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Orchestrating AI code review at scale

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Re: Orchestrating AI code review at scale

#61

Earlier quoted context omitted.

Well, AI costs are definitely going to go down at least 90% in the next ~18 months for the same quality of output (and probably 90% again in the 24 months after). Are you sure it's going to make sense to pay someone to do that moving forward? I don't think it's worth it now, by the way. It's definitely not going to be worth it in the near future. Can we even blink for $0.002? What happens when the next 90% increase i…

> Well, AI costs are definitely going to go down at least 90% in the next ~18 months for the same quality of output (and probably 90% again in the 24 months after As far as I can see, token costs have been steadily increasing over the past few months, so I’m not sure that buying the hype that another 90% cost reduction is just around the corner is warranted.

Doesn’t seem like token costs, specifically, are increasing.

Opus cut its token pricing by 66% 6 months ago and it had previously been that higher price consistently for a year and a half (since that model launch).

GPT’s latest model is harder to track since it’s not named, but it’s historically inline with its history.

Not to mention what’s happening with other models like DeepSeek, GLM, and Kimi.

It seems to me the bigger change in costs is based on token appetite. People are discovering agentic capabilities are stronger than they used to be and use cases have broadened because of that. They’ll eventually discover too that these alternative models offer 95% of the intelligence at 20% of the price.

Re: Orchestrating AI code review at scale

#62

>Code review is a fantastic mechanism for catching bugs and sharing knowledge "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…

Well, AI costs are definitely going to go down at least 90% in the next ~18 months for the same quality of output (and probably 90% again in the 24 months after). Are you sure it's going to make sense to pay someone to do that moving forward? I don't think it's worth it now, by the way. It's definitely not going to be worth it in the near future. Can we even blink for $0.002? What happens when the next 90% increase i…

On local models that cost power (post initial hardware cost), makes sense. My work is building this out and I think it's solid. But until we can use our own hardware and local models the long term cost is a big question mark.

Re: Orchestrating AI code review at scale

#63
I’ve built something similar internally, but under the hood it’s mostly codex exec + Git worktrees. The main advantage over diff-only review is that it can walk the entire codebase, trace dependencies, and understand architectural or cross-system impacts instead of only looking at the changed files. The tradeoff is that it’s noticeably more expensive to run. I'm still experimenting on it but I quite like this approach so far.

Re: Orchestrating AI code review at scale

#64
How do you all handle code review for projects that use specific frameworks or libraries? I write a lot of PySpark and the "flavour" of code is sort of different than traditional Python. AI code reviewers tend to nitpick conventions and common patterns in these libraries, so I find it not very helpful.

Re: Orchestrating AI code review at scale

#65

How do you all handle code review for projects that use specific frameworks or libraries? I write a lot of PySpark and the "flavour" of code is sort of different than traditional Python. AI code reviewers tend to nitpick conventions and common patterns in these libraries, so I find it not very helpful.

Also write a lot of pyspark and the best I can say is to let the repo become its own style guide, can enforce on review with “make sure code is consistent with patterns and style in this module” seems to work well enough.

Re: Orchestrating AI code review at scale

#66
How do you all handle code review for projects that use specific frameworks or libraries? I write a lot of PySpark and the 'flavour' of code is different... AI code reviewers tend to nitpick conventions and common patterns in these libraries, so I find it not very helpful.
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