[Dumbledumb]

This blog post is full of small inconsistencies that make it read like a low quality SEO piece.

> We also extract a shared context file (shared-mr-context.txt) from the coordinator's prompt and write it to disk. Sub-reviewers read this file instead of having the full MR context duplicated in each of their prompts. This was a deliberate decision, as duplicating even a moderately-sized MR context across seven concurrent reviewers would multiply our token costs by 7x.

No, it would not, because neither is the prompt of the subagent 100% of its token usage, nor will the "shared-mr-context.txt" which is then being read have a size of zero compared to the creation of this shared context.

> You don't need seven concurrent AI agents burning Opus-tier tokens to review a one-line typo fix in a README.

Yeah, well you wouldn't have anyways. Earlier in the post it says that Opus is "exclusively for the Review Coordinator".

[Dumbledumb]

Also this seems plain wrong. Input token caching has now idea whether you @include the file or copy the contents into the prompt. That is handled entirely by opencode and, all else being equal, has no bearing on the cache ability of a trace.

> Our cache hit rate sits at 85.7%, which saves us an estimated five figures compared to what we would pay at full input token pricing. This is partially thanks to the shared context file optimisation — sub-reviewers reading from a cached context file rather than each getting their own copy of the MR metadata, but also by using the exact same base prompts across all runs, across all merge requests.

[fg137]

Looks like they need some sort of agent to review their blog posts as well.