Current models aren't capable of that, but that doesn't mean it's not possible.
If you made models able to code to long spec, you would be left with the hard issue of having to write them.
Like if you show the LLM a page, can the LLM review the page and then spit out a review that is close to what a human would say about the page?
Software was always that way, though.
> given the sufficiently smart compiler
For those unaware, this is a similar quote used by compiler proponents. The first full compiler was created in 1957 (+/- 70 years ago) and the "sufficiently smart compiler" never happened, hand written code from the best coders still is faster. Now, that doesn't mean that compilers didn't do the job well enough, we just accepted that 90-95% of the top speed was enough for almost everything.
To the LLM one shotting point, it took 30 (40?) years for compilers to be good enough for the mass market. Caveat early adopter and investor.
Plus what pyrale said.
The agentic engineering paradigm is just a narrative trend pushed by AI companies to get people to 10x their token consumption per prompt. It plays into people's laziness and addiction to dopamine too causing addict like behavior in people that fall prey to this trend.
If I do that, I'm literally slower then just doing the change without sufficiently specifying it to the model.
I can see how a junior dev or generally someone that's not particularly knowledgeable about the language or framework they're working with may benefit from such usage, but for experienced people there is very little value in that approach.
I say this because I've just had to face this decision this month with Copilot introducing the usage based billing. I attempted to scale back my usage, first with non-opus - output essentially became discardable as it continually hallucinated no existing fields in the responses of Apis etc... Then my scoping the changes smaller and smaller, until I ultimately gave up and reduced usage to just generating tests.
What is tested often makes no sense at all, completely implausible edge cases are tested on internals, while it doesn't create tests for the overall application using user events.
And some things in these test cases are downright ridiculous: instead of instantiating your classes, it sets up some barebones fake objects reimplementing some of the behavior of your actual class, then ignores the TypeScript errors via force cast or similar.
Then it proceeds to slap some test ids on the output, stubs components and dependencies more or less randomly, adds some assertions on test ids and calls it a day.
Apparently that's good enough for many colleagues to open a MR for that garbage.
That said, at home with SOTA models I happily hand large units of work to it, outsource much of the thinking, and get workable results. I think this is the future.
I see little value in throwing a ton of context at an llm and waiting 10-20 minutes for a coin flip on whether or not its going to produce junk. I'd rather do quick 60 second turns, get most of the way there and fix the rest myself if I have to. I'd rather honestly just not use them.
Everyone that I've ever interacted with and claims to prompt in "seconds" actually needs multiple minutes to think about the solution they want the model to implement - and then need twice as long to formulate that into a sentence which provides the model enough context to actually do that
So the more realistic estimates are "I'd rather spend the 2 minutes just implementing the minor change myself, instead of spending 1.5 minutes thinking about it, then 2.5 minutes writing the prompt and then waiting 1 minute for it to finish"
That's the main value I've been getting out of coding agents. I have them do (comparatively) simpler tasks or explorative tasks in the background while I'm in a meeting, doing code reviews, or otherwise working on something else.