Posted by evakhoury 12/16/2025
https://interjectedfuture.com/the-best-way-to-learn-might-be...
You might find it useful. I also caveat the experience and recount some of the pitfalls, which you might enjoy as a skeptic.
Martin Klepmann seemed to like it too. https://bsky.app/profile/martin.kleppmann.com/post/3m7ugznx4...
How big is the effort of writing a specification for an application versus implementing the application in the traditional way? Can someone with more knowledge chime in here please?
Take sorting a list for example. The spec is quite short.
- for all xs: xs is a permutation of sort(xs)
- for all xs: sorted(sort(xs))
Where we can define "xs is a permutation of ys" as "for each x in xs: occurrences(x, xs) = occurrences(x, ys)"
And "sorted(l)" as "forall xs, x, y, ys: (l = xs ++ [x, y] ++ ys) => x < y".
A straightforward bubble or insertion sort would perhaps be considered as simple or simpler than this spec. But the sorting algorithms in, say, standard libraries, tend to be significantly more complex than this spec.
However I don't still believe in vibecoding full programs. There are too many layers in software systems, even when the program core is fully verified, the programmer must know about the other layers.
You are Android app developer, you need to know what phones people commonly use, what kind of performance they have, how the apps are deployed through Google App Store, how to manage wide variety of app versions, how to manage issues when storage is low, network is offline, battery is low and CPU is in lower power state.
Problem is - while these will be resolved (in one way or another) - or left unresolved, as the user will only test the app on his device and that LLM "roll" will not have optimizations for the broad range of others - the user is still pretty much left clueless as to what has really happened.
Models theoretically inform you about what they did, why they did it (albeit, largely by using blanket terms and/or phrases unintelligible to the average 'vibe coder') but I feel like most people ignore that completely, and those who don't wouldn't need to use a LLM to code an entirety of an app regardless.
Still, for very simple projects I use at work just chucking something into Gemini and letting it work on it is oftentimes faster and more productive than doing it manually. Plus, if the user is interested in it, it can be used as a relatively good learning tool.
What will happen instead is a more general application of AI systems to verifying software correctness, which should lead to more reliable software. The bottleneck in software quality is in specifying what the behavior needs to be, not in validating conformance to a known specification.
If anyone does write a specification, the "AI" won't get even past the termination proof of a moderately complex function, which is the first step of accepting said function in the proof environment. Before you can even start the actual proof.
This article is pretty low on evidence, perhaps it is about getting funding by talking about "AI".