People are sticking up for LLMs here and that's cool.
I wonder, what if you did the opposite? Take a project of moderate complexity and convert it from code back to natural language using your favorite LLM. Does it provide you with a reasonable description of the behavior and requirements encoded in the source code without losing enough detail to recreate the program? Do you find the resulting natural language description is easier to reason about?
I think there's a reason most of the vibe-coded applications we see people demonstrate are rather simple. There is a level of complexity and precision that is hard to manage. Sure, you can define it in plain english, but is the resulting description extensible, understandable, or more descriptive than a precise language? I think there is a reason why legalese is not plain English, and it goes beyond mere gatekeeping.
> Do you find the resulting natural language description is easier to reason about?
An example from an different field - aviation weather forecasts and notices are published in a strongly abbreviated and codified form. For example, the weather at Sydney Australia now is:
It's almost universal that new pilots ask "why isn't this in words?". And, indeed, most flight planning apps will convert the code to prose.
But professional pilots (and ATC, etc) universally prefer the coded format. Is is compact (one line instead of a whole paragraph), the format well defined (I know exactly where to look for the one piece I need), and it's unambiguous and well defined.
Same for maths and coding - once you reach a certain level of expertise, the complexity and redundancy of natural language is a greater cost than benefit. This seems to apply to all fields of expertise.
It shows at least one lengthy and quite wordy example of how an equation would have been stated, then contrasts it in the "new" symbolic representation (this was one of the first major works to make use of Robert Recorde's development of the equals sign).
> Same for maths and coding - once you reach a certain level of expertise, the complexity and redundancy of natural language is a greater cost than benefit. This seems to apply to all fields of expertise.
And as well as these points, ambiguity. A formal specification of communication can avoid ambiguity by being absolute and precise regardless of who is speaking and who is interpreting. Natural languages are riddled wth inconsistencies, colloquialisms, and imprecisions that can lead to misinterpretations by even the most fluent of speakers simply by nature of natural languages being human language - different people learn these languages differently and ascribe different meanings or interpretations to different wordings, which are inconsistent because of the cultural backgrounds of those involved and the lack of a strict formal specification.
An interesting perspective on this is that language is just another tool on the job. Like any other tool, you use the kind of language that is most applicable and efficient. When you need to describe or understand weather conditions quickly and unambiguously, you use METAR. Sure, you could use English or another natural language, but it's like using a multitool instead of a chef knife. It'll work in a pinch, but a tool designed to solve your specific problem will work much better.
Not to slight multitools or natural languages, of course - there is tremendous value in a tool that can basically do everything. Natural languages have the difficult job of describing the entire world (or, the experience of existing in the world as a human), which is pretty awesome.
And different natural languages give you different perspectives on the world, e.g., Japanese describes the world from the perspective of a Japanese person, with dedicated words for Japanese traditions that don't exist in other cultures. You could roughly translate "kabuki" into English as "Japanese play", but you lose a lot of what makes kabuki "kabuki", as opposed to "noh". You can use lots of English words to describe exactly what kabuki is, but if you're going to be talking about it a lot, operating solely in English is going to become burdensome, and it's better to borrow the Japanese word "kabuki".
you guys are not wrong. explain any semi complez program, you will instantly resort to diagrams, tables, flow charts etc. etc.
ofcourse, you can get your LLM to be bit evil in its replies, to help you truly. rather than to spoon feed you an unhealthy diet.
i forbid my LLM to send me code and tell it to be harsh to me if i ask stupid things. stupid as in, lazy questions. send me the link to the manual/specs with an RTFM or something i can digest and better my undertanding. send links not mazes of words.
now i can feel myself grow again as a programmer.
as you said. you need to build expertise, not try to find ways around it.
with that expertise you can find _better_ ways. but for this, firstly, you need the expertise.
On the other hand "a folder that syncs files between devices and a server" is probably a lot more compact than the code behind Dropbox. I guess you can have both in parallel - prompts and code.
You can see the same phenomenon playing a roguelike game.
They traditionally have ASCII graphics, and you can easily determine what an enemy is by looking at its ASCII representation.
For many decades now graphical tilesets have been available for people who hate the idea of ASCII graphics. But they have to fit in the same space, and it turns out that it's very difficult to tell what those tiny graphics represent. It isn't difficult at all to identify an ASCII character rendered in one of 16 (?) colors.
The point of LLM is to enable "ordinary people" to write software. This movement is along with "zero code platform", for example. Creating algorithms by drawing block-schemes, by dragging rectangles and arrows. This is old discussion and there are many successful applications of this nature. LLM is just another attempt to tackle this beast.
Professional developers don't need this ability indeed. Most professional developers, who had to deal with zero code platforms, probably would prefer to just work with ordinary code.
I'm not so sure it's about precision rather than working memory. My presumption is people struggle to understand sufficiently large prose versions for the same reason a LLM would struggle working with larger prose versions: people have limited working memory. The time needed to reload info from prose is significant. People reading large text works will start highlighting and taking notes and inventing shorthand forms in their notes. Compact forms and abstractions help reduce demands for working memory and information search. So I'm not sure it's about language precision.
Another important difference is reproducibility. With the same program code, you are getting the same program. With the same natural-language specification, you will presumably get a different thing each time you run it through the "interpreter". There is a middle ground, in the sense that a program has implementation details that aren't externally observable. Still, making the observable behavior 100% deterministic by mere natural-language description doesn't seem a realistic prospect.
Language can carry tremendous amounts of context. For example:
> I want a modern navigation app for driving which lets me select intersections that I never want to be routed through.
That sentence is low complexity but encodes a massive amount of information. You are probably thinking of a million implementation details that you need to get from that sentence to an actual working app but the opportunity is there, the possibility is there, that that is enough information to get to a working application that solves my need.
And just as importantly, if that is enough to get it built, then “can I get that in cornflower blue instead” is easy and the user can iterate from there.
But it doesn't 'carry context' ; it's just vague and impossible to implement what you have in mind. And that's the problem; You assume people live in your reality, I assume mine, LLMs have some kind of mix between us and we will get 3 very different apps, none of which will be useful from that line alone. I like that line to be expanded with enough context to have an idea what you actually need to have built and I am quite sure pseudocode (or actual code) will be much shorter than a rambling english description you can come up with; most of which (unless it's logic language) will have enough unambiguous context to implement.
So sure, natural language is great for spitballing ideas, but after that it's just guessing what you actually want to get done.
You call it context or information but I call it assumptions. There are a ton assumptions in that sentence that an LLM will need to make in order to take that and turn it into a v1. I’m not sure what resulting app you’d get but if you did get a useful starting point, I’d wager the fact that you chose a variation of an existing type of app helped a lot. That is useful, but I’m not sure this is universally useful.
Sure but we build (leaky) abstractions, and this is even happens in legal texts.
Asking an llm to build a graphical app in assembly from an ISA and a driver for the display would give you nothing.
But with a mountain of abstractions then it can probably do it.
This is not to defend an LLM more to say I think that by providing the right abstractions (reusable components) then I do think it will get you a lot closer.
Being doing toy-examples of non-trivial complexity. Architecting the code so context is obvious and there are clear breadcrumbs everywhere is the key. And the LLM can do most of this. Prototype-> refactor/cleanup -> more features -> refactor / cleanup add architectural notes.
If you know what a well architected piece of code is supposed to look like, and you proceed in steps, LLM gets quite far as long as you are handholding it. So this is usable for non-trivial _familiar_ code where typing it all would be slower than prompting the llm. Maintaining LLM context is the key here imo and stopping it when you see weird stuff. So it requires you act as thr senior partner PR:ing everyhting.
Yeah, my theory on this has always been that a lot of programming efficiency gains have been the ability to unambiguously define behavior, which mostly comes from drastically restricting the possible states and inputs a program can achieve.
The states and inputs that lawyers have to deal with tend to much more vague and imprecise (which is expected if you're dealing with human behavior and not text or some other encodeable input) and so have to rely on inherently ambiguous phrases like "reasonable" and "without undue delay."
This opens an interesting possibility for a purely symbol-based legal code. This would probably improve clarity when it came to legal phrases that overlap common English, and you could avoid ambiguity when it came to language constructs, like in this case[1], where some drivers were losing overtime pay because of a comma in the overtime law.
I've thought about this quite a bit. I think a tool like that would be really useful. I can imagine asking questions like "I think this big codebase exposes a rest interface for receiving some sort of credit check object. Can you find it and show me a sequence diagram for how it is implemented?"
The challenge is that the codebase is likely much larger than what would fit into a single codebase. IMO, the LLM really needs to be taught to consume the project incrementally and build up a sort of "mental model" of it to really make this useful. I suspect that a combination of tool usage and RL could produce an incredibly useful tool for this.
"Sure, you can define it in plain english, but is the resulting description extensible, understandable, or more descriptive than a precise language? I think there is a reason why legalese is not plain English, and it goes beyond mere gatekeeping."
Is this suggesting the reason for legalese is to make documents more "extensible, understable or descriptive" than if written in plain English.
What is this reason that the parent thinks legalese is used that "goes beyond gatekeeping".
Plain English can be every bit as precise as legalese.
It is also unclear that legalese exists for the purpose of gatekeeping. For example, it may be an artifact that survives based on familiarity and laziness.
Law students are taught to write in plain English.
> Plain English can be every bit as precise as legalese.
If you attempt to make "plain English" as precise as legalese, you will get something that is basically legalese.
Legalese does also have some variables, like "Party", "Client", etc. This allows for both precision -- repeating the variable name instead of using pronouns or re-identifying who you're talking about -- and also for reusability: you can copy/paste standard language into a document that defines "Client" differently, similar to a subroutine.
What you're describing is decontextualization. A sufficiently powerful transformer would theoretically be able recontextualize a sufficiently descriptive natural language specification. Likewise, the same or an equivalently powerful transformer should be able to fully capture the logic of a complicated program. We just don't have sufficient transformers yet.
I don't see why a complete description of the program's design philosophy as well as complete descriptions of each system and module and interface wouldn't be enough. We already produce code according to project specification and logically fill in the gaps by using context.
“Fill in the gaps by using context” is the hard part.
You can’t pre-bake the context into an LLM because it doesn’t exist yet. It gets created through the endless back-and-forth between programmers, designers, users etc.
the vibe coding seems a lot like the dream of using UML, but in a distinctly different direction, and how in theory (and occasional practice) you can create a two way street, most often these things are one way conversions and while we all desire some level of two way dependency and continual integration to make certain aspects of coding (documentation, testing) to be up to date, the reality is that the generative code aspect always breaks and you're always going to be left with the raw products of these tools and it's rarely going to be a cycle of code -> tool -> code. And thus the ultimate value beyond the bootstrap is lose.
We're still going to have AI tools, but seriously complex applications, the ones we pay money for, arn't going to yield many LLM based curation strategies. There will probably be some great documentation and testing ones, but the architetural-code paradigm isnt going to yield any time soon.
> > there is a reason why legalese is not plain English, and it goes beyond mere gatekeeping.
> unfortunately they're not in any kind of formal language either
Most formulas made of fancy LaTeX symbols you find in math papers aren't a formal language either. They usually can't be mechanically translated via some parser to an actual formal language like Python or Lean. You would need an advanced LLM for that. But they (the LaTeX formulas) are still more precise than most natural language. I assume something similar is the case with legalese.
This reminded me of this old quote from Hal Abelson:
"Underlying our approach to this subject is our conviction that "computer science" is not a science and that its significance has little to do with computers. The computer revolution is a revolution in the way we think and in the way we express what we think. The essence of this change is the emergence of what might best be called procedural epistemology—the study of the structure of knowledge from an imperative point of view, as opposed to the more declarative point of view taken by classical mathematical subjects. Mathematics provides a framework for dealing precisely with notions of "what is". Computation provides a framework for dealing precisely with notions of "how to"."
This is key: computation is about making things happen. Coding with an LLM adds a level of abstraction but the need for precision and correctness of the "things that happen" doesn't go away. No matter how many cool demos and "coding is dead" pronouncements because AI - and the demos are very cool - the bulk of the work moves to the pre- and post-processing and evals with AI. To the extent that it makes programming more accessible it's a good thing, but can't really replace it.
Finally someone put it this way! Natural language has embedded limitations that stem from our own mental limitations -the human mind thinks sometimes too abstract or too specific things, and misses important details or generalizations.
As a programmer, I know first hand that the problems or even absurdities of some assignments only become apparent after one has begun implement the code as code, i.e. as strict symbolisms.
Not to mention that it often takes more time to explain something accurately in natural language than it takes to just write the algorithm as code.
Yes! I have a certain personality preference for abstractions and tend to understand things in an abstract manner which is extremely difficult for me to articulate in natural language.
This is one of the best comments I've read on this site in a long while.
A single, crude, statement of fact slaying the work of a million typewriter monkeys spewing out random characters thinking they're actually writing the Shakespeare novel, lmao.
We need realistic expectations for the limitations of LLMs as they work today. Philosophically, natural language is imperfect at communicating ideas between people, which is its primary purpose! How often do you rewrite sentences, or say "actually what I meant was...", or rephrase your emails before pressing Send? We are humans and we rarely get things perfect on the first try.
And now we're converting this imperfect form of communication (natural language) into a language for machines (code), which notoriously do exactly what you say, not what you intend.
NLP is massively, and I mean massively, beneficial to get you started on the right path to writing an app/script/etc. But at the end of the day it may be necessary to refactor things here and there. The nice thing is you don't have to be a code ninja to get value out of LLMs, but it's still helpful and sometimes necessary.
/s: that’s because we haven’t gone far enough. People use natural language to generate computer programs. Instead, they should directly run prompts.
“You are the graphics system, an entity that manages what is on the screen. You can receive requests from all programs to create and destroys “windows”, and further requests to draw text, lines, circles, etc. in a window created earlier. Items can be of any colour.
You also should send more click information to whomever created the window in which the user clicked the mouse.
There is one special program, the window manager, that can tell you what windows are displayed where on any of the monitors attached to the system”
and
“you are a tic-tac-toe program. There is a graphics system, an entity that manages what is on the screen. You can command it to create and destroys “windows”, and to draw text, lines, circles, etc. in a window created earlier. Items can be of any colour.
The graphics you draw should show a tic-tac-toe game, where users take turn by clicking the mouse. If a user wins the game, it should…
Add ads to the game, unless the user has a pay-per-click subscription”
That should be sufficient to get a game running…
To save it, you’d need another prompt:
”you are a file system, an entity that persists data to disk…”
You also will want
”you are a multi-tasking OS. You give multiple LLMs the idea that they have full control over a system’s CPU and memory. You…”
I look forward to seeing this next year in early April.
> Machine code, with its absence of almost any form of redundancy, was soon identified as a needlessly risky interface between man and machine. Partly in response to this recognition so-called "high-level programming languages" were developed, and, as time went by, we learned to a certain extent how to enhance the protection against silly mistakes. It was a significant improvement that now many a silly mistake did result in an error message instead of in an erroneous answer.
I feel that we’ve collectively jumped into programming with LLMs too quickly. I really liked how Rust has iterated on pointing out “silly mistakes” and made it much more clear what the fix should be. That’s a much more favorable development for me as a developer. I still have the context and understanding of the code I work on while the compiler points out obvious errors and their fixes. Using an LLM feels like a game of semi-intelligent guessing on the other hand. Rust’s compiler is the master teaching the apprentice. LLMs are the confident graduate correcting the master. I greatly prefer Rust’s approach and would like to see it evolved further if possible.
Natural language is poor medium at communicating rules and orders. The current state of affair in US is a prime example.
We are still debating what some laws and amendments mean. The meaning of words change over time, lack of historical context, etc.
I would love natural language to operate machines, but I have been programming since mid 80's and the stubbornness of the computer languages (from BASIC, to go) strikes a good balance, and puts enough responsibility on the emitter to precisely express what he wants the machine to do.
> It was a significant improvement that now many a silly mistake did result in an error message instead of in an erroneous answer. (And even this improvement wasn't universally appreciated: some people found error messages they couldn't ignore more annoying than wrong results, and, when judging the relative merits of programming languages, some still seem to equate "the ease of programming" with the ease of making undetected mistakes.)
If I didn't know who wrote this it would seem like a jab directly at people who dislike Rust.
Rust? Since when is Rust the pinnacle of static type safety?
After I've worked for some time with a language that can express even stronger invariants in types than Rust (Scala) I don't see that property anymore as clear win regardless circumstances. I don't think any more "stronger types == better, no matter what".
You have a price to pay for "not being allowed to do mistakes": Explorative work becomes quite difficult if the type system is really rigid. Fast iteration may become impossible. (Small changes may require to re-architecture half your program, just to make the type system happy again![1])
It's a trade-off. Like with everything else. For a robust end product it's a good thing. For fast experimentation it's a hindrance.
> You have a price to pay for "not being allowed to do mistakes": Explorative work becomes quite difficult
This is a huge deal for me.
At the beginning of most "what if...?" exercises, I am just trying to get raw tuples of information in and out of some top-level-program logic furnace for the first few [hundred] iterations. I'll likely resort to boxing and extremely long argument lists until what I was aiming for actually takes hold.
I no longer have an urge to define OOP type hierarchies when the underlying domain model is still a vague cloud in my head. When unguided, these abstractions feel like playing Minecraft or Factorio.
As a person who dislikes rust, the problem is the error messages when there's no error -- quite a different problem. The rust type system is not an accurate model of RAM, the CPU and indeed, no device.
He's here talking about interpreted languages.
He's also one of those mathematicians who are now called computer scientists whose 'algorithms' are simple restatements of mathematics and require no devices. A person actively hostile, in temperament, to the embarrassing activity of programming an actual computer.
Using natural language to specify and build an application is not unlike having a game design document before you actually start prototyping your game. But once you have implemented the bulk of what you wanted, the implementation becomes the reference and you usually end up throwing away the GDD since it's now out of sync with the actual game.
Insisting that for every change one should go read the GDD, implement the feature and then sync back the GDD is cumbersome and doesn't work well in practice. I've never seen that happen.
But if there ever comes a time when some AI/LLM can code the next version of Linux or Windows from scratch based on some series of prompts, then all bets are off. Right now it's clearly not there yet, if ever.
I somewhat disagree with this. In real life, say in some company, the inception of an idea for a new feature is made in the head of some business person. This person will not speak any formal language. So however you turn it, some translation from natural language to machine language will have to be done to implement the feature.
Typically the first step, translation from natural to formal language, will be done by business analysts and programmers. But why not try to let computers help along the way?
Computers can and should help along the way, but Dijkstra's argument is that a) much of the challenge of human ideas is discovered in the act of converting from natural to formal language and b) that this act, in and of itself, is what trains our formal logical selves.
So he's contesting not only the idea that programs should be specified in natural language, but also the idea that removing our need to understand the formal language would increase our ability to build complex systems.
It's worth noting that much of the "translation" is not translation, but fixing the logical ambiguities, inconsistencies and improper assumptions. Much of it can happen in natural language, if we take Dijkstra seriously, precisely because programmers at the table who have spent their lives formalizing.
There are other professions which require significant formal thinking, such as math. But also, the conversion of old proofs into computer proofs has lead us to discover holes and gaps in many well accepted proofs. Not that much has been overturned, but we still do t have a complete proof for Fermats last theorem [1].
The first step isn't from natural language to formal language. It's from the idea in your head into natural language. Getting that step right in a way that a computer could hope to turn into a useful thing is hard.
>It's from the idea in your head into natural language. Getting that step right in a way that a computer could hope to turn into a useful thing is hard.
The "inside the head" conversion step would be more relevant in the reply to the gp if the hypothetical AI computer would be hooked up directly to brain implants like neuralink, functional MRI scans, etc to translate brain activity to natural language or programming language code.
But today, human developers who are paid to code for business people are not translating brain implant output signals. (E.g. Javascript programmers are not translating raw electrical waveforms[1] into React code.)
Instead, they translate from "natural language" specifications of businesspeople to computer code. This layer of translation is more tractable for future AI computers even though natural language is more fuzzy and ambiguous. The language ambiguity in business requirements is unavoidable but it still hasn't stopped developers from somehow converting it into concrete non-ambiguous code.
Without descending fully into epistemology, I tend to think that there is no proper "idea" in your head before it's phrased in language - the act of initially describing something in natural language *is* the act of generating it.
That's pretty much the whole basis of Domain Driven Design. The core message is to get to an Ubiquitous Language which is the formalization of the business jargon (pretty much a glossary). From which the code can then be derived naturally.
The only issue I have with trusting a computer to do so much is that it doesn't necessarily have the long term vision or intuition some humans might have for the direction of the software product. There's so much nuance to the connection between a business need and getting it into software, or maybe I am overthinking it :D
I don't think youre fully comprehending Dijkstra's argument. He's not saying to not use tool to help with translation, he is saying that not thinking in terms of formal symbols HURTS THINKING. Your ideas are worse if you don't think in formal systems. If you don't treat your thoughts as formal things.
In your example, he has no opinion on how to translate the idea of a "business person" because in his view the ideas of the "business person" are already shallow and bad because they don't follow a formalism. They are not worth translating.
If that's correct, then it's very falsifiable. If a businessperson says "there's a gap in the market - let's build X" they will be following a formalism at their level of detail. They see the market, the interactions between existing products and customers, and where things might be going.
Just because they can't spell it out to the nth degree doesn't matter. Their formalism is "this is what the market would like".
Having an LLM then tease out details - "what should happen in this case" would actually be pretty useful.
Because then you don't know what the computer's doing. The whole point of this article was that there is value in the process of writing your ideas out formally. If you "let computers help you along the way", you'll run straight into the issue of needing an increasingly formal natural language to get sufficiently good results from the machine.
Forth, PostScript and Assembly are the "natural" programming languages from the perspective of how what you express maps to the environment in which the code executes.
The question is "natural" to whom, the humans or the computers?
AI does not make human language natural to computers. Left to their own devices, AIs would invent languages that are natural with respect to their deep learning architectures, which is their environment.
There is always going to be an impedance mismatch across species (humans and AIs) and we can't hide it by forcing the AIs to default to human language.
Modern programming already is very, very far from strict obedience and formal symbolism. Most programmers these days (myself included!) are using libraries, frameworks, and other features that mean what they are doing in practice is wielding sky-high abstractions, gluing things together they do not (and can not) fully understand the inner workings of.
If I create a website with Node.js, I’m not manually managing memory, parsing HTTP requests byte-by-byte, or even attempting to fully grasp the event loop’s nuances. I’m orchestrating layers of code written by others, trusting that these black boxes will behave as advertised according to my best, but deeply incomplete, understanding of them.
I'm not sure what this means for LLMs programming, but I already feel separated from the case Dijkstra lays out.
> Modern programming already is very, very far from strict obedience and formal symbolism
Difficult to sort this out with what follows.
Consider group theory. A group G is a set S with an operator * that supports an identity, closure, and an inverse. With that abstraction comes a hefty amount of power. In some sense, a group is akin to a trait on some type, much like how a class in Java can implement or extend Collection. (Consider how a ring ‘extends’ a group.)
I’d posit frameworks and libraries are no different in terms of formal symbolism from the math structure laid out above. Maybe the interfaces are fuzzy and the documentation is shoddy, but there’s still a contract we use to reason about the tool at hand.
> I’m not manually managing memory, parsing HTTP requests byte-by-byte
If I don’t reprove Peano’s work, then I’m not really doing math?
Natural language is pretty good for describing the technical requirements for the complex system, though. I.e. not the current code implementation, but why the current code implementation is selected vs other possible implementations. Not what code do, but what it is expected to do. Basically, most of the missing parts, that live in Jira-s, instead of your repo. It is also good, at allowing better refactoring capabilities, when all your system is described by outside rules, which could be enforced on the whole codebase. We just use programming languages, because it is easier to use in automated/computer context (and was the only way to use, to be honest, before all the LLM stuff). Though, while it gives us non-ambiguity on the local scale, it stops working on the global scale, the first moment person went and copy-pasted part of the code. Are you sure that part follows all the high-level restrictions we should to follow and is correct program? It is program that would run, when compile, but definition of run is pretty loose. In C++ program that corrupts all the memory is also runnable.
Reminds me of another recurring idea of replacing code with flowcharts. First I've seen that idea coming from some unknown Soviet professor from 80s, and then again and again from different people from different countries in different contexts. Every time it is sold as a total breakthrough in simplicity and also every time it proves to be a bloat of complexity and a productivity killer instead.
Or weak typing. How many languages thought that simplifying strings and integers and other types into "scalar", and making any operation between any operands meaningful, would simplify the language? Yet every single one ended up becoming a total mess instead.
Or constraint-based UI layout. Looks so simple, so intuitive on simple examples, yet totally failing to scale to even a dozen of basic controls. Yet the idea keeps reappearing from time to time.
Or an attempt at dependency management by making some form of symlink to another repository e.g. git modules, or CMake's FetchContent/ExternalProject? Yeah, good luck scaling that.
Maybe software engineering should have some sort of "Hall of Ideas That Definitely Don't Work", so that young people entering the field could save their time on implementing one more incarnation of an already known not good idea.
> Maybe software engineering should have some sort of "Hall of Ideas That Definitely Don't Work", so that young people entering the field could save their time on implementing one more incarnation of an already known not good idea.
I'm deeply curious to know how you could easily and definitively work out what is and is not an idea that "Definitely Don't Work"
Mathematics and Computer Science seem to be littered with unworkable ideas that have made a comeback when someone figured out how to make them work.
Well, "Hall of Ideas That Are So Difficult To Make Work Well That They May Not In Fact Be Much Use" doesn't roll off the tongue as smoothly.
What this Hall could contain, for each idea, is a list of reasons why the idea has failed in the past. That would at least give future Quixotes something to measure their efforts by.
Constraint-based layout works, but you need a serious constraint engine, such as the one in the sketch editors of Autodesk Inventor or Fusion 360, along with a GUI to talk to it. Those systems can solve hard geometry problems involving curves, because you need that when designing parts.
Flowchart-based programming scales badly. Blender's game engine (abandoned) and Unreal Engine's "blueprints" (used only for simple cases) are examples.
Not sure if you’re talking about DRAKON here, but I love it for documentation of process flows.
It doesn’t really get complicated, but you can very quickly end up with drawings with very high square footage.
As a tool for planning, it’s not ideal, because “big-picture” is hard to see. As a user following a DRAKON chart though, it’s very, very simple and usable.
> Maybe software engineering should have some sort of "Hall of Ideas That Definitely Don't Work", so that young people entering the field could save their time on implementing one more incarnation of an already known not good idea.
FWIW, neural networks would be in that pool until relatively recently.
If we change "definitely don't work" to "have the following so far insurmountable challenges", it addresses cases like this. Hardware scaling limitations on neural networks have been known to be a limitation for a long time - Minsky and Papert touched in this in Perceptrons in 1969.
The Hall would then end up containing a spectrum ranging from useless ideas to hard problems. Distinguishing between the two based on documented challenges would likely be possible in many cases.
For young engineers, it is a good thing to spend time implementing what you call "bad ideas". In the worst-case, they learn from their mistake and gain valuable insight into the pitfalls of such ideas. In the best case, you can have a technological breakthrough as someone finds a way to make such an idea work.
Of course, it's best that such learning happens before one has mandate to derail the whole project.
Most popular dependency management systems literally linking to a git sha commit (tag), see locks file that npm/rebar/other tool gives you. Just in a recursive way.
They do way more than that. For example they won't allow you to have Foo-1 that depends on Qux-1 and Bar-1 that depends on Qux-2 where Qux-1 and Qux-2 are incompatible and can't be mixed within the same static library or assembly. But may allow it if mixing static-private Qux inside dynamic Foo and Bar and the dependency manager is aware of that.
A native submodule approach would fail at link time or runtime due to attempt to mix incompatible files in the same build run. Or, in some build systems, simply due to duplicate symbols.
That "just in a recursive way" addition hides a lot of important design decisions that separate having dependency manager vs. not having any.
> Or weak typing. How many languages thought that simplifying strings and integers and other types into "scalar", and making any operation between any operands meaningful, would simplify the language? Yet every single one ended up becoming a total mess instead.
Yet JavaScript and Python are the most widely used programming languages [1]. Which suggests your analysis is mistaken here.
This is recurring topic indeed. I remember it was hot topic at least two times, when ALM tools were introduced (e.g. Borland ALM suite - https://www.qast.com/eng/product/develop/borland/index.htm), next when BPML language become popular - processes were described by the "marketing" and the software was, you know, generated automatically.
All this went out of fashion, leaving some good stuff that was built at that time (remaining 95% was crap).
Today's "vibe coding" ends when Chat GPT and alikes want to call on some object a method that does not exist (but existed in 1000s of other objects LLM was trained with, so should work here). Again, we will be left with the good parts, the rest will be forgotten and we will move to next big thing.
I read this when I was younger, but I only now get it, and realize how true it all is.
13) Humans writing code is an inherently flawed concept. Doesn't matter what form the code takes. Machine code, assembly language, C, Perl, or a ChatGPT prompt. It's all flawed in the same way. We have not yet invented a technology or mechanism which avoids it. And high level abstraction doesn't really help. It hides problems only to create new ones, and other problems simply never go away.
21) Loosely coupled interfaces made our lives easier because it forced us to compartmentalize our efforts into something manageable. But it's hard to prove that this is a better outcome overall, as it forces us to solve problems in ways that still lead to worse outcomes than if we had used a simpler [formal] logic.
34) We will probably end up pushing our technical abilities to the limit in order to design a superior system, only to find out in the end that simpler formal logic is what we needed all along.
55) We're becoming stupider and worse at using the tools we already have. We're already shit at using language just for communicating with each other. Assuming we could make better programs with it is nonsensical.
For a long time now I've been upset at computer science's lack of innovation in the methods we use to solve problems. Programming is stupidly flawed. I've never been good at math, so I never really thought about it before, but math is really the answer to what I wish programming was: a formal system for solving a problem, and a formal system for proving that the solution is correct. That's what we're missing from software. That's where we should be headed.
> Remark. As a result of the educational trend away from intellectual discipline, the last decades have shown in the Western world a sharp decline of people's mastery of their own language: many people that by the standards of a previous generation should know better, are no longer able to use their native tongue effectively, even for purposes for which it is pretty adequate.
Dijkstra also advocated for proving the correctness of imperative code using the composition of a set of simple rules, and most programmers ignore that aspect of his work too.
Seeing as much of the discussion here is about LLMs, not just the shortcomings of natural language as a programming language, another LLM-specific aspect is how the LLM is interpreting the natural language instructions it is being given...
One might naively think that the "AI" (LLM) is going to apply it's intelligence to give you the "best" code in response to your request, and in a way it is, but this is "LLM best" not "human best" - the LLM is trying "guess what's expected" (i.e. minimize prediction error), not give you the best quality code/design per your request. This is similar to having an LLM play chess - it is not trying to play what it thinks is the strongest move, but rather trying to predict a continuation of the game, given the context, which will be a poor move if it thinks the context indicates a poor player.
With an RL-trained reasoning model, the LLM's behavior is slightly longer horizon - not just minimizing next token prediction errors, but also steering the output in a direction intended to match the type of reasoning seen during RL training. Again, this isn't the same as a human, applying their experience to achieve (predict!) a goal, but arguably more like cargo-cult reasoning - following observed patterns of reasoning in the training set, without the depth of understanding and intelligence to know if this is really applicable in the current context, nor with the ability to learn from it's mistakes when it is not.
So, while natural language itself is of course too vague to program in, which is part of the reason that we use programming languages instead, it's totally adequate as a way to communicate requirements/etc to an expert human developer/analyst, but when communicating to an LLM instead of a person, one should expect the LLM to behave as an LLM, not as a human. It's a paperclip maximizer, not a human-level intelligence.
Dijkstra's insights on the importance of simplicity and elegance in programming are timeless. His emphasis on the process of abstraction and the value of clear, concise code is as relevant today as it was in 1978. A thought-provoking read for any programmer striving to improve their craft.
One of the most challenging aspects in my career has been: communication.
This is largely because it leaves chance for misinterpretation or miscommunication.
Programming languages eliminated misinterpretation and made miscommunication easier to notice through errors.
Programming language enables micromanaging proactively, I specify the instructions before they run.
I often find myself micromanaging retroactively with LLMs, until I reach the path I am looking for.
What needs to be done can and is almost always described in natural language.
Whether that is feasible is a different question (https://xkcd.com/1425/), but also can be described in natural language.
Here is something I tried with o3-mini:
> Create a program that takes an input image and can tell if there is a bird in it.
> ChatGPT said:
> Reasoned for 38 seconds
> Below is an example in Python that uses TensorFlow’s Keras API and a pre-trained ResNet50 model to classify an input image. The code loads an image, preprocesses it to the required size, obtains the top predictions, and then checks if any of the top labels appear to be a bird. You can expand the list of bird keywords as needed for your application.
> python code that works
If you take the critical view, you can always find a way to find an exception that will fail. I can see many happy cases which will just work most of the time, even with the currently available technology. Most of the programming work done today is putting libraries and api services together.
Me too, I printed it and underlined it, I will try to memorize some of the concepts and the exposition, because this is a cristallization of what I vaguely feel about the abuse of LLM I am currently witnessing
Absolutely with you on the abuse of LLMs. I'm deeply concerned about loss of competence and I am so burned out from having to deal with other people's messy and overly complex code.
I think people who think about this like us need to start building resilience for the very real possibility that in a couple of years we'll be the ones dealing with these awful LLM-generated code bases, fixing bad logic and bugs.
Ah, thanks. Yes it couldn't have been 2010 because he died in 2002. But the date this was written is important, otherwise his references to "the last decade" don't mean anything!
Apple didn't learn those lessons with Apple Script either: there is a rigid sequence of characters that represents a valid program and if you're off by one character, tough. If you're lucky, the tool will guide you to what it wants. If not, you're stuck looking up the exact syntax so off to the reference manual you go, either way.
So there's minimal to looking up the syntax, whether it's based on some natural language phrase or a less wordy or ambiguous artificial language.
Over the last couple of weeks or so of me finally starting to use AI pair programming tools (for me, Cursor) I've been realizing that, much like when I play music, I don't really think about programming a natural language terms in the first place, it's actually been kind of hard to integrate an AI coding agent into my workflow mentally
Djikstra's clarity of expression and thoguht is indeed impressive. One nuance : he seems to completely equate ease of language with ability to do undetectable mistakes. I disagree: I know people whose language is extremely efficient at producing analogies that can shortcut for the listener many pages of painful mathematical proofs: for instance, convenu the emergence of complexity for many simple processes by a "swarming"
> he seems to completely equate ease of language with ability to do undetectable mistakes.
I do not believe this is his argument. He was making the point that there is a balance. You need to consider the context of the times, and remember that in this context a language like C is considered "high-level", not a language like Python. He later moves on to discuss formalism through mathematics (referencing Vieta, Descartes, Leibniz, and Boole), in how this symbolism is difficult to perform and many are adverse to it, but that through its birth we've been able to reap a lot of rewards. He precisely makes the claim that were we not to move to formal methods and instead maintain everyday language, we would still be stuck at the level of the Greeks.
Actually in one season of An Opinionated History of Mathematics, the host (a mathematician) specifically discusses the transition in the Greeks and highlights how many flaws there were in this system. How the slow move to mathematical formalism actually enabled correctness.
The point is that human language is much more vague. It has to be this way. But the formalism in symbolics (i.e. math) would similarly make a terrible language for computing. The benefit of the symbolic approach is the extreme precision, but it also means the language is extremely verbose. While in human languages we trade precision for speed and flexibility. To communicate what I have with a mathematical language would require several pages of text. Like he says, by approaching human language this shifts more responsibility to the machine.
50 years ago, in "The Mythical Man-Month" Fred Brooks was already discussing the cost of cloud based solutions.
> Since size is such a large part of the user cost of a programming
system product, the builder must set size targets, control size, and
devise size-reduction techniques, just as the hardware builder sets
component-count targets, controls component count, and devises
count-reduction techniques. Like any cost, size itself is not bad,
but unnecessary size is.
And the why of Agile, DDD and TDD:
> Plan the System for Change [...] Plan the Organization for Change
I love reading literate-programming software! The problem is that very few programmers are as skilled at writing clearly as are Knuth and Dijkstra. I think I read somewhere that book publishers receive thousands of manuscripts for each one they publish. Likewise, few programmers can write prose worth reading.
About every six to ten years I look in on the state of the art on making artificial human languages in which one cannot be misunderstood.
If we ever invent a human language where laws can be laid out in a manner that the meaning is clear, then we will have opened a door on programming languages that are correct. I don’t know that a programmer will invent this first. We might, but it won’t look that natural.
Just like Web 1.0 - when the best we could think of to do was shovel existing printed brochures onto web pages.
In AI 1.0 we are simply shoveling existing programming languages into the LLM - in no way marrying programming and LLM - they exist as entirely different worlds.
AI 2.0 will be programming languages - or language features - specifically designed for LLMs.
A language is invented for a domain for precision and clarity the natural language cannot provide … trying to do the opposite would certainly create more work
> thanks to the carefully, or at least consciously designed formal symbolisms that we owe to people like Vieta, Descartes, Leibniz, and (later) Boole.
Please check this talk on the contributions of these mentioned people for the complementary form of deterministic AI (machine intelligence) namely logic, optimization and constraint programming in a seminal lecture by John Hooker [1].
I have got the feeling that if we combine the stochastic nature of LLM based NLP with the deterministic nature of feature structure trchnique based NLP (e.g. CUE), guided by logic, optimization and constraint programming we probably can solve intuitive automation or at least perform proper automation (or automatic computing as Dijkstra put it).
Apparently Yann LeCun also recently proposing optimization based AI namely inference through optimization, or objective driven AI in addition to data-driven AI [2].
Fun facts, you can see Donald Knuth asking questions towards the end of the JH's lecture presentation.
[1] Logic, Optimization, and Constraint Programming: A Fruitful Collaboration - John Hooker - CMU (2023) [video]:
[2] Mathematical Obstacles on the Way to Human-Level AI - Yann LeCun - Meta - AMS Josiah Willard Gibbs Lecture at the 2025 Joint Mathematics Meetings (2025) [video]:
One of the things LLM's/natural language programming brings is greater access.
While the actual code may be crap, it opens up things to more people to play around and iterate on ideas without having to have a ton of knowledge.
That is powerful by itself.
Baley shrugged. He would never teach himself to avoid asking useless questions. The robots knew. Period. It occurred to him that, to handle robots with true efficiency, one must needs be expert, a sort of roboticist. How well did the average Solarian do, he wondered?
this clearly has nothing to do with the current main usages of LLMs, it's about using natural language as an interface to produce accurate results, as a further abstraction on top of general purpose languages.
LLMs take natural language as input and produces it as output (note that producing source code is the same thing). The algorithm that takes input and produces output is still written in mathematical precise symbols and needs to be accurate, even if the input and output aren't expressed in a formal language.
We will see if and when an algorithm can be parsed and executed from a natural language "source code" and if that is an improvement.
Also note that "source code" implies it is a code. Natural languages are not a code, that is, a unique mapping from a set to another.
Formal can be foolish, too. If you don't believe that, then I have a set for sale, with the property that it contains all sets that don't contain itself.
The problem you’re referring to arose precisely due to lack of formalism. It was a problem back when mathematicians were toying with naive set theory, one not based on axioms, but instead on intuitive terminology. Moving to axiomatic, more formal, set theory solved it.
The problem is the same, no matter if you look at it formally or informally. You could get your set theory axioms wrong, for example, but you would still be formal. Oh wait, you have a proof that set theory is consistent, right?
Vibes aren’t really about clarity, are they? The point is that a clear, programmatic approach is not the only effective computational mechanism for realizing intentions anymore.
Keep in mind that Dijkstra had some giant shoulders to stand on. This article is the very first one I’ve ever seen that directly dealt with vibes.
Computers do what you tell them to, not what you want them to. This is naturally infuriating, as when a computer doesn't do what you want, it means you've failed to express your vague idea in concrete terms.
LLMs in the most general case do neither what you tell them, nor what you want them to. This, surprisingly, can be less infuriating, as now it feels like you have another actor to blame - even though an LLM is still mostly deterministic, and you can get a pretty good idea of what quality of response you can expect for a given prompt.
Why did mathematicians invent new symbols? Imagine if all of algebra, calculus, linear algebra looked like those word problems from antiquity? Natural language is not good for describing systems, symbolic forms are more compressed and be considered a kind of technology in its own right.
Dijkstra is entirely correct in this, and it's something I've been trying to urge people to recognize since the beginnings of this LLM wave.
There is inherent value in using formal language to refine, analyze, and describe ideas. This is, after all, why mathematical symbolism has lasted in spite of the fact that all mathematicians are more than capable of talking about mathematical ideas in their natural tongues.
Code realizes a computable model of the world. A computable model is made up of a subset of the mathematical functions we can define. We benefit greatly from formalism in this context. It helps us be precise about the actual relationships and invariants that hold within a system. Stable relationships and invariants lead to predictable behaviors, and predictable systems are reliable systems on the plan of human interaction.
If you describe your system entirely in fuzzily conceived natural language, have you done the requisite analysis to establish the important relationships and invariants among components in your system, or are you just half-assing it?
Engineering is all about establishing relative degrees of certainty in the face of the wild uncertainty that is the default modality of existence. Moving toward a world in which we "engineer" systems increasingly through informal natural language is a step backwards on the continuum of reliability, comprehensibility, and rigor. The fact that anyone considers using these tools and still thinks of themselves as an "engineer" of some kind is an absolute joke.
It's pretty obvious to me that this LLM business won't be economically feasible until it can actually produce better code than a team of humans could without it. The reason programmers are paid so highly is because their work is incredibly productive and valuable. One programmer can enable and improve the work of many hundreds of other people. Cost cutting on the programmer isn't worth it because it'll create greater losses in other places. Hence the high salaries. Every saving you make on the programmer is magnified a hundred times in losses elsewhere.
given that LLMs are going to produce code that is essentially an average of the code it has been trained on, which is all human code of varying quality, I don't see how the current methods are going to actually produce better code than humans do when working with their own domain-specific knowledge.
LLMs are usually used to describe goals or to provide feedback (correction or encouragement) towards its implementation.
Programming is about iteratively expressing a path towards satisfying said goals.
What LLMs are doing now is converting "requirements" into "formalizations".
I don't think Djikstra is wrong in saying - that performing programming in plain-language is a pretty weird idea.
We want to concretize ideas in formalisms. But that's not what any human (including Djikstra) starts with... you start with some sort of goal, some sort of need and requirements.
LLMs merely reduce the time/effort required to go from goals -> formalism.
(2010) by the way, which makes this article all the more impressive.
I was under the assumption this was a current body of work seeing as Dijkstra spoke so well about the possibilities but this just goes to show some people were ahead of their tike with their worries.
Also adding your home address to something you write / publish / host on the internet is pretty hardcore.
> 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.
Seeing as transformers are relatively simple to implement…
It stands to reason he was, in some sense, right. LLMs are damn easy to use.
Personal context is tacit experience. Cultural context is aggregate.
Wittgenstein stated that the differences between personal and cultural language makes it impossible to agree on anything foundational to philosophy.
Godel did something similar to discrete structures by hacking self reference -- a kind of recursive self reference with no exit state.
I think pair programming with an LLM is interesting. You get to compare personal context with aggregate context. Plus, the external feedback helps me break out of excessive personal self reference.
People are sticking up for LLMs here and that's cool.
I wonder, what if you did the opposite? Take a project of moderate complexity and convert it from code back to natural language using your favorite LLM. Does it provide you with a reasonable description of the behavior and requirements encoded in the source code without losing enough detail to recreate the program? Do you find the resulting natural language description is easier to reason about?
I think there's a reason most of the vibe-coded applications we see people demonstrate are rather simple. There is a level of complexity and precision that is hard to manage. Sure, you can define it in plain english, but is the resulting description extensible, understandable, or more descriptive than a precise language? I think there is a reason why legalese is not plain English, and it goes beyond mere gatekeeping.
> Do you find the resulting natural language description is easier to reason about?
An example from an different field - aviation weather forecasts and notices are published in a strongly abbreviated and codified form. For example, the weather at Sydney Australia now is:
It's almost universal that new pilots ask "why isn't this in words?". And, indeed, most flight planning apps will convert the code to prose.
But professional pilots (and ATC, etc) universally prefer the coded format. Is is compact (one line instead of a whole paragraph), the format well defined (I know exactly where to look for the one piece I need), and it's unambiguous and well defined.
Same for maths and coding - once you reach a certain level of expertise, the complexity and redundancy of natural language is a greater cost than benefit. This seems to apply to all fields of expertise.
Reading up on the history of mathematics really makes that clear as shown in
https://www.goodreads.com/book/show/1098132.Thomas_Harriot_s...
(ob. discl., I did the typesetting for that)
It shows at least one lengthy and quite wordy example of how an equation would have been stated, then contrasts it in the "new" symbolic representation (this was one of the first major works to make use of Robert Recorde's development of the equals sign).
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> Same for maths and coding - once you reach a certain level of expertise, the complexity and redundancy of natural language is a greater cost than benefit. This seems to apply to all fields of expertise.
And as well as these points, ambiguity. A formal specification of communication can avoid ambiguity by being absolute and precise regardless of who is speaking and who is interpreting. Natural languages are riddled wth inconsistencies, colloquialisms, and imprecisions that can lead to misinterpretations by even the most fluent of speakers simply by nature of natural languages being human language - different people learn these languages differently and ascribe different meanings or interpretations to different wordings, which are inconsistent because of the cultural backgrounds of those involved and the lack of a strict formal specification.
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An interesting perspective on this is that language is just another tool on the job. Like any other tool, you use the kind of language that is most applicable and efficient. When you need to describe or understand weather conditions quickly and unambiguously, you use METAR. Sure, you could use English or another natural language, but it's like using a multitool instead of a chef knife. It'll work in a pinch, but a tool designed to solve your specific problem will work much better.
Not to slight multitools or natural languages, of course - there is tremendous value in a tool that can basically do everything. Natural languages have the difficult job of describing the entire world (or, the experience of existing in the world as a human), which is pretty awesome.
And different natural languages give you different perspectives on the world, e.g., Japanese describes the world from the perspective of a Japanese person, with dedicated words for Japanese traditions that don't exist in other cultures. You could roughly translate "kabuki" into English as "Japanese play", but you lose a lot of what makes kabuki "kabuki", as opposed to "noh". You can use lots of English words to describe exactly what kabuki is, but if you're going to be talking about it a lot, operating solely in English is going to become burdensome, and it's better to borrow the Japanese word "kabuki".
All languages are domain specific languages!
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you guys are not wrong. explain any semi complez program, you will instantly resort to diagrams, tables, flow charts etc. etc.
ofcourse, you can get your LLM to be bit evil in its replies, to help you truly. rather than to spoon feed you an unhealthy diet.
i forbid my LLM to send me code and tell it to be harsh to me if i ask stupid things. stupid as in, lazy questions. send me the link to the manual/specs with an RTFM or something i can digest and better my undertanding. send links not mazes of words.
now i can feel myself grow again as a programmer.
as you said. you need to build expertise, not try to find ways around it.
with that expertise you can find _better_ ways. but for this, firstly, you need the expertise.
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And to this point - the English language has far more ambiguity than most programming languages.
> prefer the coded format. Is is compact...
On the other hand "a folder that syncs files between devices and a server" is probably a lot more compact than the code behind Dropbox. I guess you can have both in parallel - prompts and code.
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You can see the same phenomenon playing a roguelike game.
They traditionally have ASCII graphics, and you can easily determine what an enemy is by looking at its ASCII representation.
For many decades now graphical tilesets have been available for people who hate the idea of ASCII graphics. But they have to fit in the same space, and it turns out that it's very difficult to tell what those tiny graphics represent. It isn't difficult at all to identify an ASCII character rendered in one of 16 (?) colors.
I wonder why the legal profession sticks to natural language
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The point of LLM is to enable "ordinary people" to write software. This movement is along with "zero code platform", for example. Creating algorithms by drawing block-schemes, by dragging rectangles and arrows. This is old discussion and there are many successful applications of this nature. LLM is just another attempt to tackle this beast.
Professional developers don't need this ability indeed. Most professional developers, who had to deal with zero code platforms, probably would prefer to just work with ordinary code.
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I'm not so sure it's about precision rather than working memory. My presumption is people struggle to understand sufficiently large prose versions for the same reason a LLM would struggle working with larger prose versions: people have limited working memory. The time needed to reload info from prose is significant. People reading large text works will start highlighting and taking notes and inventing shorthand forms in their notes. Compact forms and abstractions help reduce demands for working memory and information search. So I'm not sure it's about language precision.
Another important difference is reproducibility. With the same program code, you are getting the same program. With the same natural-language specification, you will presumably get a different thing each time you run it through the "interpreter". There is a middle ground, in the sense that a program has implementation details that aren't externally observable. Still, making the observable behavior 100% deterministic by mere natural-language description doesn't seem a realistic prospect.
So is more compact better? Does K&R's *d++ = *s++; get a pass now?
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Language can carry tremendous amounts of context. For example:
> I want a modern navigation app for driving which lets me select intersections that I never want to be routed through.
That sentence is low complexity but encodes a massive amount of information. You are probably thinking of a million implementation details that you need to get from that sentence to an actual working app but the opportunity is there, the possibility is there, that that is enough information to get to a working application that solves my need.
And just as importantly, if that is enough to get it built, then “can I get that in cornflower blue instead” is easy and the user can iterate from there.
But it doesn't 'carry context' ; it's just vague and impossible to implement what you have in mind. And that's the problem; You assume people live in your reality, I assume mine, LLMs have some kind of mix between us and we will get 3 very different apps, none of which will be useful from that line alone. I like that line to be expanded with enough context to have an idea what you actually need to have built and I am quite sure pseudocode (or actual code) will be much shorter than a rambling english description you can come up with; most of which (unless it's logic language) will have enough unambiguous context to implement.
So sure, natural language is great for spitballing ideas, but after that it's just guessing what you actually want to get done.
You call it context or information but I call it assumptions. There are a ton assumptions in that sentence that an LLM will need to make in order to take that and turn it into a v1. I’m not sure what resulting app you’d get but if you did get a useful starting point, I’d wager the fact that you chose a variation of an existing type of app helped a lot. That is useful, but I’m not sure this is universally useful.
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Sure but we build (leaky) abstractions, and this is even happens in legal texts.
Asking an llm to build a graphical app in assembly from an ISA and a driver for the display would give you nothing.
But with a mountain of abstractions then it can probably do it.
This is not to defend an LLM more to say I think that by providing the right abstractions (reusable components) then I do think it will get you a lot closer.
Being doing toy-examples of non-trivial complexity. Architecting the code so context is obvious and there are clear breadcrumbs everywhere is the key. And the LLM can do most of this. Prototype-> refactor/cleanup -> more features -> refactor / cleanup add architectural notes.
If you know what a well architected piece of code is supposed to look like, and you proceed in steps, LLM gets quite far as long as you are handholding it. So this is usable for non-trivial _familiar_ code where typing it all would be slower than prompting the llm. Maintaining LLM context is the key here imo and stopping it when you see weird stuff. So it requires you act as thr senior partner PR:ing everyhting.
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Llm frameworks !!
--I think there is a reason why legalese is not plain English
This is true. Part of the precision of legalese is that the meanings of some terms have already been more precisely defined by the courts.
Yeah, my theory on this has always been that a lot of programming efficiency gains have been the ability to unambiguously define behavior, which mostly comes from drastically restricting the possible states and inputs a program can achieve.
The states and inputs that lawyers have to deal with tend to much more vague and imprecise (which is expected if you're dealing with human behavior and not text or some other encodeable input) and so have to rely on inherently ambiguous phrases like "reasonable" and "without undue delay."
This opens an interesting possibility for a purely symbol-based legal code. This would probably improve clarity when it came to legal phrases that overlap common English, and you could avoid ambiguity when it came to language constructs, like in this case[1], where some drivers were losing overtime pay because of a comma in the overtime law.
[1] https://cases.justia.com/federal/appellate-courts/ca1/16-190...
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I've thought about this quite a bit. I think a tool like that would be really useful. I can imagine asking questions like "I think this big codebase exposes a rest interface for receiving some sort of credit check object. Can you find it and show me a sequence diagram for how it is implemented?"
The challenge is that the codebase is likely much larger than what would fit into a single codebase. IMO, the LLM really needs to be taught to consume the project incrementally and build up a sort of "mental model" of it to really make this useful. I suspect that a combination of tool usage and RL could produce an incredibly useful tool for this.
"Sure, you can define it in plain english, but is the resulting description extensible, understandable, or more descriptive than a precise language? I think there is a reason why legalese is not plain English, and it goes beyond mere gatekeeping."
Is this suggesting the reason for legalese is to make documents more "extensible, understable or descriptive" than if written in plain English.
What is this reason that the parent thinks legalese is used that "goes beyond gatekeeping".
Plain English can be every bit as precise as legalese.
It is also unclear that legalese exists for the purpose of gatekeeping. For example, it may be an artifact that survives based on familiarity and laziness.
Law students are taught to write in plain English.
https://www.law.columbia.edu/sites/default/files/2021-07/pla...
In some situations, e.g., drafting SEC filings, use of plain English is required by law.
https://www.law.cornell.edu/cfr/text/17/240.13a-20
> Plain English can be every bit as precise as legalese.
If you attempt to make "plain English" as precise as legalese, you will get something that is basically legalese.
Legalese does also have some variables, like "Party", "Client", etc. This allows for both precision -- repeating the variable name instead of using pronouns or re-identifying who you're talking about -- and also for reusability: you can copy/paste standard language into a document that defines "Client" differently, similar to a subroutine.
What you're describing is decontextualization. A sufficiently powerful transformer would theoretically be able recontextualize a sufficiently descriptive natural language specification. Likewise, the same or an equivalently powerful transformer should be able to fully capture the logic of a complicated program. We just don't have sufficient transformers yet.
I don't see why a complete description of the program's design philosophy as well as complete descriptions of each system and module and interface wouldn't be enough. We already produce code according to project specification and logically fill in the gaps by using context.
>sufficiently descriptive natural language specification https://www.commitstrip.com/en/2016/08/25/a-very-comprehensi...
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“Fill in the gaps by using context” is the hard part.
You can’t pre-bake the context into an LLM because it doesn’t exist yet. It gets created through the endless back-and-forth between programmers, designers, users etc.
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I think you can basically make the same argument for programming directly in machine code since programming languages are already abstractions.
the vibe coding seems a lot like the dream of using UML, but in a distinctly different direction, and how in theory (and occasional practice) you can create a two way street, most often these things are one way conversions and while we all desire some level of two way dependency and continual integration to make certain aspects of coding (documentation, testing) to be up to date, the reality is that the generative code aspect always breaks and you're always going to be left with the raw products of these tools and it's rarely going to be a cycle of code -> tool -> code. And thus the ultimate value beyond the bootstrap is lose.
We're still going to have AI tools, but seriously complex applications, the ones we pay money for, arn't going to yield many LLM based curation strategies. There will probably be some great documentation and testing ones, but the architetural-code paradigm isnt going to yield any time soon.
isn't that just copilot "explain", one of the earliest copilot capabilities. It's definitely helpful to understand new codebases at a high level
> there is a reason why legalese is not plain English, and it goes beyond mere gatekeeping.
unfortunately they're not in any kind of formal language either
> isn't that just copilot "explain", one of the earliest copilot capabilities. It's definitely helpful to understand new codebases at a high level
In my experience this function is quite useless. It will just repeat the code in plain English. It will not explain it.
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> > there is a reason why legalese is not plain English, and it goes beyond mere gatekeeping.
> unfortunately they're not in any kind of formal language either
Most formulas made of fancy LaTeX symbols you find in math papers aren't a formal language either. They usually can't be mechanically translated via some parser to an actual formal language like Python or Lean. You would need an advanced LLM for that. But they (the LaTeX formulas) are still more precise than most natural language. I assume something similar is the case with legalese.
[dead]
The voice of reason, but no amount of reason can dissuade anybody from believing what they want to believe.
This reminded me of this old quote from Hal Abelson:
"Underlying our approach to this subject is our conviction that "computer science" is not a science and that its significance has little to do with computers. The computer revolution is a revolution in the way we think and in the way we express what we think. The essence of this change is the emergence of what might best be called procedural epistemology—the study of the structure of knowledge from an imperative point of view, as opposed to the more declarative point of view taken by classical mathematical subjects. Mathematics provides a framework for dealing precisely with notions of "what is". Computation provides a framework for dealing precisely with notions of "how to"."
This is key: computation is about making things happen. Coding with an LLM adds a level of abstraction but the need for precision and correctness of the "things that happen" doesn't go away. No matter how many cool demos and "coding is dead" pronouncements because AI - and the demos are very cool - the bulk of the work moves to the pre- and post-processing and evals with AI. To the extent that it makes programming more accessible it's a good thing, but can't really replace it.
Well that sure isn't what they teach in computer science programs anymore
Finally someone put it this way! Natural language has embedded limitations that stem from our own mental limitations -the human mind thinks sometimes too abstract or too specific things, and misses important details or generalizations.
As a programmer, I know first hand that the problems or even absurdities of some assignments only become apparent after one has begun implement the code as code, i.e. as strict symbolisms.
Not to mention that it often takes more time to explain something accurately in natural language than it takes to just write the algorithm as code.
Yes! I have a certain personality preference for abstractions and tend to understand things in an abstract manner which is extremely difficult for me to articulate in natural language.
The man has been dead for 23 years
He's the Immortal Dutchman.
This is one of the best comments I've read on this site in a long while.
A single, crude, statement of fact slaying the work of a million typewriter monkeys spewing out random characters thinking they're actually writing the Shakespeare novel, lmao.
We need realistic expectations for the limitations of LLMs as they work today. Philosophically, natural language is imperfect at communicating ideas between people, which is its primary purpose! How often do you rewrite sentences, or say "actually what I meant was...", or rephrase your emails before pressing Send? We are humans and we rarely get things perfect on the first try.
And now we're converting this imperfect form of communication (natural language) into a language for machines (code), which notoriously do exactly what you say, not what you intend.
NLP is massively, and I mean massively, beneficial to get you started on the right path to writing an app/script/etc. But at the end of the day it may be necessary to refactor things here and there. The nice thing is you don't have to be a code ninja to get value out of LLMs, but it's still helpful and sometimes necessary.
/s: that’s because we haven’t gone far enough. People use natural language to generate computer programs. Instead, they should directly run prompts.
“You are the graphics system, an entity that manages what is on the screen. You can receive requests from all programs to create and destroys “windows”, and further requests to draw text, lines, circles, etc. in a window created earlier. Items can be of any colour.
You also should send more click information to whomever created the window in which the user clicked the mouse.
There is one special program, the window manager, that can tell you what windows are displayed where on any of the monitors attached to the system”
and
“you are a tic-tac-toe program. There is a graphics system, an entity that manages what is on the screen. You can command it to create and destroys “windows”, and to draw text, lines, circles, etc. in a window created earlier. Items can be of any colour.
The graphics you draw should show a tic-tac-toe game, where users take turn by clicking the mouse. If a user wins the game, it should…
Add ads to the game, unless the user has a pay-per-click subscription”
That should be sufficient to get a game running…
To save it, you’d need another prompt:
”you are a file system, an entity that persists data to disk…”
You also will want
”you are a multi-tasking OS. You give multiple LLMs the idea that they have full control over a system’s CPU and memory. You…”
I look forward to seeing this next year in early April.
all these prompts are currently implemented under the hood by generating and running python code
> Machine code, with its absence of almost any form of redundancy, was soon identified as a needlessly risky interface between man and machine. Partly in response to this recognition so-called "high-level programming languages" were developed, and, as time went by, we learned to a certain extent how to enhance the protection against silly mistakes. It was a significant improvement that now many a silly mistake did result in an error message instead of in an erroneous answer.
I feel that we’ve collectively jumped into programming with LLMs too quickly. I really liked how Rust has iterated on pointing out “silly mistakes” and made it much more clear what the fix should be. That’s a much more favorable development for me as a developer. I still have the context and understanding of the code I work on while the compiler points out obvious errors and their fixes. Using an LLM feels like a game of semi-intelligent guessing on the other hand. Rust’s compiler is the master teaching the apprentice. LLMs are the confident graduate correcting the master. I greatly prefer Rust’s approach and would like to see it evolved further if possible.
Rust (and others) has type inference, LLMs have so-called "reasoning". They fake understanding, a lie that will sooner or later have consequences.
Natural language is poor medium at communicating rules and orders. The current state of affair in US is a prime example.
We are still debating what some laws and amendments mean. The meaning of words change over time, lack of historical context, etc.
I would love natural language to operate machines, but I have been programming since mid 80's and the stubbornness of the computer languages (from BASIC, to go) strikes a good balance, and puts enough responsibility on the emitter to precisely express what he wants the machine to do.
> It was a significant improvement that now many a silly mistake did result in an error message instead of in an erroneous answer. (And even this improvement wasn't universally appreciated: some people found error messages they couldn't ignore more annoying than wrong results, and, when judging the relative merits of programming languages, some still seem to equate "the ease of programming" with the ease of making undetected mistakes.)
If I didn't know who wrote this it would seem like a jab directly at people who dislike Rust.
Rust? Since when is Rust the pinnacle of static type safety?
After I've worked for some time with a language that can express even stronger invariants in types than Rust (Scala) I don't see that property anymore as clear win regardless circumstances. I don't think any more "stronger types == better, no matter what".
You have a price to pay for "not being allowed to do mistakes": Explorative work becomes quite difficult if the type system is really rigid. Fast iteration may become impossible. (Small changes may require to re-architecture half your program, just to make the type system happy again![1])
It's a trade-off. Like with everything else. For a robust end product it's a good thing. For fast experimentation it's a hindrance.
[1] Someone described that issue quite well in the context of Rust and game development here: https://loglog.games/blog/leaving-rust-gamedev/
But it's not exclusive to Rust, nor game dev.
> You have a price to pay for "not being allowed to do mistakes": Explorative work becomes quite difficult
This is a huge deal for me.
At the beginning of most "what if...?" exercises, I am just trying to get raw tuples of information in and out of some top-level-program logic furnace for the first few [hundred] iterations. I'll likely resort to boxing and extremely long argument lists until what I was aiming for actually takes hold.
I no longer have an urge to define OOP type hierarchies when the underlying domain model is still a vague cloud in my head. When unguided, these abstractions feel like playing Minecraft or Factorio.
I can't remember if I came up with this analogy or not, but programming in Rust is like trying to shape a piece of clay just as it's being baked.
> Explorative work becomes quite difficult if the type system is really rigid
Or to put it another way, the ease of programming is correlated with the ease of making undetected mistakes.
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I would have thought of people who unironically liked fractal-of-bad-design-era PHP and wat-talk JavaScript.
I guess some kinds of foolishness are just timeless.
As a person who dislikes rust, the problem is the error messages when there's no error -- quite a different problem. The rust type system is not an accurate model of RAM, the CPU and indeed, no device.
He's here talking about interpreted languages.
He's also one of those mathematicians who are now called computer scientists whose 'algorithms' are simple restatements of mathematics and require no devices. A person actively hostile, in temperament, to the embarrassing activity of programming an actual computer.
Using natural language to specify and build an application is not unlike having a game design document before you actually start prototyping your game. But once you have implemented the bulk of what you wanted, the implementation becomes the reference and you usually end up throwing away the GDD since it's now out of sync with the actual game.
Insisting that for every change one should go read the GDD, implement the feature and then sync back the GDD is cumbersome and doesn't work well in practice. I've never seen that happen.
But if there ever comes a time when some AI/LLM can code the next version of Linux or Windows from scratch based on some series of prompts, then all bets are off. Right now it's clearly not there yet, if ever.
I somewhat disagree with this. In real life, say in some company, the inception of an idea for a new feature is made in the head of some business person. This person will not speak any formal language. So however you turn it, some translation from natural language to machine language will have to be done to implement the feature.
Typically the first step, translation from natural to formal language, will be done by business analysts and programmers. But why not try to let computers help along the way?
Computers can and should help along the way, but Dijkstra's argument is that a) much of the challenge of human ideas is discovered in the act of converting from natural to formal language and b) that this act, in and of itself, is what trains our formal logical selves.
So he's contesting not only the idea that programs should be specified in natural language, but also the idea that removing our need to understand the formal language would increase our ability to build complex systems.
It's worth noting that much of the "translation" is not translation, but fixing the logical ambiguities, inconsistencies and improper assumptions. Much of it can happen in natural language, if we take Dijkstra seriously, precisely because programmers at the table who have spent their lives formalizing.
There are other professions which require significant formal thinking, such as math. But also, the conversion of old proofs into computer proofs has lead us to discover holes and gaps in many well accepted proofs. Not that much has been overturned, but we still do t have a complete proof for Fermats last theorem [1].
[1] https://xenaproject.wordpress.com/2024/12/11/fermats-last-th...
But even real translation is bad.
There has been some efforts to make computer languages with local (non-english) keywords. Most have fortunately already failed horribly.
But it still exists, e.g. in spreadsheet formulas.
In some cases even number formatting (decimal separators) are affected.
The first step isn't from natural language to formal language. It's from the idea in your head into natural language. Getting that step right in a way that a computer could hope to turn into a useful thing is hard.
>It's from the idea in your head into natural language. Getting that step right in a way that a computer could hope to turn into a useful thing is hard.
The "inside the head" conversion step would be more relevant in the reply to the gp if the hypothetical AI computer would be hooked up directly to brain implants like neuralink, functional MRI scans, etc to translate brain activity to natural language or programming language code.
But today, human developers who are paid to code for business people are not translating brain implant output signals. (E.g. Javascript programmers are not translating raw electrical waveforms[1] into React code.)
Instead, they translate from "natural language" specifications of businesspeople to computer code. This layer of translation is more tractable for future AI computers even though natural language is more fuzzy and ambiguous. The language ambiguity in business requirements is unavoidable but it still hasn't stopped developers from somehow converting it into concrete non-ambiguous code.
[1] https://www.technologyreview.com/2020/02/06/844908/a-new-imp...
Without descending fully into epistemology, I tend to think that there is no proper "idea" in your head before it's phrased in language - the act of initially describing something in natural language *is* the act of generating it.
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Say, doesn't each business - each activity - have its own formal language?
Not as formalized as programming languages, but it's there.
Try to define any process, you end up with something trending towards formalized even if you don't realize it.
That's pretty much the whole basis of Domain Driven Design. The core message is to get to an Ubiquitous Language which is the formalization of the business jargon (pretty much a glossary). From which the code can then be derived naturally.
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I like your take.
The only issue I have with trusting a computer to do so much is that it doesn't necessarily have the long term vision or intuition some humans might have for the direction of the software product. There's so much nuance to the connection between a business need and getting it into software, or maybe I am overthinking it :D
I don't think youre fully comprehending Dijkstra's argument. He's not saying to not use tool to help with translation, he is saying that not thinking in terms of formal symbols HURTS THINKING. Your ideas are worse if you don't think in formal systems. If you don't treat your thoughts as formal things.
In your example, he has no opinion on how to translate the idea of a "business person" because in his view the ideas of the "business person" are already shallow and bad because they don't follow a formalism. They are not worth translating.
If that's correct, then it's very falsifiable. If a businessperson says "there's a gap in the market - let's build X" they will be following a formalism at their level of detail. They see the market, the interactions between existing products and customers, and where things might be going.
Just because they can't spell it out to the nth degree doesn't matter. Their formalism is "this is what the market would like".
Having an LLM then tease out details - "what should happen in this case" would actually be pretty useful.
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Because then you don't know what the computer's doing. The whole point of this article was that there is value in the process of writing your ideas out formally. If you "let computers help you along the way", you'll run straight into the issue of needing an increasingly formal natural language to get sufficiently good results from the machine.
Forth, PostScript and Assembly are the "natural" programming languages from the perspective of how what you express maps to the environment in which the code executes.
The question is "natural" to whom, the humans or the computers?
AI does not make human language natural to computers. Left to their own devices, AIs would invent languages that are natural with respect to their deep learning architectures, which is their environment.
There is always going to be an impedance mismatch across species (humans and AIs) and we can't hide it by forcing the AIs to default to human language.
Any sufficiently advanced method of programming will start to look less like natural language and more like a programming language.
If you still don’t want to do programming, then you need some way to instruct or direct the intelligence that _will_ do the programming.
And any sufficiently advanced method of instruction will look less like natural language, and more like an education.
Modern programming already is very, very far from strict obedience and formal symbolism. Most programmers these days (myself included!) are using libraries, frameworks, and other features that mean what they are doing in practice is wielding sky-high abstractions, gluing things together they do not (and can not) fully understand the inner workings of.
If I create a website with Node.js, I’m not manually managing memory, parsing HTTP requests byte-by-byte, or even attempting to fully grasp the event loop’s nuances. I’m orchestrating layers of code written by others, trusting that these black boxes will behave as advertised according to my best, but deeply incomplete, understanding of them.
I'm not sure what this means for LLMs programming, but I already feel separated from the case Dijkstra lays out.
> Modern programming already is very, very far from strict obedience and formal symbolism
Difficult to sort this out with what follows.
Consider group theory. A group G is a set S with an operator * that supports an identity, closure, and an inverse. With that abstraction comes a hefty amount of power. In some sense, a group is akin to a trait on some type, much like how a class in Java can implement or extend Collection. (Consider how a ring ‘extends’ a group.)
I’d posit frameworks and libraries are no different in terms of formal symbolism from the math structure laid out above. Maybe the interfaces are fuzzy and the documentation is shoddy, but there’s still a contract we use to reason about the tool at hand.
> I’m not manually managing memory, parsing HTTP requests byte-by-byte
If I don’t reprove Peano’s work, then I’m not really doing math?
Natural language is pretty good for describing the technical requirements for the complex system, though. I.e. not the current code implementation, but why the current code implementation is selected vs other possible implementations. Not what code do, but what it is expected to do. Basically, most of the missing parts, that live in Jira-s, instead of your repo. It is also good, at allowing better refactoring capabilities, when all your system is described by outside rules, which could be enforced on the whole codebase. We just use programming languages, because it is easier to use in automated/computer context (and was the only way to use, to be honest, before all the LLM stuff). Though, while it gives us non-ambiguity on the local scale, it stops working on the global scale, the first moment person went and copy-pasted part of the code. Are you sure that part follows all the high-level restrictions we should to follow and is correct program? It is program that would run, when compile, but definition of run is pretty loose. In C++ program that corrupts all the memory is also runnable.
Reminds me of another recurring idea of replacing code with flowcharts. First I've seen that idea coming from some unknown Soviet professor from 80s, and then again and again from different people from different countries in different contexts. Every time it is sold as a total breakthrough in simplicity and also every time it proves to be a bloat of complexity and a productivity killer instead.
Or weak typing. How many languages thought that simplifying strings and integers and other types into "scalar", and making any operation between any operands meaningful, would simplify the language? Yet every single one ended up becoming a total mess instead.
Or constraint-based UI layout. Looks so simple, so intuitive on simple examples, yet totally failing to scale to even a dozen of basic controls. Yet the idea keeps reappearing from time to time.
Or an attempt at dependency management by making some form of symlink to another repository e.g. git modules, or CMake's FetchContent/ExternalProject? Yeah, good luck scaling that.
Maybe software engineering should have some sort of "Hall of Ideas That Definitely Don't Work", so that young people entering the field could save their time on implementing one more incarnation of an already known not good idea.
> Maybe software engineering should have some sort of "Hall of Ideas That Definitely Don't Work", so that young people entering the field could save their time on implementing one more incarnation of an already known not good idea.
I'm deeply curious to know how you could easily and definitively work out what is and is not an idea that "Definitely Don't Work"
Mathematics and Computer Science seem to be littered with unworkable ideas that have made a comeback when someone figured out how to make them work.
Well, "Hall of Ideas That Are So Difficult To Make Work Well That They May Not In Fact Be Much Use" doesn't roll off the tongue as smoothly.
What this Hall could contain, for each idea, is a list of reasons why the idea has failed in the past. That would at least give future Quixotes something to measure their efforts by.
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Constraint-based layout works, but you need a serious constraint engine, such as the one in the sketch editors of Autodesk Inventor or Fusion 360, along with a GUI to talk to it. Those systems can solve hard geometry problems involving curves, because you need that when designing parts.
Flowchart-based programming scales badly. Blender's game engine (abandoned) and Unreal Engine's "blueprints" (used only for simple cases) are examples.
Not sure if you’re talking about DRAKON here, but I love it for documentation of process flows.
It doesn’t really get complicated, but you can very quickly end up with drawings with very high square footage.
As a tool for planning, it’s not ideal, because “big-picture” is hard to see. As a user following a DRAKON chart though, it’s very, very simple and usable.
Link for the uninitiated: https://en.m.wikipedia.org/wiki/DRAKON
> Maybe software engineering should have some sort of "Hall of Ideas That Definitely Don't Work", so that young people entering the field could save their time on implementing one more incarnation of an already known not good idea.
FWIW, neural networks would be in that pool until relatively recently.
If we change "definitely don't work" to "have the following so far insurmountable challenges", it addresses cases like this. Hardware scaling limitations on neural networks have been known to be a limitation for a long time - Minsky and Papert touched in this in Perceptrons in 1969.
The Hall would then end up containing a spectrum ranging from useless ideas to hard problems. Distinguishing between the two based on documented challenges would likely be possible in many cases.
For young engineers, it is a good thing to spend time implementing what you call "bad ideas". In the worst-case, they learn from their mistake and gain valuable insight into the pitfalls of such ideas. In the best case, you can have a technological breakthrough as someone finds a way to make such an idea work.
Of course, it's best that such learning happens before one has mandate to derail the whole project.
Most popular dependency management systems literally linking to a git sha commit (tag), see locks file that npm/rebar/other tool gives you. Just in a recursive way.
They do way more than that. For example they won't allow you to have Foo-1 that depends on Qux-1 and Bar-1 that depends on Qux-2 where Qux-1 and Qux-2 are incompatible and can't be mixed within the same static library or assembly. But may allow it if mixing static-private Qux inside dynamic Foo and Bar and the dependency manager is aware of that.
A native submodule approach would fail at link time or runtime due to attempt to mix incompatible files in the same build run. Or, in some build systems, simply due to duplicate symbols.
That "just in a recursive way" addition hides a lot of important design decisions that separate having dependency manager vs. not having any.
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> Or weak typing. How many languages thought that simplifying strings and integers and other types into "scalar", and making any operation between any operands meaningful, would simplify the language? Yet every single one ended up becoming a total mess instead.
Yet JavaScript and Python are the most widely used programming languages [1]. Which suggests your analysis is mistaken here.
[1] https://www.statista.com/statistics/793628/worldwide-develop...
JavaScript is indeed weakly typed, and is widely lampooned and criticized for it¹². But Python has strong typing, and has always had it.
(Both JavaScript and Python have dynamic typing; Python’s type declarations are a form of optional static type checking.)
Do not confuse these concepts.
1. <https://www.destroyallsoftware.com/talks/wat>
2. <https://eqeq.js.org/>
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Python went through a massive effort to add support for type annotations due to user demand.
Similarly, there's great demand for a typed layer on top of Javascript:
- Macromedia: (2000) ActionScript
- Google: (2006) GWT [Compiling Java to JS], and (2011) Dart
- Microsoft: (2012) Typescript
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This is recurring topic indeed. I remember it was hot topic at least two times, when ALM tools were introduced (e.g. Borland ALM suite - https://www.qast.com/eng/product/develop/borland/index.htm), next when BPML language become popular - processes were described by the "marketing" and the software was, you know, generated automatically.
All this went out of fashion, leaving some good stuff that was built at that time (remaining 95% was crap).
Today's "vibe coding" ends when Chat GPT and alikes want to call on some object a method that does not exist (but existed in 1000s of other objects LLM was trained with, so should work here). Again, we will be left with the good parts, the rest will be forgotten and we will move to next big thing.
I read this when I was younger, but I only now get it, and realize how true it all is.
13) Humans writing code is an inherently flawed concept. Doesn't matter what form the code takes. Machine code, assembly language, C, Perl, or a ChatGPT prompt. It's all flawed in the same way. We have not yet invented a technology or mechanism which avoids it. And high level abstraction doesn't really help. It hides problems only to create new ones, and other problems simply never go away.
21) Loosely coupled interfaces made our lives easier because it forced us to compartmentalize our efforts into something manageable. But it's hard to prove that this is a better outcome overall, as it forces us to solve problems in ways that still lead to worse outcomes than if we had used a simpler [formal] logic.
34) We will probably end up pushing our technical abilities to the limit in order to design a superior system, only to find out in the end that simpler formal logic is what we needed all along.
55) We're becoming stupider and worse at using the tools we already have. We're already shit at using language just for communicating with each other. Assuming we could make better programs with it is nonsensical.
For a long time now I've been upset at computer science's lack of innovation in the methods we use to solve problems. Programming is stupidly flawed. I've never been good at math, so I never really thought about it before, but math is really the answer to what I wish programming was: a formal system for solving a problem, and a formal system for proving that the solution is correct. That's what we're missing from software. That's where we should be headed.
> Remark. As a result of the educational trend away from intellectual discipline, the last decades have shown in the Western world a sharp decline of people's mastery of their own language: many people that by the standards of a previous generation should know better, are no longer able to use their native tongue effectively, even for purposes for which it is pretty adequate.
Compare that to:
https://news.ycombinator.com/item?id=43522966
By chance I came just today across GitHub's SpecLang:
https://githubnext.com/projects/speclang/
Funny coincidence!
I leave it here for the nice contrast it creates in light of the submission we're discussing.
When was it written? The date says 2010 but dijkstra died in 2002.
It’s just a date of transcription. The letter was written in 1978.
Dijkstra also advocated for proving the correctness of imperative code using the composition of a set of simple rules, and most programmers ignore that aspect of his work too.
Any specific paper or article of his you would recommend?
https://en.wikipedia.org/wiki/Predicate_transformer_semantic...
Found in about 9 seconds.
_A Discipline of Programming_
Seeing as much of the discussion here is about LLMs, not just the shortcomings of natural language as a programming language, another LLM-specific aspect is how the LLM is interpreting the natural language instructions it is being given...
One might naively think that the "AI" (LLM) is going to apply it's intelligence to give you the "best" code in response to your request, and in a way it is, but this is "LLM best" not "human best" - the LLM is trying "guess what's expected" (i.e. minimize prediction error), not give you the best quality code/design per your request. This is similar to having an LLM play chess - it is not trying to play what it thinks is the strongest move, but rather trying to predict a continuation of the game, given the context, which will be a poor move if it thinks the context indicates a poor player.
With an RL-trained reasoning model, the LLM's behavior is slightly longer horizon - not just minimizing next token prediction errors, but also steering the output in a direction intended to match the type of reasoning seen during RL training. Again, this isn't the same as a human, applying their experience to achieve (predict!) a goal, but arguably more like cargo-cult reasoning - following observed patterns of reasoning in the training set, without the depth of understanding and intelligence to know if this is really applicable in the current context, nor with the ability to learn from it's mistakes when it is not.
So, while natural language itself is of course too vague to program in, which is part of the reason that we use programming languages instead, it's totally adequate as a way to communicate requirements/etc to an expert human developer/analyst, but when communicating to an LLM instead of a person, one should expect the LLM to behave as an LLM, not as a human. It's a paperclip maximizer, not a human-level intelligence.
Dijkstra's insights on the importance of simplicity and elegance in programming are timeless. His emphasis on the process of abstraction and the value of clear, concise code is as relevant today as it was in 1978. A thought-provoking read for any programmer striving to improve their craft.
A great find!
The whole thing seems a step (or several steps) backwards also in terms of UX. I mean surely there was a reason why ls was named ls, and so forth?
A bonus point is that he had also something to say about a real or alleged degeneration of natural languages themselves.
One of the most challenging aspects in my career has been: communication.
This is largely because it leaves chance for misinterpretation or miscommunication. Programming languages eliminated misinterpretation and made miscommunication easier to notice through errors.
Programming language enables micromanaging proactively, I specify the instructions before they run. I often find myself micromanaging retroactively with LLMs, until I reach the path I am looking for.
What needs to be done can and is almost always described in natural language.
Whether that is feasible is a different question (https://xkcd.com/1425/), but also can be described in natural language.
Here is something I tried with o3-mini:
> Create a program that takes an input image and can tell if there is a bird in it.
> ChatGPT said:
> Reasoned for 38 seconds
> Below is an example in Python that uses TensorFlow’s Keras API and a pre-trained ResNet50 model to classify an input image. The code loads an image, preprocesses it to the required size, obtains the top predictions, and then checks if any of the top labels appear to be a bird. You can expand the list of bird keywords as needed for your application.
> python code that works
If you take the critical view, you can always find a way to find an exception that will fail. I can see many happy cases which will just work most of the time, even with the currently available technology. Most of the programming work done today is putting libraries and api services together.
This is the most beautiful thing I've read in a long time.
Me too, I printed it and underlined it, I will try to memorize some of the concepts and the exposition, because this is a cristallization of what I vaguely feel about the abuse of LLM I am currently witnessing
Absolutely with you on the abuse of LLMs. I'm deeply concerned about loss of competence and I am so burned out from having to deal with other people's messy and overly complex code.
I think people who think about this like us need to start building resilience for the very real possibility that in a couple of years we'll be the ones dealing with these awful LLM-generated code bases, fixing bad logic and bugs.
(2010)
This refers to the era of COBOL, or maybe Hypertalk, not LLMs.
The original text was from 1978 according to other sources
Ah, thanks. Yes it couldn't have been 2010 because he died in 2002. But the date this was written is important, otherwise his references to "the last decade" don't mean anything!
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Right, it seemed earlier.
Apple didn't learn those lessons with Apple Script either: there is a rigid sequence of characters that represents a valid program and if you're off by one character, tough. If you're lucky, the tool will guide you to what it wants. If not, you're stuck looking up the exact syntax so off to the reference manual you go, either way.
So there's minimal to looking up the syntax, whether it's based on some natural language phrase or a less wordy or ambiguous artificial language.
"friendly" or "natural" really is not a thing.
Over the last couple of weeks or so of me finally starting to use AI pair programming tools (for me, Cursor) I've been realizing that, much like when I play music, I don't really think about programming a natural language terms in the first place, it's actually been kind of hard to integrate an AI coding agent into my workflow mentally
Djikstra's clarity of expression and thoguht is indeed impressive. One nuance : he seems to completely equate ease of language with ability to do undetectable mistakes. I disagree: I know people whose language is extremely efficient at producing analogies that can shortcut for the listener many pages of painful mathematical proofs: for instance, convenu the emergence of complexity for many simple processes by a "swarming"
I do not believe this is his argument. He was making the point that there is a balance. You need to consider the context of the times, and remember that in this context a language like C is considered "high-level", not a language like Python. He later moves on to discuss formalism through mathematics (referencing Vieta, Descartes, Leibniz, and Boole), in how this symbolism is difficult to perform and many are adverse to it, but that through its birth we've been able to reap a lot of rewards. He precisely makes the claim that were we not to move to formal methods and instead maintain everyday language, we would still be stuck at the level of the Greeks.
Actually in one season of An Opinionated History of Mathematics, the host (a mathematician) specifically discusses the transition in the Greeks and highlights how many flaws there were in this system. How the slow move to mathematical formalism actually enabled correctness.
The point is that human language is much more vague. It has to be this way. But the formalism in symbolics (i.e. math) would similarly make a terrible language for computing. The benefit of the symbolic approach is the extreme precision, but it also means the language is extremely verbose. While in human languages we trade precision for speed and flexibility. To communicate what I have with a mathematical language would require several pages of text. Like he says, by approaching human language this shifts more responsibility to the machine.
- some people found error messages they couldn't ignore more annoying than wrong results
I wonder if this is a static vs dynamic or compiled vs interpreted reference.
Anyway I love it. Made me giggle that we are still discussing this today, and just to be clear I love both sides, for different things.
50 years ago, in "The Mythical Man-Month" Fred Brooks was already discussing the cost of cloud based solutions.
> Since size is such a large part of the user cost of a programming system product, the builder must set size targets, control size, and devise size-reduction techniques, just as the hardware builder sets component-count targets, controls component count, and devises count-reduction techniques. Like any cost, size itself is not bad, but unnecessary size is.
And the why of Agile, DDD and TDD:
> Plan the System for Change [...] Plan the Organization for Change
I love reading literate-programming software! The problem is that very few programmers are as skilled at writing clearly as are Knuth and Dijkstra. I think I read somewhere that book publishers receive thousands of manuscripts for each one they publish. Likewise, few programmers can write prose worth reading.
So many Dijkstra links amount to "Dijkstra on the [pejorative noun] of [whatever was bothering Dijkstra]."
I promise to upvote the next Dijkstra link that I see that does not present him as Ambrose Bierce with a computer.
Dijkstra is actually a time traveling Brian Rantrill
About every six to ten years I look in on the state of the art on making artificial human languages in which one cannot be misunderstood.
If we ever invent a human language where laws can be laid out in a manner that the meaning is clear, then we will have opened a door on programming languages that are correct. I don’t know that a programmer will invent this first. We might, but it won’t look that natural.
We are AI 1.0
Just like Web 1.0 - when the best we could think of to do was shovel existing printed brochures onto web pages.
In AI 1.0 we are simply shoveling existing programming languages into the LLM - in no way marrying programming and LLM - they exist as entirely different worlds.
AI 2.0 will be programming languages - or language features - specifically designed for LLMs.
A language is invented for a domain for precision and clarity the natural language cannot provide … trying to do the opposite would certainly create more work
> thanks to the carefully, or at least consciously designed formal symbolisms that we owe to people like Vieta, Descartes, Leibniz, and (later) Boole.
Please check this talk on the contributions of these mentioned people for the complementary form of deterministic AI (machine intelligence) namely logic, optimization and constraint programming in a seminal lecture by John Hooker [1].
I have got the feeling that if we combine the stochastic nature of LLM based NLP with the deterministic nature of feature structure trchnique based NLP (e.g. CUE), guided by logic, optimization and constraint programming we probably can solve intuitive automation or at least perform proper automation (or automatic computing as Dijkstra put it).
Apparently Yann LeCun also recently proposing optimization based AI namely inference through optimization, or objective driven AI in addition to data-driven AI [2].
Fun facts, you can see Donald Knuth asking questions towards the end of the JH's lecture presentation.
[1] Logic, Optimization, and Constraint Programming: A Fruitful Collaboration - John Hooker - CMU (2023) [video]:
https://www.youtube.com/live/TknN8fCQvRk
[2] Mathematical Obstacles on the Way to Human-Level AI - Yann LeCun - Meta - AMS Josiah Willard Gibbs Lecture at the 2025 Joint Mathematics Meetings (2025) [video]:
https://youtu.be/ETZfkkv6V7Y
Who is laughing now?
It is clear NLU can't be done in the reign of PL itself, there is never going to be natural language grammar that is precise as PL.
But LLM is a different kind of beast entirely.
One of the things LLM's/natural language programming brings is greater access. While the actual code may be crap, it opens up things to more people to play around and iterate on ideas without having to have a ton of knowledge. That is powerful by itself.
In the same vein, Asimov in 1956:
Baley shrugged. He would never teach himself to avoid asking useless questions. The robots knew. Period. It occurred to him that, to handle robots with true efficiency, one must needs be expert, a sort of roboticist. How well did the average Solarian do, he wondered?
This is also, inadvertently, an argument against managers.
Why talk to your team when you could just program it yourself?
But Dijkstra was writing long ago. I'm sure the situation is greatly improved today.
People are supposed to communicate symbolically with LLMs too
this clearly has nothing to do with the current main usages of LLMs, it's about using natural language as an interface to produce accurate results, as a further abstraction on top of general purpose languages.
What is the difference between those things?
LLMs take natural language as input and produces it as output (note that producing source code is the same thing). The algorithm that takes input and produces output is still written in mathematical precise symbols and needs to be accurate, even if the input and output aren't expressed in a formal language.
We will see if and when an algorithm can be parsed and executed from a natural language "source code" and if that is an improvement.
Also note that "source code" implies it is a code. Natural languages are not a code, that is, a unique mapping from a set to another.
Formal can be foolish, too. If you don't believe that, then I have a set for sale, with the property that it contains all sets that don't contain itself.
The problem you’re referring to arose precisely due to lack of formalism. It was a problem back when mathematicians were toying with naive set theory, one not based on axioms, but instead on intuitive terminology. Moving to axiomatic, more formal, set theory solved it.
The problem is the same, no matter if you look at it formally or informally. You could get your set theory axioms wrong, for example, but you would still be formal. Oh wait, you have a proof that set theory is consistent, right?
He didn’t understand the concept of the vibe. Here’s the best theory article I’ve read
https://www.glass-bead.org/article/a-theory-of-vibe/
The difference between the clarity of Dijkstra writing and the text at this link is astounding.
Vibes aren’t really about clarity, are they? The point is that a clear, programmatic approach is not the only effective computational mechanism for realizing intentions anymore.
Keep in mind that Dijkstra had some giant shoulders to stand on. This article is the very first one I’ve ever seen that directly dealt with vibes.
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What is with the mix of high academia and lowbrow language? Does the author think it’s cute? Covering for something?
> the computations involved in autoencoding… are mathematically intractable
OK, so he doesn’t understand what he’s talking about.
> Peli Grietzer recently finished his PhD in mathematically informed literary theory at Harvard Comparative Literature
How is this a thing.
looks at https://hyperscript.org
laughs nervously
Computers do what you tell them to, not what you want them to. This is naturally infuriating, as when a computer doesn't do what you want, it means you've failed to express your vague idea in concrete terms.
LLMs in the most general case do neither what you tell them, nor what you want them to. This, surprisingly, can be less infuriating, as now it feels like you have another actor to blame - even though an LLM is still mostly deterministic, and you can get a pretty good idea of what quality of response you can expect for a given prompt.
Why did mathematicians invent new symbols? Imagine if all of algebra, calculus, linear algebra looked like those word problems from antiquity? Natural language is not good for describing systems, symbolic forms are more compressed and be considered a kind of technology in its own right.
Dijkstra is entirely correct in this, and it's something I've been trying to urge people to recognize since the beginnings of this LLM wave.
There is inherent value in using formal language to refine, analyze, and describe ideas. This is, after all, why mathematical symbolism has lasted in spite of the fact that all mathematicians are more than capable of talking about mathematical ideas in their natural tongues.
Code realizes a computable model of the world. A computable model is made up of a subset of the mathematical functions we can define. We benefit greatly from formalism in this context. It helps us be precise about the actual relationships and invariants that hold within a system. Stable relationships and invariants lead to predictable behaviors, and predictable systems are reliable systems on the plan of human interaction.
If you describe your system entirely in fuzzily conceived natural language, have you done the requisite analysis to establish the important relationships and invariants among components in your system, or are you just half-assing it?
Engineering is all about establishing relative degrees of certainty in the face of the wild uncertainty that is the default modality of existence. Moving toward a world in which we "engineer" systems increasingly through informal natural language is a step backwards on the continuum of reliability, comprehensibility, and rigor. The fact that anyone considers using these tools and still thinks of themselves as an "engineer" of some kind is an absolute joke.
It's pretty obvious to me that this LLM business won't be economically feasible until it can actually produce better code than a team of humans could without it. The reason programmers are paid so highly is because their work is incredibly productive and valuable. One programmer can enable and improve the work of many hundreds of other people. Cost cutting on the programmer isn't worth it because it'll create greater losses in other places. Hence the high salaries. Every saving you make on the programmer is magnified a hundred times in losses elsewhere.
given that LLMs are going to produce code that is essentially an average of the code it has been trained on, which is all human code of varying quality, I don't see how the current methods are going to actually produce better code than humans do when working with their own domain-specific knowledge.
LLMs are usually used to describe goals or to provide feedback (correction or encouragement) towards its implementation.
Programming is about iteratively expressing a path towards satisfying said goals.
What LLMs are doing now is converting "requirements" into "formalizations".
I don't think Djikstra is wrong in saying - that performing programming in plain-language is a pretty weird idea.
We want to concretize ideas in formalisms. But that's not what any human (including Djikstra) starts with... you start with some sort of goal, some sort of need and requirements.
LLMs merely reduce the time/effort required to go from goals -> formalism.
TLDR: Requirements != Implementation
> The foolishness of "natural language programming"
Wasn't that the actual motivation behind the development of SQL?
IIRC, SQL was something that "even" business people could code in because it was closer to "natural language".
When you see the monstrosity the motivation gave birth to, I think the "foolish" argument was well warranted at the time.
Of course, in these days of LLM's, Dijkstra's argument isn't as clear cut (even if LLM's aren't there yet, they're getting much closer).
(2010) by the way, which makes this article all the more impressive.
I was under the assumption this was a current body of work seeing as Dijkstra spoke so well about the possibilities but this just goes to show some people were ahead of their tike with their worries.
Also adding your home address to something you write / publish / host on the internet is pretty hardcore.
Dijkstra died in 2002, so I assume 2010 is just the date of transcription
1979. https://doi.org/10.1007/BFb0014656
Is this necessarily a stance against LLM? LLMs can generate real code, ideally functional, strict code instead of the slop they do these days.
> 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.
Seeing as transformers are relatively simple to implement…
It stands to reason he was, in some sense, right. LLMs are damn easy to use.
Unless you include "getting correct results" within the scope of "use".
Personal context is tacit experience. Cultural context is aggregate.
Wittgenstein stated that the differences between personal and cultural language makes it impossible to agree on anything foundational to philosophy.
Godel did something similar to discrete structures by hacking self reference -- a kind of recursive self reference with no exit state.
I think pair programming with an LLM is interesting. You get to compare personal context with aggregate context. Plus, the external feedback helps me break out of excessive personal self reference.
Heh, Popper wrote an entire book that says pretty much the opposite, but Wittgenstein attacked him with a poker once so maybe it was payback.