I find it quite funny all these folks who are addicted to chasing frontier models, only just noticing that small models became "good enough" for most tasks. Those of us without fable-sized expense accounts noticed this quite a while back

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SomeonesAccount 6 hours ago | parent | next [-]

Exactly! Composer 2/2.5 were amazing, cheap, and fast. Everyone else was Gaga about GPT 5.5 and such, while we were over here doing the work with less cost and more speed

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jbjbjbjb 6 hours ago | parent | prev | next [-]

I’ve been playing around with Luna, Terra and Sol and for the type of work I’ve been doing lately I actually think Sol is just a likely to trip up as Luna. Examples were Sol over assuming, persisting in the wrong direction, over engineering a little script to do some exploration of api. They can all be fixed but it’s a waste of tokens, I rather have Luna do it because course correction on small pieces of work is cheaper.

	▲	scoring1774 5 hours ago \| parent \| next [-]
		I've found the distinction to be in how much I care about how the final product looks. If I want high-quality code I typically find a smaller model with a well-designed spec to do better, if I want it to just run and produce something close to my vague description typically Sol does better. For most actual business use-cases I think the first is likely better but the experimentation speed up with the frontier is very nice.
	▲	ZeWaka 2 hours ago \| parent \| prev [-]
		I've found Luna to be quite capable.

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kccqzy 5 hours ago | parent | prev | next [-]

> for most tasks

The word “most” is doing a lot of work here. On a percentage basis perhaps most tasks a typical SWE needs to do when they aren’t in meetings or writing docs are just glorified autocomplete. But that’s boring and that’s why people don’t usually talk about it.

People are addicted to chasing frontier models because they all have memories of spending a week on a deeply challenging algorithm problem or even have crazy complicated algorithms they cannot implement themselves and want to have the models achieve this technical breakthrough. It’s the kind of productivity boost from spending one week on a problem to spending one hour. In contrast the productivity boost from spending ten minutes to spending one minute just doesn’t occupy people’s mind.

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swiftcoder 3 hours ago | parent [-]

> crazy complicated algorithms they cannot implement themselves

I'm not sure I know very many engineers who would fall in this bucket. Or do you mean the business types who suddenly think AI can replace all the engineers?

	▲	kccqzy 3 hours ago \| parent [-]
		It probably depends on the background and the company. For example if one works at a startup that happens to use technology, it’s unlikely to happen because SWEs just translate business rules to code. But if one works at the place where the technology itself is the focus, then yes most people will fall in that bucket. In fact I noticed that this is the one place where people discussing AI on HN tend to talk past each other. On the one hand people are talking about supreme intelligence like designing new algorithms (on the same vein as finding counter examples for the Jacobian conjecture) and on the other hand people are just satisfied using AI to automate a few quotidian tasks that hitherto couldn’t be automated.

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jlkuester7 6 hours ago | parent | prev | next [-]

Exactly. Even 32b parameter models you can run locally on consumer hardware are "good enough" at this point for some workflows!

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dominotw 3 hours ago | parent | prev [-]

no they are not good enough for "most" tasks