71–80 of 136 posts
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I won't rest until the average microcontroller in an optical mouse is more powerful than a Cray 1.
I'm not aware of any optical mouse using a general purpose MCU, to my knowledge they are all using ASICs
The Cray-1 is really a very simple machine, with a small instruction set. It just has 64 of everything. It was built from discrete components, almost the last CPU built that way.
[1] https://www.cpushack.com/2010/09/15/homebrew-cray-1a-1976-vs...
Comparing against a raspberry pi 5 is kind of overkill. While a Pico 2 is close to computationally equivalent to a cray 1 now (version 2 added hardware floating point), the cray still has substantially more memory - almost 9MB vs 520k. For parity, you have to move up to a raspberry pi zero 2, which costs $15 and uses about 2W of powerm A million times cheaper than a cray in 2025 dollars and quite a bit more capable.
https://www.olimex.com/Products/RaspberryPi/PICO/PICO2-XXL/o...
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Someday real soon, kids being shown episodes of 'Knight Rider' by their grandparents won't understand why a talking car was so futuristic.
Tried explaining what a Tamagotchi was to someone recently. Looks of utter bewilderment
Imagine traveling back to 1977 and explaining to someone that in 2025 we've allocated all that extra computing power to processing javascript bundles and other assorted webshit.
> the Cray had about 160MFLOPS of raw processing power; the Pi has... up to 30GFLOPS. Yes... that's giga FLOPS. This makes it almost 200 times faster than the Cray. Imagine traveling back to 1977 and explaining to someone that in 2025 we've allocated all that extra computing power to processing javascript bundles and other assorted webshit.
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No need for an RPi 5. Back in 1982, a dual or quad-CPU X-MP could have run a small LLM, say, with 200–300K weights, without trouble. The Crays were, ironically, very well suited for neural networks, we just didn’t know it yet. Such an LLM could have handled grammar and code autocompletion, basic linting, or documentation queries and summarization. By the late 80s, a Y-MP might even have been enough to support a small…
> a small LLM, say, with 200–300K weights A "small Large Language Model", you say? So a "Language Model"? ;-) > Such an LLM could have handled grammar and code autocompletion, basic linting, or documentation queries and summarization. No, not even close. You're off by 3 orders of magnitude if you want even the most basic text understanding, 4 OOM if you want anything slightly more complex (like code autocompletion),…
https://www.tomshardware.com/tech-industry/artificial-intell...
John Carmack was also hinting at this: we might have had AI decades earlier, obviously not large GPT-4 models but useful language reasoning at a small scale was possible. The hardware wasn't that far off. The software and incentives were.
My former boss (Steve Parker, RIP) shared a story of Turner Whitted making predictions about how much compute would be needed to achieve real-time ray tracing, some time around when his seminal paper was published (~1980). As the story goes, Turner went through some calculations and came to the conclusion that it’d take 1 Cray per pixel. Because of the space each Cray takes, they’d be too far apart and he thought the…