41–50 of 126 posts
Awesome. I've just designed and built my own z80 computer, though right now it has 32kb ROM and 32kb RAM. This will definitely change on the next revision so I'll be sure to try it out.
RAM is very expensive right now.
I love it, instant Github star. I wrote an MLP in Fortran IV for a punched card machine from the sixties ( https://github.com/dbrll/Xortran ), so this really speaks to me. The interaction is surprisingly good despite the lack of attention mechanism and the limitation of the "context" to trigrams from the last sentence. This could have worked on 60s-era hardware and would have completely changed the world (and science…
Tin foil hat on: i think that a huge part of the major buyout of ram from AI companies is to keep people from realising that we are essentially at the home computer revolution stage of llms. I have a 1tb ram machine which with custom agents outperforms all the proprietary models. It's private, secure and won't let me be motetized.
We should show this every time a Slack/Teams/Jira engineer tries to explain to us why a text chat needs 1.5GB of ram to start up.
You can buy a kid’s tiger electronics style toy that plays 20 questions.
It’s not like this LLM is bastion of glorious efficiency, it’s just stripped down to fit on the hardware.
Slack/Teams handles company-wide video calls and can render anything a web browser can, and they run an entire App Store of apps, all from a cross-platform application.
Including Jira in the conversation doesn’t even make logical sense. It’s not a desktop application that consumes memory. Jira has such a wide scope that the word “Jira” doesn’t even describe a single product.
Imagine, this working on a Gameboy, in those days. Would've sounded like magic
Earlier quoted context omitted.
Connections: Alternative History of Technology by James Burke documents these "coincidences".
Those "coincidences" in Connections are really no coincidence at all, but path dependence. Breakthrough advance A is impossible or useless without prerequisites B and C and economic conditions D, but once B and C and D are in place, A becomes obvious next step.
Although from what I remember from the TV show, most of what he investigates/talks about is indeed path dependence in one way or another, although not everything was like that.
We should show this every time a Slack/Teams/Jira engineer tries to explain to us why a text chat needs 1.5GB of ram to start up.
> It won't write your emails, but it can be trained to play a stripped down version of 20 Questions, and is sometimes able to maintain the illusion of having simple but terse conversations with a distinct personality. You can buy a kid’s tiger electronics style toy that plays 20 questions. It’s not like this LLM is bastion of glorious efficiency, it’s just stripped down to fit on the hardware. Slack/Teams handles com…
That's a bug not a feature, and strongly coupled to the root cause for slack's bloat.
I love it, instant Github star. I wrote an MLP in Fortran IV for a punched card machine from the sixties ( https://github.com/dbrll/Xortran ), so this really speaks to me. The interaction is surprisingly good despite the lack of attention mechanism and the limitation of the "context" to trigrams from the last sentence. This could have worked on 60s-era hardware and would have completely changed the world (and science…
Stuff like this is fascinating. Truly the road not taken. Tin foil hat on: i think that a huge part of the major buyout of ram from AI companies is to keep people from realising that we are essentially at the home computer revolution stage of llms. I have a 1tb ram machine which with custom agents outperforms all the proprietary models. It's private, secure and won't let me be motetized.
Earlier quoted context omitted.
We're talking kilobytes, not gigabytes. And it isn't DDR5 either.
Yeah, even an average household can afford 40k of slow DRAM if they cut down on luxuries like food and housing.