I can only offer you garbage specs along with really shitty build quality for 1kEUR. It’s certified as ”not a direct fire hazard”. Take it or leave it. (/s)
I have high hopes for framework, but last time I was in the market they still had some things to resolve.
The M-series hardware is locked down and absofuckinglutely proprietary and locked down and most likely horrible to repair.
But holy shit, every other laptop I’ve ever used looks and feels like a cheap toy in comparison. Buggy firmware that can barely sleep, with shitty drivers from the cheapest components they could find. Battery life in low single digits. The old ThinkPads are kinda up there in perceived ”build quality”, but I haven’t seen any other laptop that’s even close to a modern macbook. Please HP, Dell, Lenovo, Framework or whoever , just give me a functional high quality laptop. I’ll pay.
Fairly significant factor when building really large systems. If we do the math, there ends up being some relationships between
Basically, for a given risk acceptance and total system size there is usually a sweet spot for disk sizes.
Say you want 16TB of usable space, and you want to be able to lose 2 drives from your array (fairly common requirement in small systems), then these are some options:
The more drives you have, the better recovery speed you get and the less usable space you lose to replication. You also get more usable performance with more drives. Additionally, smaller drives are usually cheaper per TB (down to a limit).
This means that 140TB drives become interesting if you are building large storage systems (probably at least a few PB), with low performance requirements (archives), but there we already have tape robots dominating.
The other interesting use case is huge systems, large number of petabytes, up into exabytes. More modern schemes for redundancy and caching mitigate some of the issues described above, but they are usually onlu relevant when building really large systems.
tl;dr: arrays of 6-8 drives at 4-12TB is probably the sweet spot for most data hoarders.