> Each experiment is a branch (or work-tree) so yes there are a lot of commits happening, but the results are measurably real.

If you are correct , and GitHub is scaling its compute mostly as a reaction to this externality (agents churning through code that will mostly be discarded), then you can look forward to getting billed for your usage. After all, it is hard to build a scalable system without back-pressure.

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crystal_revenge 4 hours ago | parent [-]

I've already started moving my personal projects off github and onto forgejo running on my homelab. I know a lot of people doing the same. With a hermes-agent for a sysadmin I can debug problems from my phone, so I wouldn't be surprised if I have more "9s" that GH.

But if it ends up costing extra for GH, especially for work usage, then it's just a simple calculation of "is this worth it?" which I suspect for most cases will be 'yes'.

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overfeed 3 hours ago | parent [-]

> [...]it's just a simple calculation of "is this worth it?" which I suspect for most cases will be 'yes'

Once the landgrab-stage flat-pricing goes away, it will become a case-by-case calculation because unsupervised agents can (and will) run up your billing with zero understanding of the business value of what they're instructed to solve.

	▲	crystal_revenge an hour ago \| parent [-]
		> with zero understanding of the business value What kind of products/services are you building where you aren't able to tie your eval suite to business value? If you can't, then why are you building whatever is it you are in the first place? By far one of the biggest changes I think we'll see in things being built by agents is reducing the gap between code and value. The first stage is to start making it possible to measure quality (evals) and the second stage is to more closely align measurable equality with value. The business value of the tokens spent on my team was discussed my first day. > Once the landgrab-stage flat-pricing goes away Aside from the above point, I'm already running local LLMs on my homelab that, while not quite what I want for truly production work, have been able to iterate on and solve real, non-trivial research tasks for effectively zero cost (energy cost was roughly on par with running an old light bulb). The way open, local models have been developing there will be many cases where if proprietary providers over-charge it won't be a deal breaker to just switch to local models. Not to mention that there are plenty of open, but non-local models that are already 5x cheaper and roughly on par with the mainstream model providers.