"Half the money I spend on advertising is wasted; the trouble is I don't know which half." -John Wanamaker

This applies even more strongly to model choosing. I know for a fact that majority of my work doesn't require a very strong model, but separating the trivial and non-trivial tasks is a famously hard problem (if at all decidable).

I don't see why it should be all that difficult. All you have to do is first find a library that implements a decent solution to the halting problem and you're off to the races.

Luna is comparable to GPT 5.4 from 4 months ago on many benchmarks. I know many who have said during that time, myself included, that if that's the model they had to use for the rest of their lives, they'd be fine.

GPT 5.4 is/was a very capable model.

Cosmically apt username given the substance of this comment.

Highlighting https://news.ycombinator.com/item?id=49113236 in response.

HN could be run as a BBS on 70's hardware. Instead of using a CPU with ~10 thousand transistors, you're likely using one with ~10 billion to do basically the same thing, and you don't think twice about it.

Exactly. I haven't reached the "let 1000 agents bloom" mode yet, so currently I'm spending real headspace managing agents doing work, and that work is all important, so why "settle" for sub-frontier models for that work? Maybe I'll get there for non-coding work.

If you have agents and users, you can run evals and see how far the models go. Luna is not greatest in tool calls, but if you define your problem well and the tools well, it is comparable to Gemini 4 Flash with much lower price tag.

> separating the trivial and non-trivial tasks is a famously hard problem (if at all decidable).

Famously, this is also a problem for human coders in sprint planning.

I get frustrated with a poor quality model leaving my codebase littered with wrong comments, which then later trip up smarter models.

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You just need a very strong frontier model to do triage of your tasks.

/s