Same as big data centers siphoned up all the SSDs before their prices became reasonable for consumers, same with HDDs, or RAM every generation. They’ll be high before they’ll be reasonable, and then they’ll be cheap once their next generation comes along.
Yeah, started in the late 70s (1978) with the StorageTek STC 4305, which was 45MB in a large cabinet, cost $400,000, and was from what I can find the first SSD. From there up until the 2000s they served as expensive but crucial networking infrastructure, when in the early-mid 2000s the industry moved from RAM to NAND, and the price-per-GB of NAND became reasonable enough to enter the consumer space as a proper form of storage drive, and not just for USB sticks or memory cards.
For comparison, here is the rough trajectory of price:
1978: $9.1 million/GB
1985: $920,000/GB
1990: $50,000/GB
1995: $1,000/GB [0]
2005: $100/GB
2006 is when the first commercial SSDs hit market in laptops, and by 2010 they were ~$3/GB, which brought them to wide adoption.
[0]: 2000 was a stagnant year, prices stayed the same from my research between ‘95 and the new millennium before dropping in 2005.
Well the effects of radiation were still poorly understood, but also there wasn't that much radioactive material left over in any concentration. And the gamma ray blast would appear superficially like normal burns from being near a firebomb until atleast a few days later.
Contrary to Fallout lore, most nuclear bombs dont leave much concentrated radioactive material unless it was done on purpose. You either need a shittier and smaller explosion with the same material, or you need a ground burst which reduces the effective blast area.
The US was on ground zero in under 2 weeks in Japan and couldn't detect anything over the background in radioactive isotopes.
>Now you don't need to understand the details of connecting to Stripe or Auth0 or setting up a Kubernetes cluster.
Ehhh, if you're smart you want to at the very least gloss over the details, and look at the code being generated by whatever model you're using for this solution.
As far as I know, there isn’t an interface like nvlink that allows these macs to work in tandem; they would just send their data over ethernet, maybe thunderbolt/usb-c?
Nice, not sure why I was downvoted for suggesting the cheapest GPU which comes with quite a lot of VRAM... but I guess some people are fatigued by AI / GPU talk?
From what I gather, the Chinese are behind, but a lot of their research amounts to scrappy, clever discoveries in how to use more novel technologies (for Qwen and Deepseek, its mixture of expert models, that can do inference using a portion of the model at a time). The chinese also distill information from American models, so there’s that.
The American companies, from my impression don’t involve themselves with such lowly “hacks” because they have so much money to just push forward with doing everything on big heavy models that run on the most cutting edge nvidia chips that they can, the moment, kinda sorta get on demand (I say that in some degree of jest).
this is not an effective long term strategy in a collaborative environment that is advancing for the same reason that having a private secret fork of the linux kernel with a few proprietary improvements is not an effective strategy.
integrating your own work with the latest public advances takes resources. For one or two small changes this is manageable, but the further you diverge from the public, the cost of maintenance rises exponentially if you want to continue to integrate public advances. when you publish your meaningful advance, you offload the maintenance burden onto everyone else (and they only have to pay a linear cost rather than an exponential one) as it's integrated by default in new work.
In most cases, the (exponential) maintenance cost of integrating public advances with secret ones exceeds the value of the public advances, so most that undertake this strategy of advancing the open frontier in secret don't attempt to integrate continually, but instead try to make a breakaway sprint in isolation to grab a few sticky customers before the unstoppable wave of the public frontier catches up.
This is a pattern commonly seen in university research departments when researchers switch into product development mode, most of these projects are a sprint to advance away from the public frontier once a good idea is found and they do good work and find a few customers for a little while. But if you check back in a few years you won't find an advanced research department but a zombie IP company that brings in a steady income via IP enforcement and a small number of customers for whom switching is too expensive.
