The kernel metaphor is interesting but I think the hard questions are in the gaps between the metaphor and reality.
Real kernels have well-defined failure modes. Process crashes, OOM kills, filesystem corruption — there are decades of battle-tested recovery patterns. When your "kernel" is a language model, failure is probabilistic and often silent. The healer agent can fix a broken file, but can it detect when a sub-agent made a subtly wrong architectural decision that will cascade later?
The 5 sub-agent model maps loosely to microservices, but coordination is the part that kills you. How do you handle conflicting state when two sub-agents modify overlapping resources? File-based IPC is simple but gives you no atomicity guarantees.
Genuine question: what does the failure rate look like in practice? When you say "self-healing," is that recovering from syntax errors in generated code, or recovering from semantic failures where the agent did something technically valid but wrong?
super valid concerns, but i believe all of them fixable.
true that llms are stochastic by nature, but with models like opus 4.5/4.6, the quality is just another magnitude. and all these can have live tests, giving feedback for the code written.
the "kernel" here is merely an analogy, just like how we don't care about lsp and all the details in old ide's, and just use claude code and such, the ai native os will abstract away all these details, like kernel, ...
the failure rate will depend on what type of projects you're asking and how much you're customizing your os. here is where the tests needs to be in place (and the healer jumps to fix the config format,...)
The kernel metaphor is interesting but I think the hard questions are in the gaps between the metaphor and reality.
Real kernels have well-defined failure modes. Process crashes, OOM kills, filesystem corruption — there are decades of battle-tested recovery patterns. When your "kernel" is a language model, failure is probabilistic and often silent. The healer agent can fix a broken file, but can it detect when a sub-agent made a subtly wrong architectural decision that will cascade later?
The 5 sub-agent model maps loosely to microservices, but coordination is the part that kills you. How do you handle conflicting state when two sub-agents modify overlapping resources? File-based IPC is simple but gives you no atomicity guarantees.
Genuine question: what does the failure rate look like in practice? When you say "self-healing," is that recovering from syntax errors in generated code, or recovering from semantic failures where the agent did something technically valid but wrong?
super valid concerns, but i believe all of them fixable.
true that llms are stochastic by nature, but with models like opus 4.5/4.6, the quality is just another magnitude. and all these can have live tests, giving feedback for the code written.
the "kernel" here is merely an analogy, just like how we don't care about lsp and all the details in old ide's, and just use claude code and such, the ai native os will abstract away all these details, like kernel, ...
the failure rate will depend on what type of projects you're asking and how much you're customizing your os. here is where the tests needs to be in place (and the healer jumps to fix the config format,...)