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time to RE play services

People are way ahead of you.

https://github.com/microg/GmsCore


hell ya, I love the internet

Time based data is extremely important, and there are tons of techniques out there to train super fast super small models, but they are a pain in the ass to tune. I could see a foundation model that is kind of good at a lot of things, that could be fine tuned or used when you want something more straight forward to use to analyze a bunch of data.

Honestly this might be very good for feature selection to refine a much more specialized model.


ya especially because it's huge, you aren't running this on your desktop

but you could change networks, you could run your own.


Protocol was documented but each server is a silo.


that's a wild theory. You think there are really warehouses of GPUs that they are just sitting on until data centers are built, that will just all of a sudden flood the market?

First, the form factor of GPUs in data centers aren't the same as desktop GPUs, so you couldn't use them even if you wanted to in a normal rig.

Second, from a business perspective that doesn't make a lot of sense. It's much more likely that GPUs are going to the highest bidder/large contracts who are scooping them up to populate/upgrade data centers that are in operation because they are going to get more money per gpu on newer hardware. The "old" hardware might go to a warehouse to be auctioned to the highest bidder or go to a data center coming online, but I highly doubt they are sitting on market wrecking amounts of GPUs just waiting to flood the market. When they go bankrupt and they have to sell a datacenter or two, those datacenters being parted out as part of a bankruptcy deal I could see. Warehouses full of unused GPUs doesnt make any sense to me though.


"Microsoft Is Warehousing An Estimated $50 Billion to $100 Billion In GPUs" https://www.wheresyoured.at/wherere-all-the-ai-chips/


> First, the form factor of GPUs in data centers aren't the same as desktop GPUs, so you couldn't use them even if you wanted to in a normal rig.

With eGPU PCIe 3.0 or 4.0 x4 links (or even Thunderbolt), that essentially doesn't matter for inference.

You pay the bandwidth hit on model load / unload (a few seconds), but it's irrelevant for post-loaded inference.

Anyone hosting a high wattage GPU is going to be fine hosting in an external enclosure, most of which have generous extra-spec room.

And if they don't, if a flood of cheaper DC GPUs hit the used market, you can bet Chinese manufacturers will have enclosures that fit them available the day after.


It makes sense if you consider that maybe all of these companies are colluding to forward a shared agenda. It is happening whether it makes sense or not. So there's that too.


API error, endpoint overloaded


Way too many acronyms, what is this, the government?


lol, essentially yes. sorry about that. there's a lot of regulations around software that's used for aerospace and defense, and that's where a lot of the new builds are happening these days. but here's a little decoder ring:

ERP: the core system that manages/plans everything from accounting to inventory MES: the shop floor QMS: the quality system that keeps track of what's went wrong and how we're fixing it CMMC: government regulation NIST 800-171: spec that drives the regulation


flash next is good, I've been running it for like 2 weeks now and it's pretty solid, hope you like it and it meets your needs. I still lean on Claude and codex a fair bit for harder stuff, but I'm rapidly moving towards 2x $20 plans instead of 2x $200 plans


A fight worth fighting


You can get used to it and it still be rage inducing.


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