Developer 7d00e65a0a feat: load-aware actor placement + work stealing research ... Replace blind round-robin placement with load-aware strategy that reads per-worker stats (actor count + mailbox depth) and biases toward lighter workers. Falls back to round-robin when stats are equal (initial burst). Deep research on work stealing across Tokio (steal-half, LIFO slot), Go (M:N scheduler), BEAM (proactive migration), and ForkJoinPool. Full actor migration is feasible but deferred due to message-loss window. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>		2026-02-12 11:46:53 +00:00
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runtime_api.rs	feat: load-aware actor placement + work stealing research	2026-02-12 11:46:53 +00:00
test_python.py	fix: stats datatypes and refactor channel signature (#28)	2026-02-10 07:35:58 +00:00
transport_api.rs	feat: transport protocol (#27)	2026-02-09 19:05:37 +00:00