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Using LLMs at Oxide

(rfd.shared.oxide.computer)
694 points steveklabnik | 1 comments | | HN request time: 0.001s | source
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thundergolfer ◴[] No.46178458[source]
A measured, comprehensive, and sensible take. Not surprising from Bryan. This was a nice line:

> it’s just embarrassing — it’s as if the writer is walking around with their intellectual fly open.

I think Oxide didn't include this in the RFD because they exclusively hire senior engineers, but in an organization that contains junior engineers I'd add something specific to help junior engineers understand how they should approach LLM use.

Bryan has 30+ years of challenging software (and now hardware) engineering experience. He memorably said that he's worked on and completed a "hard program" (an OS), which he defines as a program you doubt you can actually get working.

The way Bryan approaches an LLM is super different to how a 2025 junior engineer does so. That junior engineer possibly hasn't programmed without the tantalizing, even desperately tempting option to be assisted by an LLM.

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1. govping ◴[] No.46180863[source]
Interesting tension between craft and speed with LLMs. I've been building with AI assistance for the past week (terminal clients, automation infrastructure) and found the key is: use AI for scaffolding and boilerplate, but hand-refine anything customer-facing or complex. The 'intellectual fly open' problem is real when you just ship AI output directly. But AI + human refinement can actually enable better craft by handling the tedious parts. Not either/or, but knowing which parts deserve human attention vs which can be delegated.