I'm curious why you find descriptions of images useful for searching. I developed a similar flow and ended up embedding keywords into the image metadata instead. It makes them easily searchable and not tied to any databases, and it is faster (dealing with tens of thousands of images personally).

* https://github.com/jabberjabberjabber/LLavaImageTagger

nice!

Who can trust a company whose name contradicts its presence.

Not yet. It has ‘Knowledge sources’ that you can set up, and I think that supplies data for built in RAG - but I am not sure until I try it.

Were you using claude’s computer mode? It can do this

I work on such a team, and we don't refer to ourselves as "AI engineers". We're devs working on developing deep learning systems (but not genAI).

I haven't seen this sort of work called AI Developer yet, but I may have missed the trend shift. Isn't the convention for this still to use the title of Machine Learning Engineer (MLE)?

Are you saying that true Data Engineers typically do more than just use Tableau or run OLAP queries, or do you see the title 'Data Engineer' itself as a bit of a red flag these days? I’m pretty early in my career and was leaning toward Data Engineering, but hearing stuff like this makes me wonder if going for SWE might be smarter.

having data engineer in my title has led to recruiters calling about DBA type roles. ugh.

I feel the same way about “UX Designer/Engineer”. Seems to mean someone who can put a wireframe together but has no design chops. Any good designer that has experience crafting user interfaces should be skilled at thinking through in-depth about what the user is doing when using the product and how to successfully guide them through that process.

Eh. Titles dont really mean anything. Do we want to start the age old software engineer vs software developer title debate again?

Let someone call themselves whatever they want. If they can do the job they were hired for then... who cares?

To be fair, the market dictates the job titles.

You would be surprised how many people have no idea what "machine learning" means (not even technical definition but just as in the field). I'm working on a PhD in an adjacent field and basically have to tell people I work in AI for them to have any semblance of what I do.

I used to be in the same camp, but have since abandoned this position. The world has changed. Very serious people use the term "AI" nowadays. I don't find that it's a high-precision signal for identifying charlatans anymore (though it is high-recall).

in 2024 thats as crazy as being suspicious of anyone that uses the term “internet”

> back in the day

Well, most are called "project manager" now. But it would still be a giant red flag, just like the project manager job title or even worse, using PM so you don't know exactly what it means.

"AI engineer" is the new "Web developer".

I prefer the term "Scrumlord" /s

Of the great developers I have worked with in real life, across a large number of projects and workplaces, very few have any Github presence. Most don't even have LinkedIn. They usually don't have any online presence at all: No blog with regular updates. No Twitter presence full of hot takes.

Sometimes this industry is a lot like the "finance" industry: People struggling for credibility talk about it constantly, everywhere. They flex and bloviate and look for surrogates for accomplishments wherever they can be found. Peacocking on github, writing yet another tutorial on what tokens are and how embeddings work, etc.

That obviously doesn't mean in all cases, and there are loads of stellar talents that have a strong online presence. But by itself it is close to meaningless, and my experience is that it is usually a negative indicator.

drones may still be a thing, and weren't nearly as hyped as blockchain.

my barometer for penetration is how often the non-tech people talk about it, e.g. goofball uncle didn't buy a drone, but he went hard on BTC. if he's still holding he probably made money recently, too.

... and Cloud! Don't forget The Klaaooud ...

Of course it does, your resume is a critical part of selling your skills & services to employers. Want to close faster and for more $$$? Demonstrate your value prop in the terms they know and care about.

The term AI engineer is now pretty well recognised in the field (https://www.latent.space/p/ai-engineer), and is very much not the same as an AI researcher (which would be involved in training and building new models). I'd expect an AI engineer to be primarily a software developer, but with an excellent understanding of how to implement, use and evaluate LLMs in a production environment, including skills like evaluation and fine-tuning. This is not some dataset you can just bundle in software developer.

> these hero images are a convenient cue to stop reading.

If you don't like such content. But I would say don't judge a book by its cover.

A trend I see in the "frothiest" parts of the AI world is an inattention to details and overwhelming excitement about things just over the horizon. LLMs are clearly a huge deal and will be changing a lot of things and also there are a bunch of folks wielding it like a blunt object without the discernment to notice that they're slightly off. I'm looking forward to the next couple of decades but I'm worried about the next five years.

You aren't exaggerating! There are some creepy arms in that image, along with the other weirdness. I'm surprised Karpathy of all people used such a poor quality image for such a post.

Yeah, wow. And it's a lovely post otherwise, too!

The Spam sketch from Monty Python comes to mind.

Edit: i looked at it more closely and the way the people are sitting, their direction, is totally unnatural.

If the P is silent, how can you "Prompt"?

as it is in "swimming"

What does 50% precision mean in this case? I know 50% accuracy might mean P(fraud_predicted | fraud) = 50%, but I don't understand what you mean by precision?

I'd hope you're shipping _anti_-fraud models. Fraud models are abundant in fintech. On a side note, anyone who _really_ knows LLMs will be able to do your primitive fintech models with their eyes closed. You don't need a ton of skill to build a logistic regression model that runs at 14 qps. :-)

No we don't. Humans were capable of mathematics and analytical tasks long before the establishment of modern twelve year education. We don't require "installing" a program to do basic mathematics, as we would never have figured out basic agriculture or developed civilization or seafaring if that were the case. I mean, Eratosthenes worked out the circumference of the Earth to a reasonable degree of accuracy in the third century BC. Even primitive hunter gather societies had concepts of counting, grouping and sequence that are beyond LLMs.

If humans were as bad as LLMs at basic math and logic, we would consider them developmentally challenged. Yet this constant insistence that humans are categorically worse than, or at best no better than, LLMs persists. It's a weird, almost religious belief in the superiority of the machine even in spite of obvious evidence to the contrary.

It seemingly built the modern world.

Ineffective seems harsh.

No, they don't. They capture underlying patterns in training data and use them to generate text that fits the context.

Codegen, contentgen, and driving existing products in human language (which you can largely bucket into interactive codegen)

At a very minimum I'd say they'll have a way to "chat" with the apps to ask questions / do stuff. Either via APIs that the OS calls or integrated in the app via whatever frameworks will rise to handle this. In 5 to 10 years we'll probably see this. At a very minimum searching docs and "guide" the users through the functionality / do it straight up when asked.

Basically what chatgpt did for chatbots, but at app level. There are lots of apps that take a long time to master. But the average joe doesn't need to master them. If I want to lightly edit some photos, I know photoshop can do it, but I have no clue where that specific thing is in the menus, because I haven't used it in 10 years. But it would be cool to type in a chat box "take all the pictures from my sd card, adjust the colors, straighten the ones that need it, and put them in my Pictures folder under "trip to the sea". And then I can go do something else for the 30-60 minutes it would have taken me to google how to do all of that, or script something, etc.

The ideea of an assistant that can work like that isn't that far-fetched today, IMO. The apps need to expose some APIs, and the "os" needs an language -> action model capable enough to handle basic stuff for average joes. I'd bet good money sonnet3.5 + proper APIs + a bit of fine-tuning could do it today for 50%+ of average user cases.

If it makes any kind of decision whatsoever (like an "if" statement), slap the word AI on it.

Think of it as another human having access to the keyboard, mouse and the screen buffer.