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548 points kmelve | 1 comments | | HN request time: 0.001s | source
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ale ◴[] No.45109875[source]
It’s about time these types of articles actually include the types of tasks being “orchestrated” (as the author writes) that aren’t just plain refactoring chores or React boilerplate. Sanity has quite a backlog of long-requested features and the message here is that these agents are supposedly parallelizing a lot of the work. What kind of staff engineer has “80% of their code” written by a “junior developer who doesn't learn“?
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bakugo ◴[] No.45110352[source]
Actually providing examples of real tasks given to the AI and the subsequent results would break the illusion and give people opportunities to question the hype. Can't have that.

We'll just keep getting submission after submission talking about how amazing Claude Code is with zero real world examples.

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1. vincent_builds ◴[] No.45116130[source]
Author here. It's fair enough. I didn't give real-world examples; that's partially down to what I typically work on. I usually work in brownfield backend logic in closed-source applications that don't showcase well.

Two recent production features:

1. *Quota crossing detection system* - Complex business logic for billing infrastructure - Detects when usage crosses configurable thresholds across multiple metric types - Time: 4 days parallel work vs ~10 days focused without AI

   The 3-attempt pattern was clear here:
   - Attempt 1: DB trigger approach - wouldn't scale for our requirements
   - Attempt 2: SQL detection but wrong interfaces, misunderstood counter vs gauge metrics
   - Attempt 3: Correct abstraction after explaining how values are stored and consumed
2. *Sentry monitoring wrapper for cron jobs* - Reusable component wrapping all cron jobs with monitoring - Time: 1 day parallel vs 2 days focused

Nothing glamorous, but they are real-world examples of changes I've deployed to production quicker because of Claude.