That's what I meant by hiring a self-employed freelancer. I don't know a lot about contracting so maybe I used the wrong phrase.

One built a bunch of local search tools with MCP and that’s where I get a lot of my value out of it

RAG workflows are incredibly useful and with modern agents and tool calls work very well.

They kind of went out of style but it’s a perfect use case.

I have to use Gemini in one specific workflow

I would love some story on why AI is needed at all.

How bad is this on a scale of sad emoji to eggplant emoji.

Children are replacing us, it's terrifying.

ok so, i have large reservations with how LLM’s are used. but when used correctly they can be helpful. but where and how?

if you were to use it as a tutor, the same way you would ask a friend what a segment of code does, it will break down the code and tell you. and it will get as nity grity, and elementary school level as you weir wish without judgement, and i in what ever manner you prefer, it will recommend best practices, and will tell you why your code may not work with the understanding that it does not have the knowledge of the project you are working on. (it’s not going to know the name of the function you are trying to load, but it will recommend checking for that in trouble shooting).

it can rtfm and give you the parts you need for any thing with available documentation, and it will link to it so you can verify it, wich you should do often, just like you were taught to do with wikipedia articles.

if you ask i it for code, prepare to go through each line like a worksheet from high school to point out all the problems, wile good exercise for a practicle case, being the task you are on, it would be far better to write it yourself because you should know the particulars and scope.

also it will format your code and provide informational comments if you can’t be bothered, though it will be generic.

again, treat it correctly for its scope, not what it’s sold as by charletons.

Batch process turning unstructured free form text data into structured outputs.

As a crappy example imagine if you wanted to download metadata about your albums but they’re all labelled “Various Artists”. You can use an LLM call to read the album description and fix the track artists for the tracks, now you can properly organize your collection.

I’m using the same idea, different domain and a complex set of inputs.

It can be much more cost effective than manually spending days tagging data and writing custom importers.

You can definitely go lighter than LLMs. You can use gensim to do category matching, you can use sentence transformers and nearest neighbours (this is basically what Semantle does), but LLM performed the best on more complex document input.

Oops I dweted evewyfing 🥺

Replit sucks

You're referring to more generic machine learning, not LLMs. These are vastly different technologies.

And I have used them for programming, I know their limitations. They don't really transfer solutions to new problems, not on their own anyway. It usually requires pretty specific prompting. They can at best apply solutions to problems, but even then it's not a truly generalised thing, even if it seems to work for many cases.

That's the trap you're falling into as well; LLMs look like they're doing all this stuff, because they're trained on data produced by people who actually do so. But they can't think of something truly novel. LLMs are mathematically unable to truly generalize, it would prove P=NP if they did (there was a paper from a researcher in IIRC Nijmegen that proved this). She also proved they won't scale, and lo and behold LLM performance is plateauing hard (except in very synthetic, artificial benchmarks designed to make LLMs look good).

Even after he used "ALL CAPS"?!? Impossible!

That's pretty much what google says they use AI for, for structuring.

Thanks for your insight.

They don’t really transfer solutions to new problems

Lets say there is a binary format some old game uses (Doom), and in it some of its lumps it can store indexed images, each pixel is an index of color in palette which is stored in another lump, there's also a programming language called Rust, and a little known/used library that can look into binary data of that format, there's also a GUI library in Rust that not many people used either. Would you consider it an "ability to transfer solutions to new problems" that it was able to implement extracting image data from that binary format using the library, extracting palette data from that binary format, converting that indexed image using extracted palette into regular rgba image data, and then render that as window background using that GUI library, the only reference for which is a file with names and type signatures of functions. There's no similar Rust code in the wild at all for any of those scenarios. Most of this it was able to do from a few little prompts, maybe even from the first one. There sure were few little issues along the way that required repromting and figuring things together with it. Stuff like this with AI can take like half an hour while doing the whole thing fully manually could easily take multiple days just for the sake of figuring out APIs of libraries involved and intricacies of recoding indexed image to rgba. For me this is overpowered enough even right now, and it's likely going to improve even more in future.

That is also the premise of one of the stories in Asimov's I, Robot. Human operator did not say the command with enough emphasis, so the robot went did something incredibly stupid.

Those stories did not age well... Or now I guess they did?

So it's the LLM's fault for violating Best Practices, SOP, and Opsec that the rest of us learned about in Year One?

Someone needs to be shown the door and ridiculed into therapy.

His mood shifted the next day when he found Replit “was lying and being deceptive all day. It kept covering up bugs and issues by creating fake data, fake reports, and worse of all, lying about our unit test.”

yeah that's what it does

That's applying existing solutions to a different programming language or domain, but ultimately every single technique used already exists. It only applied what it knew, it did not come up with something new. The problem as stated is also not really "new" either, image extraction, conversion and rendering isn't exactly a "new problem".

I'm not disputing that LLMs can speed up some work, I know it occasionally does so for me as well. But what you have to understand is that the LLM only remembered similar problems and their solutions, it did not at any point invent something truly new. I understand the distinction is difficult to make.

Headling should say, "Incompetent project managers fuck up by not controlling production database access. Oh well."

I understand what you're having in mind, I've had similar intuitions about AI in early 2000s.
What exactly is "truly new" is an interesting topic ofc, but it's a separate topic.
Nowadays I'm trying to look at things more empyrically, without projecting my internal intuitions on everything.
In practice it does generalize knowledge, use many forms of abstract reasoning and transfer knowledge across different domains.
And it can do coding way beyond the level of complexity of what average software developer does at everyday work.

Replit is a vibe coding service now? Swear it just used to be a place to write code in projects

I knew it would make you mad but I did it anyway.

I don't think you have the guts to do anything about it either, vibe coder.