can't properly assess the output of the AI, you are really only shooting into the dark.

> Vibe Coding seems to work best when you are already an experienced programmer.

I think that is a very vague and ambiguous way of putting it.

I would frame it a tad more specific: vibecode seems to work best when users know what they want and are able to set requirements and plan ahead.

Vibecoding doesn't work at all or is an unmaintainable god awful mess if users don't do software engineering and instead hack stuff together hoping it works.

Garbage in, garbage out.

Exactly. I would not like to be called a vibe coder for using an LLM for tedious tasks though, is it not a pejorative term? I used LLMs for a few projects and it did well, because I knew what I wanted and how I wanted. So yeah, you do have to be an experienced programmer to excel with LLMs.

That said, you can learn a lot using LLMs, which is nice. I have a friend who wants to learn Python, and I have given him actual resources, but I have also told him to use LLMs.

Even then, I've seen LLMs generate code with subtle bugs that even experienced programmers would trip on. For the Atari specifically, I've seen:

- Attempting to use BBC BASIC features in Atari BASIC, in ways that parsed but didn't work - Corrupting OS memory due to using addresses only valid on an Apple II - Using the ORG address for the C64, such that it corrupts memory if loaded from Atari DOS - Assembly that subtly doesn't work because it uses 65C02 instructions that execute as a NOP on a 6502 - Interrupt handlers that occasionally corrupt registers - Hardcoding internal OS addresses only valid for the OS ROM on one particular computer model

The POKE 77,0 in the article is another good example. ChatGPT labeled that as hiding the cursor, but that's wrong -- location 77 is the attract timer counter on the Atari OS. Clearing it to 0 periodically resets the timer that controls the OS's primitive screensaver. But in order for this to work, it has to be done periodically -- doing it at the start will just reset this timer once, after which attract mode will start in 9 minutes. So effectively, this is an easter egg that got snuck into the program, and even if the unrequested behavior was desirable, doesn't work.

I'm going to doubt that. I was pushing GPT a couple weeks ago to test its limits. It's 100% unable to write compilable Go ASM syntax. In fairness it's slightly oddball, but enough exists that it's not esoteric.

In the error feedback cycle, it kept blaming Go, not itself. A bit eye opening.

If you are familiar with HP's calculators, x87 Asm isn't that difficult. Also noteworthy is that its density makes it a common choice for tiny demoscene productions.

Prediction: it can do it, so long as you tell it to explicitly keep track of the FPU stack in comments on every FPU instr.

Somehow this also reminds me of http://raisingtalentthebook.com/wp-content/uploads/2014/04/t...

"I taught my dog to whistle!"

"Really? I don't hear him whistling."

"I said I taught him, not that he learnt it."

The variation I've seen on this applied to AIs is:

Fred insists to his friend that he has a hyper intelligent dog that can talk. Sceptical, the friend enquires of the dog "What's 2+2?"

"Five" says the dog

"Holy shit a talking dog!" says the friend "This is the most incredible thing that I've ever seen in my life".

"What's 3+3?"

"Eight" says the dog.

"What is this bullshit you're trying to sell me Fred?"

Try playing an adversarial word game with ChatGPT. like the rules are, one player asks questions and the other is not allowed to say "yes" or "no", not allowed to reuse the same wording, and not allowed to evade the question. You'll see its tail wagging pretty quickly.

From my experience (we have to vibe code as it becomes a norm in the company), vibe coding is most effective when the developer feeds detailed context to the agent beforehand, and gives very specific commands to it for each task. It still speeds up development quite a bit once everything goes the right direction.

Nobody says LLMs aren't impressive. But there is a subtle difference an impressive trick and being something worth throwing 1% of GDP at. Proponents say that if we keep throwing more money at it, it will improve, but this is far from certain.

I asked chatgpt to write a short fable about the phenomena of vibe coding in the style of Aesop:

The Owl and the Fireflies

One twilight, deep in the woods where logic rarely reached, an Owl began building a nest from strands of moonlight and whispers of wind.

"Why measure twigs," she mused, "when I can feel which ones belong?"

She called it vibe nesting, and declared it the future.

Soon, Fireflies gathered, drawn to her radiant nonsense. They, too, began to build — nests of smoke and echoes, stitched with instinct and pulse. "Structure is a cage," they chirped. "Flow is freedom."

But when the storm came, as storms do, their nests dissolved like riddles in the rain.

Only the Ants, who had stacked leaves with reason and braced walls with pattern, slept dry that night. They watched the Owl flutter in soaked confusion, a nestless prophet in a world that demanded substance.

Moral: A good feeling may guide your flight, but only structure will hold your sky.

Exactly! The way in which the LLM is used here is very, very basic and outdated. This experiment should be redone in a proper „agentic” setup where there is a feedback loop between the model and the runtime plus access to documentation / internet. The goal now is not to encapsulate all the knowledge inside single LLM - this is too problematic and costly. LLM is a language model not knowledge database. It allows to interpret and interact with knowledge and text data from multiple sources.

I think that is a cool way of looking at it - build on what you said, to me it might be a bit like in both instances (dreaming and llms) - it's maybe a bit related to what they are trying to do (presuming dreams have a purpose) + the resource they have to do it in + the context they have to couple in to get the point across + something related to the abilities of the user? Lets for fun say there is a subsystem that understands and runs dreaming, you only have so much time, plus it's a weird modality, and you're trying to do something... maybe it's fine enough to just serve up the story no matter how muddled it is, what you might be talking about in LLMs is a similar thing? Dreams have a ticking clock where the brain chemistry is literally changing and the opportunity for that type of processing is about to disappear, and eventually the human will awaken, LLMs have a context window size. Fun thinking, anyway.

Well it's neither schizophrenic nor using human reasoning. It's a machine using transformers no reason to overly anthropomorphize it, but sure the parallels do exist to some degree.

These tools are obviously not reasoning tools in the classic sense, as they're not building off of core axiomic truths like classic logic. These truths may be largely embedded in the probabilistic output, due to the input universe that we feed it being based on human reason, but it's certainly not in a power of these tools. That said, of course we are attacking on more and more of this ability (ability to check databases of facts, iterative "reasoning" models, etc) so they are becoming "adequate" in these respects.

The dreaming comparison seems quite apt though. I entirely get what you mean by rereading the dream word and seeing it suddenly transformed to another city name, yet somehow also "fitting". For some reason I'm keenly aware of these sorts of relationships when I think about my dreams. I will think of a situation and immediately be able to identify the input memories that "built" the dream scenario. Usually they involve overlapping themes and concepts, as well as some human specific common targets like "unresolved, emotionally charged, danger, etc" (presumably running through these types of brain neurons provides some sort of advantage for mammals, which makes sense to me).

What an LLM does is essentially create a huge interconnected conceptual web of the universe which is fed, and then uses probably ballistic models to travel through these chains, much like how dreaming does a trance like dance through these conceptual connections. In the former example though, we have optimized the traversal to be as close to a "mock awake human" as possible. If the dream poem is dreary in nature, and Rostov sounds dreary, and you were hearing about the dreary London rain earlier in the day, and you have a childhood memory of reading through a dreary poem that made you very sad, that's the perfect sort of overlapping set of memory synapses for a dream to light up. And when looking back, all you'll see is a strange fantasmic and (usually, not always) frustratingly inaccessible conglomeration of these inputs.

This sort of traversal isn't just used in dreaming though. To some degree we're doing similar things when we do things like creative thinking. The difference is, and this is especially so in "modern" life, we're strongly filtering our thoughts through language first and foremost (and indeed there's a lot of philosophical and scientific work done about how extremely important language is to humanness), but also through basic logic.

LLM's inherent some of the power/magic of language, in that it is deconstructing the relationships between the concepts behind language that contains so much embedded meaning. But they aren't filtering through logic like we do. Well, reasoning models do to some degree, but it is obviously quite rudimentary.

I think it's a good analogy.

Are there really plenty of examples on the internet?

My first thought reading the article was that Atari BASIC is kind of a little specialized. If BASIC is kind of an under-represented language in general on the internet (you know, compared to Javascript, for example) then Atari BASIC has to be a white whale.

> Language evolves, words change, and AI means what it means now. It turns out it’s actually really useful to have an abstraction above the concept of LLMs to talk about the broader set of these types of technologies

I agree that "AI" can be useful as an umbrella term, but using it when referring specifically to the "LLM" subset of AI technologies is not useful. A ton of information about the capabilities and limitations of the system is lost when making that substitution. I understand why marketing departments are pushing everything as "AI" to sell a product but as consumers we should be fighting against that.