In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.
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A useful mental model here is shared state versus dedicated state. Because standard containers share the host kernel, they also share its internal data structures like the TCP/IP stack, the Virtual File System caches, and the memory allocators. A vulnerability in parsing a malformed TCP packet in the kernel affects every container on that host. Stronger isolation models push this complex state up into the sandbox, exposing only simple, low-level interfaces to the host, like raw block I/O or a handful of syscalls.