Oh dear, it seems we've... somehow been psychically linked...

I developed a browser-based CP/M emulator & IDE: https://lockboot.github.io/desktop/

I was going to post that instead, but wanted a 'cool demo' instead, and fell down the rabbit hole.

Connections: Alternative History of Technology by James Burke documents these "coincidences".

Super intrigued but annoyingly I can’t view imgur here

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.

Too late, they stopped being available last year.

This is extremely similar to Karpathy's idea of a "cognitive core" [1]; an extremely small model with near-0 encyclopedic knowledge and basic reasoning and tool-use capabilities.

[1] https://x.com/karpathy/status/1938626382248149433

You might be interested in RWKV: https://www.rwkv.com/

Not exactly "minimal viable", but a "what if RNNs where good for LLMs" case study.

-> insanely fast on CPUs

I think what's amazing to speculate is how we could have had some very basic LLMs in at least the 90s if we'd invented the tech previously. I wonder what the world would be like now if we had?

For your first question, the LLM someone built in Minecraft can handle simple conversations with 5 million weights, mostly 8 bits.

I doubt it would be able to make good use of a large context window, though.

> 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 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.

I don’t know, but your question reminds me of this paper which seems to address it on a lower level: https://arxiv.org/abs/2204.06974

“Planting Undetectable Backdoors in Machine Learning Models”

“ … On the surface, such a backdoored classifier behaves normally, but in reality, the learner maintains a mechanism for changing the classification of any input, with only a slight perturbation. Importantly, without the appropriate "backdoor key", the mechanism is hidden and cannot be detected by any computationally-bounded observer. We demonstrate two frameworks for planting undetectable backdoors, with incomparable guarantees. …”

> this secret be easily reverse engineered / inferred by having access to models weights

It could with a network this small. More generally this falls under "interpretability."

RAM is very expensive right now.

The original ELIZA ran on an IBM 7094 mainframe, in the 1960s. That machine had 32K x 36-bit words, and no support for byte operations. It did support 6-bit BCD characters, packed 6 per word, but those were for string operations, and didn't support arithmetic or logical operations.

This means that a directly translated 40 KB Z80 executable might be a tight squeeze on that mainframe, because 40K > 32K, counting words, not bytes. Of course if most of that size is just 2-bit weight data then it might not be so bad.

ELIZA running on later hardware would have been a different story, with the Z80 - released in 1976 - being an example.

MAYBE

100% Please do this! I wish the same

https://3e.org/private/z80ulmweb/

It's just one-shot AI slop - literally, the prompt was 'make a web based version of [github url of this project]' and it spat this out. It appears to work fine.

I'll keep it up for a couple of months and then it'll be auto-deleted, no sense in keeping it around longer than that.

This was my first thought too haha. That would be mind blowing

Though it'll still be kinda slow on a Model I, I've written an about 9 times faster Z-80 code for the network evaluation. I imagine the pull request will end up in the main depot but for now you can find it in https://github.com/gp48k/z80ai

I think I can do a little bit better; maybe 10% faster.

I thought about this, chatbots existed well before LLMs (Eliza: 1966!) and the only time I have seen a commercially successful game with a (very simple) chatbot was Quake III Arena!

Quake 3 is probably the last game where you would expect a chatbot, as there are few games where storytelling matters less and it is a very little known feature, but Quake 3 bots can react to what you say in the chat, in addition to the usual taunts.

But that's the thing, Quake 3 can do it because it is inconsequential, in a story-driven game like a RPG, NPCs have a well defined spot in the story and gameplay, they tell you exactly what you need to know, as to not disrupt the flow of the story. Tell you too much, and they will spoil the big reveal, tell you too little, and you don't know what to do, tell you irrelevant details and you get lost chasing them. It has to be concise and to the point, so that those who don't really care know what to do to advance the story, but with enough flavor to make the world alive. It is really hard to find the right balance, and if in addition, you have to incorporate a chatbot, it borders on impossible.

It looks like a good idea on the surface, but it most likely isn't, unless it is clearly not part of the main gameplay loop, as in Quake 3.

Some people had some success using a (big) LLM as a DM in D&D, which I think is easier since it can make up the story as it advances, it is much harder to make up game elements in a computer RPG that are not programmed in.

i'll echo that practicality only surfaces once it is apparent what can be done. yea this feels like running "DOOM on pregnancy test devices" type of moment

SLM

Can't possibly be further than just around the corner.

I'm thinking early April?

Each layer of the LM is also at most 16 KiB, so if you want to minimize bank switching, I think making sure each layer is in one bank would be enough? Bank switching shouldn't give much overhead anyway unless it complicates an inner loop, which would be avoided if no layers are split across banks.

You have 32KB of ROM, plus 8 Kb of ram on original game boy. Game boy color has more. Bank switching is super fast, as well. Given that models are likely streamed, I doubt the bank switching is a problem.

Biggest pain point is likely the text input.

(Not OP)

It depends on the model, but from my experiments (quantizing one layer of a model to 2-bit and then training the model with that layer in 2-bit to fix the damage) the first layer is the most sensitive, and yes, the last layer is also sensitive too. The middle layers take the best to quantization.

Different components of a layer also have a different sensitivity; e.g. the MLP downscale block damages the model the most when quantized, while quantizing the Q projection in self attention damages the model the least.

Generate an LLM that is designed to solve one extremely specific problem: answering the ultimate question of life, the universe, and everything.

Even with modern supercomputing the computation would be outpaced by the heat death of the universe, so token output must be limited to a single integer.

I don’t think this could beat an ELIZA-style bot in how magical it feels, given the extreme terseness of its replies.

I love these thought experiments. Looking at the code size, it would have been possible for someone to come up with this back in the days, similar to the idea of a million monkeys on a typewriter eventually producing Shakespeare.

And would have lasted 3 minutes.

Speaking of - I remember my first digital camera (Fujitsu 1Mb resolution using SmartMedia)… it used so much power that you could take 20-30 photos and then needed to replace all 4 batteries lol

Flip phones had predictive texts since forever. LLMs are just* supercharged predi[ctive text algorithms are computer algorithms that are]

"Look, my Game Boy passes the Turing Test!"

*burns you at the stake*