I'm not convinced code review should be about "consistency" or "system memory" at all. Half of the dysfunction in large codebases comes from reviewers over-optimizing for long-term purity instead of short-term progress.
i'm impressed how much the runtime is optimized across so many layers - pretty rare to see an interpreted language push this far without a JIT. Do you see this approach eventually rivaling JIT performance for real world workloads, esp where predictability matters?
Not super convinced by this analogy. Tooling and convenience feel secondary in math. If formalization doesn't help us do better mathematics, not just more structured mathematics, I'm pretty skeptical these benefits will justify the cost.
skimmed the paper - how well does this plug into real serving stacks (paged-kv, vllm, speculative decoding, caching)? layer-wise top-k chunk voting sounds compatible, but does it fight with RoPE scaling or sliding-window kv eviction policies?
isn't this basically rag with a different entrypoint? following links works when the corpus is well-authored/ hierarchical but most real data isn't. how do you handle relevance ranking/stale links/huge fan-out?? "just follow hyperlinks" can blow up the context window just as easily
did you see the relocations for the main binary applied before or after the linker resolves its own symbols? the ordering always feels like black magic when you step through it in a debugger