That directory is huge already! I guess the index.md helps the agent find what it needs, but even the markdown file is very long - this would consume a ton of tokens.
Also I wonder who/what decides what papers go in there.
In the blog post, the agent is allowed to do its own search.
> The full setup works with any project that has a benchmark and test suite.
so having a clear and measurable verification step is key.
Meaning you can't simply give an AI agent a vague goal e.g. "improve the quality of the codebase" because it's too general.
I am sure this would works well in general. There is a challenge wrt to how to make them communicate effectively to e.g. 1) avoid duplicative work and 2) allow them to combine/overlay each others' findings to yield even better results
it's written to _actively_ avoid any signs of AI generated code when "in a PUBLIC/OPEN-SOURCE repository".
Also, it's not about you. Undercover mode only activates for Anthropic employees (it's gated on USER_TYPE === 'ant', which is a build-time flag baked into internal builds).
Author here.
I've seen the docs you linked to: Slurm uses "gang scheduling" to mean something specific (timesliced oversubscription where jobs alternate on shared resources).
I'm using the term in its broader CS sense: all-or-nothing co-scheduling of related processes across multiple processors [1].
This is the definition used across the K8s ecosystem e.g. Volcano [2], Kueue [3], and its Coscheduling plugin all define gang scheduling as "all or nothing" allocation.
I still stand by the origianl claim:
Slurm allocates multi-node jobs atomically, while vanilla K8s doesn't.
its default scheduler places pods as resources become available, leading to partial allocations and deadlocks for distributed training.
It's just a terminology clash. Thanks for the comment anyway.
Also I wonder who/what decides what papers go in there.
In the blog post, the agent is allowed to do its own search.