I do want to point out that I'm thinking of doing this on a very limited website, not a general purpose thing that replicates any possible website. When I imagine the complexity of a modest CMS or an online mortgage calculator I think that it is much less complex than translating human languages. The fact that web code has to be so much more precise than human language actually makes the task easier. But to be fair, I'm all talk at this point with no code to show for it. So I will keep these comments in mind, this thread has been helpful for helping me think through some of this stuff.

For session based variables? Not sure, either it all becomes stateless and the code has to read everything from storage for each reqeuest .... or maybe the lstm is able to model something like an entire user session and remember the stuff that the original app would have put in the session.

That Andrej Karpathy article that I linked to two comments above ... he pointed out, in a different blog post, that regular neural networks can approximate any pure function. Recurrent neural networks like the LSTM can approximate any computer program. It is because they can propagate state from step to step that allows them to do this.

As far as it being 100X slower, well at a certain point I will be willing to take your money :)

I just don't know how you can achieve that with static cache ... only if somebody else requested that exact mortgage calculation before and it is still in the cache.

Also, my idea of the "given input" from the earlier comment would have to include results of sql queries that would form the entire input to the LSTM.

But honestly I think over trained auto encoders can be used as hash maps. That would be an application more in line with what I think you are saying.