

Hallucinated frames like DLSS3. Completely unnecessary, just like the hallucinated pixels of DLSS2/FSR2. Dialling a couple of settings down to medium looks much better.
Hallucinated frames like DLSS3. Completely unnecessary, just like the hallucinated pixels of DLSS2/FSR2. Dialling a couple of settings down to medium looks much better.
It doesn’t care about copyright or authorship, which becomes a huge problem due to content no longer having a real home in IPFS, everybody can pin, cache or share content on IPFS.
Sounds like a feature, not a shortcoming
Ubuntu LTS has the exact same problem. And unlike Ubuntu, with Debian you have the choice to use sid which is as up to date as Arch usually
Works really well with Flatpak Steam
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GrapheneOS has strict sandboxing for all apps. App A cannot talk to App B unless given explicit user permission. Google Play services is not installed by default, and if you do install it, it’s subject to the same sandbox. This basically addresses rid of all userspace tracking concerns, unless you actively choose to weaken those defaults.
GrapheneOS has strict sandboxing for all apps. App A cannot talk to App B unless given explicit user permission. Google Play services is not installed by default, and if you do install it, it’s subject to the same sandbox. This basically addresses rid of all userspace tracking concerns, unless you actively choose to weaken those defaults.
(There are still concerns associated with the closed source firmware of the baseband modem)
Unfortunately, I think no. Nvidia sells every AI chip wafer they can get from TSMC, so if gamers won’t pay the same margins as datacenter customers Nvidia will simply stop selling to them. As such, Nvidia does not need consumers/home users anymore.
As for AMD, they just decided their pricing strategy is “whatever Nvidia does, but 10% cheaper”
Both team green and team red thought they could charge pandemic era scalper prices this generation, which of course wont happen because ETH mining is dead
AI models are universal approximators f such that y=f(x,w) with optimizable weights w that minimize some metric L(y). You can come up with a hand tuned approximator yourself that matches/beats an AI model. Does not change the fact that any approximator attempts to guess (i.e. “hallucinate”) the output y based on the prior x.