[sota_pop]

It’s interesting to me how many people articulate things like the “Preiminary Truths” section as if they are novel insights.

Effective people managers (of whom I would not specifically consider myself) have known these tenets for as long as history. “Be concise”, “state your intent clearly”, funny how these are touted as novel “strategies” with which to expertly direct AI.

I don’t agree that “everything is a file”. Files are arrays of bytes. For an LLM, everything is a vector of tokens/embeddings.

An aphorism I recently heard: "All sufficiently advanced technology eventually becomes a web browser".

… seems apt especially in the context of the progression from chat-windows to harnesses and onwards to “harnesses that can do anything”.

[TacticalCoder]

> I don’t agree that “everything is a file”. Files are arrays of bytes. For an LLM, everything is a vector of tokens/embeddings.

And yet, as of now, LLMs have a hammer (Bash / command line utilities) and every problem they have looks like a nail.

If there are people around you who are non-techies that are using Claude Code or similar, you'll hear them ask "what the heck is cron?" and "why is it talking to me about Bash again?".

At some point we may have LLMs working on their own output using a set of tools that'd be the equivalent of Bash (or any other terminal prompt) + command line utilities manipulating not files but tokens/embedded vectors but as of now, it sure looks like everything is a file, especially to LLMs.

[sota_pop]

That is fair, I HAVE INDEED seen similar of those anecdotes. However, I still am skeptical of the file analogy.

LLMs are good at traversing traditional file systems because file/tree-like structures very likely encompass an overwhelming supermajority of their training set. By contrast, other approaches like graph-based vector/data discovery via sql queries seem equally or more promising (to me), not to mention the ability to run curl/http queries. Either way, it’s still all prompt engineering approaches to discover/manage context. In this way, files seem like a “local minimum” ie a medium that dually optimizes interpretability and accessibility for both LLMs AND humans.

Just riffing here, but this could even broadly be considered a discussion of memory-vs-storage (somewhat analogous to fluid vs crystallized memory in humans). In this way, one could imagine the models performing context compaction/backup by periodically dumping their context to files (or some other non-volatile storage) WITHOUT coming back to feature space.. just dump/load the tokens directly.

Personally, I am much more interested in even other approaches like VLMs (using visual tokens), architectures like auto encoders and its variations, jepa architectures and other approaches that emphasize operating primarily within the latent space.

To be fair, my particular interests have always been more in the computer vision area, and have been amused to watch the attention mechanism rise to prominence even over CNNs (given the contrast |similarity in their mechanics). Then again, I began my journey in the ML field when GANs were still the hotness, but I digress……

for LLMs, it’s tokens all the way down, and the name of the game is how discoverable and accessible can you make them?