"Sharing knowledge" is one of the first phrases in the article, and highlighted as a key benefit of code review. But the loss to human-capital from this process is never examined in the post.
> Trivial reviews (typo fixes, small doc changes) cost 20 cents on average
They did around 25,000 of these runs (about 20% of total). So CF spent $5k in the period making language models run through PRs which were <10 lines long. I get that CF engineers are paid well, but the labour cost of having an intern/entry level engineer spend ~30-60s looking through these is likely close to $0.20, and that engineer builds some human-capital while they're at it.
> 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".
we are finding lots of value in self review. its the “imagine you are doing a synchronous paired review with someone - anything that is difficult to explain, has a code smell, doesnt fit the architecture of the system around you, write a comment.” then at the end, agents do a good job of looping over PR comments.
the second thing would be a guided, educational code review tool - there are a few attempts at this, but nothing that has a good enough interface to actually stick. organize hunks by semantic importance, spend some tokens exploring the surrounding systems, showing how new code, public apis and data model flow with the existing design, and allow a human to traverse larger PRs more quickly.
thank you to cloudflare for publishing this.
I’d prefer to have that happen as some sort of pre commit hook, before a merge request is sent. The feedback loop might be a bit faster and the process might produce less noise this way.
I think approaches like this don't need to run other than locally. Maybe integrated as pre-push hook. The system is nondeterministic, so it's at odds with the purpose of CI.
The ROI here is so high that I don't mind using the strongest model available for the actual code review. I don't trust Sonnet and such. Just let Opus or GPT 5.5 do the whole thing and pay a bit more for less complexity.
I had the same problem in my recursive agent harness. It would always come back, but it could sometimes take up to 10 minutes depending. I fixed this by adding a required "purpose" argument to every tool and call/return event. As the recursive evaluation proceeds, every single thing that happens streams incremental purpose text to the user's browser (also using the magic of JSONL for this). The incremental progress events contain the purpose and a detail section (tool arg JSON) that the user can expand/collapse.
They apparently think they need to cash in on AI by serving models and at the same time blocking scrapers. So they need to fuel the hype by pretending to use it.
This shows how the US economy is fundamentally broken: companies that provide a useful service (in theory, if you discount SSL MITM and turnstile gatekeeping) struggle, quasi-religious scams like OpenAI and Anthropic get funded by mentally ill Boomers and Gen-Xers.
