This is the second time this week on HN that I've seen people suggesting object pools to solve memory pressure problems.
I generally think it's because people aren't experienced with diagnosing and fixing memory pressure. It's one of the things I do pretty frequently for my day job. I'm fortunate enough to be the "performance" guy at work :).
It'll always depend on what the real issue is, but generally speaking the problem to solve isn't reinventing garbage collection, but rather to eliminate the reason for the allocation.
For example, a pretty common issue I've seen is copying a collection to do transformations. Switching to streams, combining transformation operations, or in an extreme case, I've found passing around a consumer object was the way to avoid a string of collection allocations.
Even the case where small allocations end up killing performance, for example like the autoboxing example of the OP, often the solution is to either make something mutable that isn't, or to switch to primitives (Valhalla can't come soon enough).
Heck, sometimes even an object cache is the right solution. I've had good success reducing the size of objects on the heap by creating things like `Map<String, String>` and then doing a `map.computeIfAbsent(str, Function.identity());` (Yes, I know about string interning, no I don't want these added to the global intern cache).
Regardless, the first step is profiling (JFRs and heap dumps) to see where memory is spent and what is dominating the allocation rate. That's a first step that people often skip and jump straight to fixing what they think is broken.
Doing it to avoid memory pressure generally means you simply have a bad algorithm that needs to be tweaked. It's very rarely the right solution.