However LLMs are not really tiny compared to real brains, brains FLOP/s have been estimated to be around 1e15 to 1e21, while a single H100 SXM can already do 1e15 BF16 FLOP/s.

The brain also has about 1e15 synapses, each holding the equivalent of about 4.7 bits of information, which would be about 590 TB worth of parameters, equivalent to 294T BF16 parameters (would need 7.4k H100s to hold those, at 80GB per H100).

So in my opinion, it's more of a memory bandwidth problem than a computing power problem, as H100s have comparatively very little memory bandwidth per FLOP than the brain, which holds information directly inside the computing units (synapses).

And I also think that detail recall is a double-edged sword. Memorizing details can certainly be useful, but it can also compete with pattern generalization. If you can perfectly remember the training set (overfit) you don't need to learn to generalize.

What do you mean by this ? The model is the neural network itself and the parameters of the model are the neural network weights (+ non-trainable parameters).

For example, when asked to write a report on Docker and explain how it works, one of the first paragraphs was : "1. *Docker Engine*: The core component, which is a client-server application that uses a server component (the daemon) to manage containers and a client interface to interact with the daemon."

This is perfectly correct and makes sense for someone that is familiar with software engineering and already knows what containers are. Not so much for someone wanting a introduction to it. Do you imagine writing something like this at the very beginning of your explanation of Docker ? It even said: "I wrote it in the neutral register from your publication preferences, with no em dashes, and defined terms on first use.", when it didn't even define what a container is. I chose Docker at random, because I could judge if its explanation made sense, since I know how it works.

A potential solution for these indexing systems is to maintain several indexes (one per branch for example). Idk how they handle code changes though.

But I think the bigger negative signal is that these tools have been around for roughly as long as LLMs have, yet they are still barely used. Maybe because they are not that useful ?

ChatGPT says it could work without updating the code, but since README doesn't advertise it, I was curious as to the reason behind this choice.

It says: "Can Slopdex run without paid embedding APIs?

Potentially yes, without modifying its code. [...] Slopdex supports a custom embeddingBaseUrl, allowing it to call a locally hosted, OpenAI-compatible embedding server. For example, with a local model producing 768-dimensional vectors: { "provider": "openai", "model": "your-local-embedding-model", "dimensions": 768, "symbolDimensions": 768, "embeddingBaseUrl": "http://localhost:1234/v1" }

Its code still requires an OPENAI_API_KEY value, but a dummy key should work if your local server doesn't enforce authentication.

The server must implement /v1/embeddings and accept Slopdex's request format, including the dimensions parameter. The relevant code is in vector_openai.rs."

For example, they have become more and more unintelligible when you ask for explanations or descriptive text. They assume you see the same context as them and shortcut explanations.

I've had to craft a skill to get them to produce remotely understandable explanations of even mildly complex/non-mainstream subjects.

This may be Curse of Knowledge https://en.wikipedia.org/wiki/Curse_of_knowledge on their part, and it also impacts human experts but still. Becoming better at one thing does not mean you become better at everything else, although I do agree with you that the better they become the more things there are they become good at, but their ability is still quite jagged and maybe increasingly so.

Not to say these proofs are slop, even if the LLM-generated work is of good quality, what happens when no one knows how it works anymore ? Even if you ask the LLM to explain, which it does quite badly, the time to understand the explanation is incompressible.

So in the end, productivity will probably reach a ceiling that we can estimate as the product of humans, their cognitive capacity and their time. And that ceiling may be lower than what AI companies valuation expect, regardless of the compute and they can pump out and the RSI level they can reach.