95 karma · joined April 1, 2022
Or place new backdoors!
But I still don't see the argument for using closed-weights models. Why should I trust a closed model where I can see none of its weights, activations, or reasoning more than a model where I CAN see its weights, activations, and reasoning?
I agree, at least for inference credits. But anecdotal counterpoint: about ten years ago I put together a system using about $1600 worth of new and used components. Including electricity that TCO has been something like $14/month. I'd be hard pressed to find 128GB of RAM, 12 cores of x86_84, and 40 TiB (raw) worth of disk, and unlimited ingress/egress traffic for rent at that rate. That's what, $0.02 an hour, rounding up?
Yes, a hyperscaler's rent-a-computer would get faster and be more energy efficient. Possibly more carbon efficient over it's lifetime, although I have no way to quantify that. But in raw dollars it would have costed me absurdly more to rent. On the other hand, my machine has sat cold and unused for multiple months when I didn't need it to be running, and I don't consume all of it's resources around the clock. So maybe I could have gotten away with a lesser amount of local compute in a Raspberry Pi or similar, and an equally small cloud box for $5 a month or something. I dunno. I don't even own a laptop or desktop, and try to use 'cloud' services whenever practical. (Edit: always giving priority to privacy in those circumstances; only data I don't care about goes up.)
There are tradeoffs. I don't use my local server for LLM inference, it's far to inefficient for that; so I'll happily pay OpenRouter per token as needed. So I do think that hosted compute for rent does have a purpose. I guess all that's to say that I don't see it in black and white; there are times to invest in local hw and times to ride on the optimized bleeding edge of what other companies are willing to host in big datacenters.
This turned into a bit more of a ramble than I intended.
Each provided an identity in the first turn, something that they won't do as readily if asked in plain English, and in each case the answer matched the model ID as disclosed by arena.ai after voting -- except in cases where the model ID was a masked/hidden one and then I just had to take it on faith that the model was what it said. (I didn't have much to vote on, but I ended up voting for the answers I felt provided the style, content, and length I was expecting.)
I'm still looking through your docs, but I'm curious how e.g., you'd prevent my job you run on your idle agent from contaminating your agent's memory or history, or in the opposite direction how you'd guarantee that your idle agent's memory or skills or system prompts or whatever aren't corrupting or impacting my job?
(Edit: future is only spelled with one f)
Keep your hands on the wheel, y'all. :-(
Homepage is cool too.
Oh, for the simpler days when not everything required an app.
-- https://about.fb.com/news/2026/09/introducing-muse-personal-...
I'm sure there are other examples of repos that move this quickly out there, but I guess this is the first one that I've become aware of. On one hand it feels good that opensource software gets this much attention; on the other hand it seems like it must (at times) feel like a Sisyphean task to keep up both on the dev side and to keep the client-side software up-to-date.
I know it's more complicated than that, with economics and privacy factors involved. But if I operate on the assumption that real privacy is real hard I'd rather just be guarded and careful with my prompts and whatever output I give to the LLM and expect that it will be training on that, rather than spill all my darkest secrets to some other LLM provider that pinkie-promises privacy only to leak it publicly later, anyway.