mechagodzilla

There was an incredible amount of investment into high-speed programmable power looms, and there were a number of innovations (later rediscovered by computer architects) targeted at 'card-saving techniques' to reduce the labor required to card-punch the designs, and to reduce the down-time of the looms. 'Double Data Rate' card readers (with a design interleaved on two chains running at half frequency), SIMD-style one-reader/many-loom setups, 'cross-border' looms that switched between two separate card loops just like a 'branch' instruction, machines to copy chains of punch cards.

What you're saying might have been true in the 18th century or earlier, but things were highly mechanized by the 19th century, and there was a lot of motivation to keep the big expensive looms highly utilized and amortize investment in punch card sets across lots of production runs.

I think the way computers developed is kind of an interesting example of this in the real world. Babbage basically realized the idea of a modern computer, but tried to build a too-complicated 'supercomputer' (40 decimal digit precision!) because he didn't know how incredibly useful a much simpler, more limited computer could be. We had to wait another 75 years and spend an incredible amount of money to develop relay and vacuum-tube supercomputers, before realizing that the idea could be scaled down, and something like a PDP-8 / PLC / microcontroller was incredibly useful for industrial control and, with sufficient cleverness, even do a lot of scientific computing.

I think if you went back in time you could pay some power loom engineers to build you a pretty decent 4-8 bit microcontroller operating at 1-5 Hz (comparable to power looms at the time) and computer-controlled textiles would have paid for the computer revolution 75-100 years ahead of time.

Yes. I can split a model like this across 3 GPUs (a 1080 with 8GB and two Titan Vs with 12GB), and it's much faster than running it on 36 CPU cores. As long as it fits in aggregate VRAM, it seems very advantageous to do so.

Relatively accurate (I think they're pretty close like 70%+ of the time). Significantly better than just projecting that the next five years will be like the last 5 years.

You haven't really been getting 'human written and thoughtful content' for a vast swath of search topics for probably 15-20 years now. You get SEO-hyper-optimized (probably LLM-generated for anything in the last 3 years) blog spam. In terms of searching for information and getting that information, there are a lot of topics where an LLM-generated result is vastly better just by virtue of not being buried inside blog spam. The slop ship sailed years ago.

It's not really an apples-to-apples comparison - I enjoy playing around with LLMs, running different models, etc, and I place a relatively high premium on privacy. The computer itself was $2k about two years ago (and my employer reimbursed me for it), and 99% of my usage is for research questions which have relatively high output per input token. Using one for a coding assistant seems like it can run through a very high number of tokens with relatively few of them actually being used for anything. If I wanted a real-time coding assistant, I would probably be using something that fit in the 24GB of VRAM and would have very different cost/performance tradeoffs.

I've been running the 'frontier' open-weight LLMs (mainly deepseek r1/v3) at home, and I find that they're best for asynchronous interactions. Give it a prompt and come back in 30-45 minutes to read the response. I've been running on a dual-socket 36-core Xeon with 768GB of RAM and it typically gets 1-2 tokens/sec. Great for research questions or coding prompts, not great for text auto-complete while programming.

1-2 tokens/sec is perfectly fine for 'asynchronous' queries, and the open-weight models are pretty close to frontier-quality (maybe a few months behind?). I frequently use it for a variety of research topics, doing feasibility studies for wacky ideas, some prototypy coding tasks. I usually give it a prompt and come back half an hour later to see the results (although the thinking traces are sufficiently entertaining that sometimes it's fun to just read as it comes out). Being able to see the full thinking traces (and pause and alter/correct them if needed) is one of my favorite aspects of being able to run these models locally. The thinking traces are frequently just as or more useful than the final outputs.

You can keep scaling down! I spent $2k on an old dual-socket xeon workstation with 768GB of RAM - I can run Deepseek-R1 at ~1-2 tokens/sec.

Kodak didn't really have the option to compete. Their business was largely film, which just disappeared completely, and even digital cameras got replaced pretty quickly with phones. There was nothing to pivot too for Kodak.

Did you have to do anything special to get the SSD to play nice with OS9? I tried adding one to a 300MHz G3 iMac and it took forever to initialize on boot and would randomly stall a lot.

If anyone can pay-as-you-go use a fully automated factory, and the factories are interchangeable, it seems like the value of capital is nearly zero in your envisioned future. Anyone with an idea for soup can start producing it with world class efficiency, prices for consumers should be low and variety should be sky-high.

Buy a used workstation with 512GB of DDR4 RAM. It will probably cost like $1-1.5k, and be able to run a Q4 version of the full deepseek 671B models. I have a similar setup with dual-socket 18 core Xeons (and 768GB of RAM, so it cost about $2k), and can get about 1.5 tokens/sec on those models. Being able to see the full thinking trace on the R1 models is awesome compared to the OpenAI models.