Ugh, not another one...

Great vision, challenging the "scale" of current AI solutions is super valid, if only for the reason that humans don't learn like this.

Architecture: despite other comments, I am not so bothered with MMAP (if read only) but rather with the performance claims. If your total db is 13kb you should be answering queries at amazing speeds, because you're just running code on in-cache data at that point. The performance claim at this point means nothing, because what you're doing is not performance intensive.

Claims: A frontal attack on the current paradigm would at least have to include real semantic queries, which I think is not currently what you're doing, you're just doing language analytics like NLP. This is maybe how you intend to solve semantic queries later, but since this is not what you're doing, I think that should be clear from the get-go. Especially because the "scale" of the current AI paradigm has nothing to do with how the tokenization happens, but rather with how the statistical model is trained to answer semantic queries.

Finally, the example of "Find all Greek-origin technical terms" is a poor one because it is exactly the kind of "knowledge graph" question that was answerable before the current AI hype.

Nevertheless, love the effort, good luck!

(oh and btw: I'm not an expert, so if any of this is wrong, please correct me)

Advice to OP: lay off the Claude Code if your goal is to become an “independent researcher”. Claude doesn’t know what it’s doing, but it’s happy to lead you into a false sense of achievement because it’ll never tell you when you’re wrong, or when it’s wrong.

> I'm a self-taught developer and researcher who left school at 16, and I've spent some time exploring a first-principles approach to system design for various frontier problems.

As much as I appreciate new ways of thinking, whenever I read "first-principles approach", my alarm bells go off. More often than not it just means "I chose to ignore (or am too impatient to learn about) all insights that generations of research in this field have made". The "left school at 16" and "self-taught" parts also indicate that. This may explain the hyperbole of the title as well, as it does not pass the smell test.

If you are looking for advice, here is mine: try to not ignore those that came before you. Giants' shoulders are very wide, very high up and pretty solid. There is no shame in standing on them, but it takes effort to climb up.

Outstanding features:

- way better representation (very information-dense) of different basic language properties directly as a storage layout property (which seems absolutely possible to me to achieve)

- attention (signal) as resonance: analog wave signal processing methods can be used -> way less computation power needed

Analysis: It will have the same fundamental limitations in terms of "understanding" and "thinking" as traditional LLMs, as its "knowledge" is still based on language itself. I believe it would be implemented in combination with other models, which supply nuances of actual content – namely traditional LLMs, which are focussed on written text as it appears. Nevertheless, it should add a high-quality and high-efficient building block for language processing to the landscape of LLMs. Furthermore it may also be a nice starting point for a general development towards rethinking architecture patterns in favor of lower resource consumption and fine quality of any kind of information.