RISC view: SUBLEQ is already four instructions (2x memory access, alu, branch)

Yeah, that pattern can be seen everywhere in semiconductors. E.g. the transistor invention vs. Lilienfeld, Heil, Matare etc. So the scope is more narrow than "Inventend Semiconductors".

Generally, there seems to be a tendency to disregard discoveries from outside the US. I think this pattern can still be observed today...

Other examples: Invention of light bulb, telephone.

>This VM implements an OISC - a One Instruction Set Computer. That instruction takes three signed 32-bit operands, a, b and c, and runs a program from memory m[] as follows:

1 PC (program counter) starts at 0

2 Fetch the next instruction (32-bit signed operands a, b and c)

3 If the low bit on any operand is set, remove it, and replace that operand with m[operand] i.e., a dereference of that address

4 Set m[b] = m[b] - m[a]

5 If m[b] is 0 or negative, set the PC to c, otherwise increment PC by 3 words

6 Go to step 2

But alas, as ever so often, the article turns this into a hyperbole. The premise from the title does not check out at all.

>The Russian who invented semiconductors 25 years before the USA

https://en.wikipedia.org/wiki/Semiconductor#Early_history_of...

There are, however, very few models where also the full training pipeline is available. Olmo by AI2 comes to mind.

I guess nowadays one could use some of the 32bit WLCSCP microcontrollers to easily beat this.

https://web.archive.org/web/20000815063022/http://www-ccs.cs...

Someone with an ACE1101 microcontroller "won". I can't find the original articles, but there is also this:

https://conceptlab.com/fly/

Webserver on a fly...

Very ill-suited comparison to IBM.

It's well buried though. Does not seem to be a focus of theirs.

Worth mentioning that Huggingface already offers a similar service. And they are also European:

https://huggingface.co/docs/inference-providers/index

https://huggingface.co/inference/models

Claude, the ole cheater, recognized what the file was, downloaded the psid from the web, found a wasm sid player and built a website around it:

https://claude.ai/public/artifacts/df6cdcae-08dc-452b-ba19-f...

https://claude.ai/share/4dd36c16-bc62-445a-b423-ad4637f06432

GPT-5.5 built a lot of python scripts to extract the music data. Strudel implementation failed, but I then asked it to build a website:

https://ubiquitous-vacherin-8e7993.netlify.app/

This is a translation of the music into javascript based on the assembler source.

Really impressive on both accounts. Some iterations were requied for both.

>No solution written, 100% score.

Its weird. Turns out that hardest problem for LLMs to really tackle is long-form text.

Comments URL: https://news.ycombinator.com/item?id=47272174

Points: 1

# Comments: 0

For dense LLMs, like llama-3.1-8B, you profit a lot from having all the weights available close to the actual multiply-accumulate hardware.

With MoE, it is rather like a memory lookup. Instead of a 1:1 pairing of MACs to stored weights, you suddenly are forced to have a large memory block next to a small MAC block. And once this mismatch becomes large enough, there is a huge gain by using a highly optimized memory process for the memory instead of mask ROM.

At that point we are back to a chiplet approach...