It all feels to me like the guys who make videos of using using electric drills to hammer in a nail - Sure, you can do that, but it is the wrong tool for the job. Everyone knows the phrase: "When all you have is a hammer, everything looks like a nail." But we need to also keep in mind the other side of that coin: "When all you have is nails, all you need is a hammer." LLMs are not a replacement for everything that happens to be digital.

If you don't like the results or the process, you have to switch targets or add new intermediates. For example instead of doing description -> implementation, do description -> spec -> plan -> implementation

We have mechanisms for ensuring output from humans, and those are nothing like ensuring the output from a compiler. We have checks on people, we have whole industries of people whose whole careers are managing people, to manage other people, to manage other people.

with regards to predictability LLMs essentially behave like people in this manner. The same kind of checks that we use for people are needed for them, not the same kind of checks we use for software.

The more I use LLMs, the more I find this true. Haskell made me think for minutes before writing one line of code. Result? I stopped using Haskell and went back to Python because with Py I can "think while I code". The separation of thinking|coding phases in Haskell is what my lazy mind didn't want to tolerate.

Same goes with LLMs. I want the model to "get" what I mean but often times (esp. with Codex) I must be very specific about the project scope and spec. Codex doesn't let me "think while I vibe", because every change is costly and you'd better have a good recovery plan (git?) when Codex goes stray.

This is technically true. But unimportant. When I write code in a higher level language and it gets compiled to machine code, ultimately I am testing statically generated code for correctness. I don’t care what type of weird tricks the compiler did for optimizations.

How is that any different than when someone is testing LLM generated C code? I’m still testing C code that isn’t going to magically be changed by the LLM without my intervention anymore than my C code is going to be changed without my recompiling it.

On this latest project I was on, the Python generated code by Codex was “correct” with the happy path. But there were subtle bugs in the distributed locking mechanics and some other concurrency controls I specified. Ironically, those were both caught by throwing the code in ChatGPT in thinking mode.

No one is using an LLM to compute is a number even or odd at runtime.

You could use a badly designed antenna with a horrible VSWR at the end of a coax, and effectively communicate with some portion of the world, by using a tuner, which helps cover up the inefficiencies involved. However, doing so loses signal, in both directions. You can add amplification at the antenna for receive (a pre-amp) and transmit with more power, but eventually the coax will break down, possibly well before the legal limit.

It is far better to use a well designed antenna and matching system at the feed point. It maximizes signal transmission in both directions, by reducing losses as much as possible.

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A compiler matches our cognitive impedance to that of the computer. We don't handle generating opcodes and instruction addresses manually very well. I don't see how an LLM is going to do that any better. Compilers, on the other hand, do it reliably, and very efficiently.

The best cognitive impedance matches happened a while ago, when Visual Basic 6 and Delphi for Windows first came out. You might think LLMs make it easier that that, but you'd be mistaken, for any problem of sufficient complexity.

It’s guessing using the entire sum total of its ingested knowledge, and, it’s reasonable to assume frontier labs are investing heavily into creating and purchasing synthetic & higher quality data.

Judgment is a byproduct of having seen a great many things, only some of which work, and being able to apply that in context.

For most purposes (granted not all) it won’t matter which of the many possible programs you get - as long as it’s usable and does the task, it’ll be fine.

This is why I think the better goal is an abstraction layer that differentiates human decisions from default (LLM) decisions. A sweeping "compiler" locks humans out of the decision making process.

This could be a good way to learn how robust your tests are, and also what accidental complexity could be removed by doing a rewrite. But I doubt that the results would be so good that you could ask a coding agent to regenerate the source code all the time, like we do for compilers and object code.

But for reference, we don't (usually) care which register three compiler uses for which variable, we just care that it works, with no bugs. If the non-dertetminism of LLMs mean the variable is called file, filename, or fileName, file_name, and breaking with convention, why do we care? At the level Claude let's us work with code now, it's immaterial.

Compilation isn't stable. If you clear caches and recompile, you don't get a bit-for-bit exact copy, especially on today's multi-core processors, without doing extra work to get there.

One current idea of mine, is to iteratively make things more and more specific, this is the approach I take with psuedocode-expander ([0]) and has proven generally useful. I think there's a lot of value in the LLM instead of one shot generating something linearly, building from the top down with human feedback, for instance. I give a lot more examples on the repo for this project, and encourage any feedback or thoughts on LLM driven code generation in a more sustainable then vibe-coding way.

[0]: https://github.com/explosion-Scratch/psuedocode-expander/

One of the first things I tried to have an llm do is transpile. These days that works really well. You find an interesting project in python, i'm a js guy, boom js version. Very helpful.

I think it’s more productive to chart all of these systems, LLMs included, on a line of abstraction leakiness. Even disregarding their stochastic nature, I think they’re a much too leaky abstraction to find any use in compilers. There’s a giant mismatch that I think is too big to reconcile.

There are people playing around with straight machine code generation, or integrating ML into the optimisation backend, finally compiling via a translation to an existing language is already a given in vibe coding with agents.

Speaking of which, using agentic runtimes is hardly any different from writing programs, there are some instructions which then get executed just like any other applications, and if it gets compiled before execution or plainly interpreted, becomes a runtime implementation detail.

Are we there yet without hallucinations?

Not yet, however the box is already open, and there are enough people trying to make it happen.

The obvious has been stated.

  0. "Make something cool out of this insane amount of energy." (temp: 10^42 Kelvin)
  1. He slept for a while.
  2. Datacenter exploded His realm.
  3. ~380 000 years passed and fiat lux.
  4. ~13 billions years passed and here we are.
  5. JMP 0.

I think this is an interesting development, because we (linguists and logicians in particular) have spent a long time developing a highly specified language that leaves no room for ambiguity. One could say that natural language was considered deficient – and now we are moving in the exact opposite direction.

>From one gut feeling I derive much consolation: I suspect that machines to be programmed in our native tongues —be it Dutch, English, American, French, German, or Swahili— are as damned difficult to make as they would be to use.

yet nobody complained about this

in fact engineers appreciate that, "we are not replaceable code monkeys cogs in the machine as management would like"

Why? Because new languages have an IR in their compilation path?

This feels like the same debate assembly programmers had about C in the 60s. "You don’t understand what the compiler is doing, therefore it’s dangerous". Eventually we realised the important thing isn’t how the code was authored but whether the behaviour is correct, testable, and maintainable.

If code generated by an LLM:

  - passes a real test suite (not toy tests),
  - meets performance/security constraints,
  - goes through review like any other change,

The real risk isn’t "LLMs as compilers", it’s letting changes bypass verification and ownership. We solved that with C, with large dependency trees, with codegen tools. Same playbook applies here.

If you give expected input and get expected output, why does it matter how the code was written?

Using LLMs to do something like what a compiler can already do is also modelling LLMs as infinite rather than finite. In fact in this particular situation not only are they finite, they're grotesquely finite, in particular, they are expensive. For example, there is no world where we just replace our entire infrastructure from top to bottom with LLMs. To see that, compare the computational effort of adding 10 8-digit numbers with an LLM versus a CPU. Or, if you prefer something a bit less slanted, the computational costs of serving a single simple HTTP request with modern systems versus an LLM. The numbers run something like LLMs being trillions of times more expensive, as an opening bid, and if the AIs continue to get more expensive it can get even worse than that.

For similar reasons, using LLMs as a compiler is very unlikely to ever produce anything even remotely resembling a payback versus the cost of doing so. Let the AI improve the compiler instead. (In another couple of years. I suspect today's AIs would find it virtually impossible to significatly improve an already-optimized compiler today.)

Moreover, remember, oh, maybe two years back when it was all the rage to have AIs be able to explain why they gave the answer they did? Yeah, I know, in the frenzied greed to be the one to grab the money on the table, this has sort of fallen by the wayside, but code is already the ultimate example of that. We ask the LLM to do things, it produces code we can examine, and the LLM session then dies away leaving only the code. This is a good thing. This means we can still examine what the resulting system is doing. In a lot of ways we hardly even care what the LLM was "thinking" or "intending", we end up with a fantastically auditable artifact. Even if you are not convinced of the utility of a human examining it, it is also an artifact that the next AI will spend less of its finite resources simply trying to understand and have more left over to actually do the work.

We may find that we want different programming languages for AIs. Personally I think we should always try to retain that ability for humans to follow it, even if we build something like that. We've already put the effort into building AIs that produce human-legible code and I think it's probably not that great a penalty in the long run to retain that. At the moment it is hard to even guess what such a thing would look like, though, as the AIs are advancing far faster than anyone (or any AI) could produce, test, prove out, and deploy such a language, against the advantage of other AIs simply getting better at working with the existing coding systems.

Stop this. This is such a stupid way way of describing mistakes from AI. Please try to use the confusion matrix or any other way. If you're going to try and make arguments, it's hard to take them seriously if you keep regurgitating that LLM's hallucinate. It's not a well defined definition so if you continually make this your core argument, it becomes disingenuous.

You can see it clearly if you just translate the article's expensive vocabulary into plain English. When the author writes, 'When you hand-build, the space of possibilities is explored through design decisions you’re forced to confront,' they are just saying, 'When you write code yourself, you have to choose how to write it.' When they claim, 'contextuality is dominated by functional correctness,' they just mean, 'Usually, we just care if the code works.' When they warn about 'inviting us to outsource functional precision itself,' they really mean, 'LLMs let you be lazy.' And finaly, 'strengthening the will to specify,' is just a dramatic way of saying, 'We need to write better requirements.' It is obscurantism plain and simple. using complexity to hide the fact that the insight is trivial.

But that is just an estethical problem to me. Worse. The argument collapses entirely when you look at the logical leap between the premises.

The author basically argues that because Natural Language is vague, engineers will inevitably stop caring about the details and just accept whatever reasonable output the AI gives. This is pure armchair psychology. It assumes that just because the tool allows for vagueness, professionals will suddenly abandon the concept of truth or functional requirements. That is a massive, unsubstantiated jump.

If we use fuzzy matching to find contacts on our phones all the time. Just because the search algorithm is imprecise doesn't mean we stop caring if we call the right person. We don't say, 'Well, the fuzzy match gave me Bob instead of Bill, I guess I'll just talk to Bob now.' The hard constraint, the functional requirement of talking to the specific person you need, remains absolute. Similarly, in software, the code either compiles and passes the tests, or it doesn't. The medium of creation might be fuzzy, but the execution environment is binary. We aren't going to drift into accepting broken banking software just because the prompt was in English.

This entire essay feels like those social psychology types that now have been thoroughly been discredited by the replication crisis in psychology. The ones who are where concerned with dazzling people with verbal skills than with being right. It is unnecessarily complex, relying on projection of dreamt up concepts and behavior, rather than observation. THIS tries to sound profound by turning a technical discussion into a philosophical crisis, but underneath the word salad, it is not just shallow, it is wrong.