[skydhash]

> Modern AI tools are just the next step to abstracting away syntax as a gatekeeper function, but the need for precise systemic thinking is as glaringly necessary as ever.

Syntax is not a gatekeeper function. It’s exactly the means to describe the precise systemic thinking. When you’re creating a program, you’re creating a DSL for multiple subsystem, which you then integrate.

The subsystem can be abstract, but we usually define good software by how closely fitted the subsystem are to the problem at hand, meaning adjustments only need slight code alterations.

So viewing syntax as a gatekeeper is like viewing sheet music as a gatekeeper for playing music, or numbers and arithmetic as a gatekeeper for accounting.

[buu700]

The difference is that human language is a much more information-dense, higher-level abstraction than code. I can say "an async function that accepts a byte array, throws an error if it's not a valid PNG image with a 1:1 aspect ratio and resolution >= 100x100, resizes it to 100x100, uploads it to the S3 bucket env.IMAGE_BUCKET with a UUID as the file name, and retries on failure with exponential backoff up to a maximum of 100 attempts", and you'll have a pretty good idea of what I'm describing despite the smaller number of characters than equivalent code.

I can't directly compile that into instructions which will make a CPU do the thing, but for the purposes of describing that component of a system, it's at about the right level of abstraction to reasonably encode the expected behavior. Aside from choosing specific libraries/APIs, there's not much remaining depth to get into without bikeshedding; the solution space is sufficiently narrow that any conforming implementation will be functionally interchangeable.

AI is just laying bare that the hard part of building a system has always been the logic, not the code per se. Hypothetically, one can imagine that the average developer in the future might one day think of programming language syntax in the same way that an average web developer today thinks of assembly. As silly as this may sound today, maybe certain types of introductory courses or bootcamps would even stop teaching code, and focus more on concepts, prompt engineering, and developing/deploying with agentic tooling.

I don't know how much learning syntax really gatekeeps the field in practice, but it is something extra that needs to be learned, where in theory that same time could be spent learning some other aspect of programming. More significant is the hurdle of actually implementing syntax; turning requirements into code might be cognitively simple given sufficiently baked requirements, but it is at minimum time-consuming manual labor which not everyone is in a position to easily afford.

[skydhash]

> and you know exactly what I'm describing.

I won't unless both you and I have a shared context which will tie each of these concept to a specific thing. You said "async function", and there's a lot of languages that don't have that concept. And what about the permissions of the s3 bucket, what's the initial time of the wait time? And what algorithm for the resizing? What if someone sent us a very big image (let say the maximum that the standard allows).

These are still logic questions that have not been addressed.

The thing is that general programming languages are general. We do have constructs like procedure/functions and class, that allows us for a more specialized notation, but that's a skill to acquire (like writing clear and informative text).

So in pseudo lisp, the code would be like

   (defun fn (bytes)
     (when-let\* ((png (byte2png bytes))
                 (valid (and (valid-png-p png)
                             (square-res-p png)))
                 (small-png (resize-image png))
                 (bucket (get-env "IMAGE_BUCKET"))
                 (filename (uuid)))
       (do-retry :backoff 'exp
                 (s3-upload bucket small-png))))

And in pseudo prolog

  square(P) :- width(P, W), height(P, H), W is H.
  validpng(P, X) :-  a whole list of clauses that parses X and build up P, square(P).
  resizepng(P) :- bigger(100,100, P), scale(100, 100, P).
  smallpng(P, X) :- validpng(P, X), resizepng(P).
  s3upload(P): env("IMAGE_BUCKET", B), s3_put(P, B, (exp_backoff(100))))
  fn(X) :-  smallpng(P, X), s3upload(P)

So what you've left is all the details. It's great if someone already have an library that already does the thing, and the functions has the same signature, but more often than not, there isn't something like that.

Code can be as highlevel as you want and very close to natural language. Where people spend time is the implementation of the lower level and dealing with all the failure modes.

[buu700]

Details like the language/stack and S3 configuration would presumably be somewhere else in the spec, not in the description of that particular function.

The fact that you're able to confidently take what I wrote and stretch it into pseudocode with zero deviation from my intended meaning proves my point.

[skydhash]

To draft a spec like this, it would take more time and the same or more knowledge than to just write the code. And you still won’t have reliable results, without doing another lengthy pass to correct the generated code.

I can create a pseudocode because I know the relevant paradigm as well as how to design software. There’s no way you can have a novice draft pseudo-code like this because they can’t abstract well and discern intent behind abstractions.

[buu700]

I don't agree that it would take more time. Drafting detailed requirements like that to feed into coding agents is a big part of how I work nowadays, and the difference is night and day. I certainly didn't spend as much time typing that function description as I would have spent writing a functional version of it in any given language.

Collaborating with AI also speeds this up a lot. For example, it's much faster to have the AI write a code snippet involving a dependency/API and manually verify the code's correctness for inclusion in the spec than it is to read though documentation and write the same code by hand.

The feat of implementing that function based on my description is well within the capabilities of AI. Grok did it in under 30 seconds, and I don't see any obvious mistakes at first glance: https://grok.com/share/c2hhcmQtMw_fa68bae1-3436-404b-bf9e-09....

[skydhash]

I don't have access to the grok sample you've shared (service not available in my region)

Reading the documentation is mostly for gotchas and understanding the subsystem you're going to incorporate in your software. You can not design something that will use GTK or sndio without understanding the core concepts of those technologies. And if you know the concepts, then I will say it's easier and faster to write the code than to write such specs.

As for finding samples, it's easy on the web. Especially with GitHub search. But these days, I often take a look at the source code of the library itself, because I often got questions that the documentation don't have the answer for. It's not about what the code I wrote may do (which is trivial to know) but what it cannot do at all.