You… understand the Cortana on windows is literally named as a reference to Cortana from Halo right? Because Microsoft owns Halo…? They named it from her… So its the same “Cortana” so to say.

Its a fundamental counterpoint to the OPs post, because it wasn’t made feminine as some kind of mental gymnastics misogyny thing, its just a nerdy reference to an already existing character Microsoft had the rights to, its not that deep.

Cortana was female.

I pointed this out in another post but Cortana is also a “higher level” AI in her depiction origins (Halo) which is inherently a counterpoint to the OP’s post anyways…

It counts as an assistant, and its a gender neutral name cuz its a place name, not a person name.

Its name was specifically chosen to be weird and different to avoid the “accidently triggers in common convo” problem that other assistants tend to have

The name was chosen because it was a play on the SRI technology and because it is a girl’s name.

The name was actually chosen because it was originally going to be the name of one of the founder’s soon to be daughter, but then his child ended up being a son so he gave the name to the machine instead as his “second child” effectively…

So it had literally nothing to do with whatever point the poster was trying to make, and everything to do with a sense of paternal love if literally anything, lol… People will find literally fucking anything to mald over, even making shit up to try and make it sound right.

Yeah but also is a heavy counterpoint to the point in the post, because Cortana was already a “higher level of autonomy” AI in her first depiction (Halo games), from the start, and Microsoft named it after the character because Microsoft bought Halo and was just doing a nod to the character… So thats literally an outright counterpoint to whatever mental gymnastics the poster of the post was doing…

Siri is definitely a gender neutral name, Ive literally met more dudes named Siri than gals when out traveling, especially in eastern Europe.

Literally only one AI assistant Im aware of was given a feminine persona out the gate and thats Alexa which is Amazon’s.

Every single other one has been purposefully kept gender neutral.

They intentionally gave Siri a gender neutral name ages ago cuz you can pick what its voice sounds like

Same for gemini, copilot, gpt…

Only 1 out of many agents had a female name, and it wasnt “tech bros” that named it.

And only one tool has been given a male name, Claude

Once a model is trained, they become functionally opaque. Weights shift… but WHY. What does that vector MEAN. True, but I guess my point is a lot of people ascribe, as you pointed out, way more “spirit” or “humanity” to what an LLM is, whereas in reality its actually a pretty simple lil box. Numbers go in, numbers come out, and all it does is guess what the next number is gonna be. Numbers go BRRRRRRRRR

I think where I fundamentally disagree is that “they do what they say they do” by any definition beyond the simple tautology that everything is what it is.

I guess I was referring to when theres a lot of tools out there that are built to do stuff other than what it outta do.

Like stick a flashlight onto a wrench if you will. Now its not just a wrench, now its a flashlight too.

But an LLM is… pretty much just what it is, though some people now are trying pretty hard to make it be more than that (and not by adding layers overtop, Im talking about training LLMs to be more than LLMs, which I think is a huge waste of time)

I apologize for any confusion.

I meant LLMs are what they say they are in a non literal sense.

Akin to abscribing the same to any other tool.

“I like wrenches cause they are what they say they are, nothing extra to them” in that sort of way.

In the sense the tool is very transparent in function. No weird bells or whistles, its a simple machine that you can see what it does merely by looking at it.

If you knpw how to use docker and claim that agents are only funded by large corps, then you must really be living under a rock and/or dont know how to google.

Theres tonnes of grassroots agentic FOSS platforms available and self hostable models people all over the world have built to run on them.

Your either extremely out of touch or purposefully spreading disinformation if you think MCP backed agentic options are limited only to “bog tech corporations”.

Go… google it? I dunno, theres tonnes of options out there now, you are talking like its still 2024, shit has moved way past beyond that now…

Nothing you linked there contradicts what I said. It expands on it in more specific detail.

LLMs are heuristic statistical token prediction engines.

Hallucinations are a shorthand term for a set of phenomena that arise out of the way the statistical prediction works, where it will string together sentences that are grammatically correct and sound right, but an LLM has no concept of right/wrong, only statistically likely next token given the prior.

That wiki article goes into much more depth on the “why” but it does support my statement.

I dunno what it is with people and linking wiki articles that support the person’s statement and claiming its the opposite.

… learn to read I guess? I dunno lol.

MCP is a fairly basic concept and just a specific type of a web server,

What part of that did you not understand.

We don’t need yet another layer on top that obscures web properties and places them behind chatbots benefiting Big Tech megacorps and nobody else.

If you think MCP servers benefit “Big Tech megacorps and nobody else” then all I can conclude is you are technically behind enough you dont even know how to use docker and therefore your argument is coming from a place of naivety

MCP servers are incredibly simple and easy to self host, and a few self hostable models are competent now at invoking them.

Tonnes of FOSS self hostable software supports wiring it up as well.

Which means anyone can leverage MCP servers to enable LLMs to do whatever you want.

I would compare it to advancements in stuff like Zigbee for IOT devices, its a simple lightweight spec thats small enough you can even put it on an ESP32 with ease.

And if you dont see how there’s a lot of power in that for private self hosted users, then you arent using your imagination enough.

You’ve gotta be living under a rock if you dont think the models themselves have been improving over the last year, lol.

We are bumping into a log scale problem where people arent fully grasping how big of a difference going from an x% error rate to a y% error rate is in actual practice for where it matters.

Hallucinate is the term used for the statistical phenomena that arises from their output.

Yeah thats not what I was saying

Personally I believe MCP is the new AMP, and I look forward to dancing on its grave.

Care to elaborate? MCP is a fairly basic concept and just a specific type of a web server, so its not exactly going to go anywhere anytime soon, since you are literally posting on a forum right now that uses the same tech, lol

When using the word “they”, in English it refers the the last primary subject you referred to, so you should be able to infer what “they” referred to in my sentences. I’ll let you figure it out.

“I love wrenches, they are very handy tools”, in this sentence, the last subject before the word “they” was “wrenches”, so you should be able to infer that “they” referred to “wrenches” in that sentence.

The “wine glass half full” thing, I assume, is you referring to the problem surrounding trying to image generate a specific glass of wine, or similar issues of “generate a room that definitely doesnt have an elephant in it, its devoid of any elephants, zero elephants in the room”

This is specifically a stable diffusion problem, and doesnt really apply to LLMs in the same manner.

Everything I said was very much correct.

LLMs are fairly primitive tools, they arent super complex and they do exactly what they say they do.

The hard part is wrapping that up in an API that is actually readable for a human to interact with, because the lower level abstract data of what an LLM takes in and spits out arent useful for us.

And then even harder is wrapping THAT API in another one that makes the input/output USEFUL for a human to interact with

You have layers upon layers of abstraction overtop of the tool to make it go from just a bunch of raw float values a human wouldnt understand, to becoming a tool that does a thing

That “wrapper” is what one calls the “platform”.

And making a platform that doesnt fuck it up is actually very very hard, and very very easy to get wrong. Even a small tweak to it can substantially shift how it works

Think of it a lot like an engine in a car. The LLM is the engine, which on its own is not actually super useful. You have to actually connect that engine to something to make it do anything useful.

And even just doing that isnt very useful if you cant control it, so we take the engine and wrap it up in a bunch of layers of stuff that allow a human to now control it and direct it.

But, turns out, when you put a V6 engine inside a car, even a tiny little bit of getting the engineering wrong can cause all sorts of problems with the engine and make it fail to start, or explode, or fall out of the car, or stall out, or break, or leak… and unlike car engines, these engines are very very new and most engineers are still only just now starting to break ground on learning how to control them well and steer them and stop them from tearing themselves out of the car, lol.

So, to bring this back to the original post:

Most LLMs (engines) are actually pretty good nowadays, but the problem was Clawdbot (a specific brand of car manufacturer) super fucked up the way they designed their car so the car itself had a very very stupid engineering mistake. IE in this case, the brakes didnt work well enough and the car drove off a cliff.

That has nothing to do with how good the engine is or is not, the engine was just doing its job. The problem was with some other part of the car entirely, the part of the car Clawdbot made that wraps around the engine.

Nah there have been huge advancements in the past few months, you are definitely out of touch if you havent witnessed them

Recent models have gotten WAY better at “second guessing” themselves, and not acting nearly so confidently wrong.

I don’t think we’ve overcome the halfglass of wine issue

That isnt an LLM issue at all, that has nothing to do with LLMs in fact. Thats a problem with Stable Diffusion which is an entirely different kind of AI, but yeah that issue is fundamental to what stable diffusion is.

with most of the “improvements” being seen coming from basically better engineering

I mean, thats not much different from any other tech, a LOT of advanced tech we have today is dozens and dozens of separate bits of engineering all working in tandem to create something more meaningful.

Your smartphone has countless different and distinct advancements on different types of technology that come together to make a useful device, and if you removed any one of those pieces from it, it would be substantially less useful as a tool.

So yeah, I personally will very much count the other pieces of the puzzle, advancing, as the system as a whole advancing.

LLMs today compared to ones a year ago are quite a bit better, by a large degree, and the tooling around them has also improved a lot. The proliferation of Model Context Protocol Tools is proving to be a massive part of the system as a whole becoming something actually very useful.