[js8]

Personally, I think we're using LLMs wrong for programming. Computer programs are solutions to a given constraint logic problem (the specs).

We should be using LLMs to translate from (fuzzy) human specifications to formal specifications (potentially resolving contradictions), and then solving the resulting logic problem with a proper reasoning algorithm. That would also guarantee correctness.

LLMs are a "worse is better" kind of solution.

[jmalicki]

> We should be using LLMs to translate from (fuzzy) human specifications to formal specifications (potentially resolving contradictions)

Agreed! This is why having LLMs write assembly or binary, as people suggest, is IMO moving in the wrong direction.

> then solving the resulting logic problem with a proper reasoning algorithm. That would also guarantee correctness.

Yes! I.e. write in a high-level programming language, and have a compiler, the reasoning algorithm, output binary code.

It seems like we're already doing this!

[zozbot234]

Full program inference from specs is actually a very hard problem, because the compiler/SAT solver cannot autonomously derive loop invariants (or, similarly, inductive hypotheses) that are necessary to write correct code. So using a LLM that can look at the spec and provide a heuristic solution makes a lot of sense. Obviously the solution still has to be verified, though.

[iLemming]

> using LLMs wrong for programming

Perhaps you meant to say "coding", not "programming". AI is immensely helpful for programming. Coding is just the last, and in a proper programming session sometimes even unnecessary step - there are times when an adequate investigation requires deleting code rather than writing new one, or writing pages of documentation without a single code change.

You have to be a detective and know what threads to pull to rope in the relevant data, digging inductively and deductively - soaring high to get the "big picture" of things and diving into the depths of a single code line change.

I've been developing software for decades now (not claiming to be great, but at least I think I've built certain intuition and knack for it), and I always struggled with the "story telling" aspect of it - you need to compose a story about every bug, every feature request - in your head, your notes, your diagrams. A story with actors, with plot lines, with beginning, middle, and end. With a villain, a hero, and stakes. But software doesn't work that way. It's fundamentally an exploratory, iterative, often chaotic process. You're not telling what happened - you're constructing a plausible fiction that satisfies the format. The tension I felt for decades is that I am a systems thinker being asked to repeatedly perform as a narrator, and that is hard.

Modern AI is already capable of digging up me the details for my narrative - I gave it access to everything - Slack, Jira, GitHub, Splunk, k8s, Prometheus, Grafana, Miro, etc. - and now I can ask it to explain a single line of code - including historical context, every conversation, every debate, every ADR, diagram, bug and stack trace - it's complete bananas.

It doesn't mean I don't have to work anymore, if anything, I have to work more now, because now I can - the reasons become irrelevant (see Steve Jobs' janitor vs. CEO quote). I didn't earn a leadership role - AI has granted it? Forced me into it? Honestly, I don't know anymore. I have mixed feelings about all of it. It is exciting and scary at the same time. Things that I dreamed about are coming true in a way that I couldn't even imagine and I don't know how to feel about all that.

[derrak]

In case you’re not familiar, I will point you to the classical program synthesis literature. There the task is to take a spec written in say first-order logic, and output a program that satisfies this spec.

I think the biggest barrier to adoption of program synthesis is writing the spec/maintaining it as the project matures. Sometimes we don’t even know what we want as the spec until we have a first draft of the program. But as you’re pointing out, LLMs could help address all of these problems.