Correctness and composability bugs in the Julia ecosystem

The middle part is the concatenation of all the middle links and handles the complexity necessary to translate from language to output. As we trying to make A less complex, the middle [super compiler] will get more complex, and more buggy because of the complexity.

I believe the fundamental issue with this model is the lack of feedback. A feedback on output, and A makes change (in A) until output get correct. With the big complex and opaque middle, for one, we can't get full feedback on output -- that is the correctness issue. The more complex the middle gets, the less coverage the testing can achieve. For two, even with clear feedback -- a bug -- A cannot easily fix it. The logic from A to output is no longer understandable.

I believe the solution is to abandon the pursuit of magic solution of A -> [super compiler] -> output but to focus on how to get feedback from every link in A->B->C->D->...->compiler->binary->output

For one this give A a path to approach and handle complexity. A can choose to check on B or C or ... directly on output, depending on A's understanding and experience. For the least, A can point fingers correctly.

For two, this provides a path to evolve the design. The initial design on which handles which or how much complexity is no longer crucial. Each link, from A, to B, to C, ... to compiler can adjust and shift the complexity up and down, and eventually settle down to a system that fits the problem and team.

I believe this is how natural language works. Initially A tells B to "get an apple" and they directly feedback on the end result of what apple B gets to A and may alter layer of A by expanding into more details until it gets the right result. Then, some of the details will be handled by B and A can feed back on B's intermediate response for behavior. As the world gets more complex, the complexity at the layer A stays finite but we added middle layers. Usually, A only need feedback on its immediate link (B) and the final output, but B needs to be able to feedback on its next immediate link, and if A is capable, A may choose to cut-out the one of his middle man.

The LV rewrite will. Some day, I'd like to have it target accelerators, but unlike fusion, I've not actually put any research/engineering into it so can't make any promises.

But my long term goal is that simple loops in -> optimized anything you want out. Enzyme also deserves a shout out for being able to generate reverse mode AD loops with mutation.

That being said, I have learned the hard way not to ignore or trivialize these review inputs, even if they are not immediately actionable as-provided. Users and reviewers are really good at figuring out weak areas or flaws even if they can't articulate the solutions, fully unentangle related issues, or do all the generalization or abstraction that would make those issues easier to address. There is usually some truth underlying the negative feedback.

The article looks to potentially be an example of an expert review in the above vein. If you are able to take a step back, you might find the HN discussion on this submission to provide further inputs to help figure out how any of this should be channeled into language, practice, and ecosystem improvements. Certainly there is more to work with here than little "to take away other than that we should work even harder on bugs and quality."