I’m in a workplace that has tried not to be overbearing about AI, but has encouraged us to use them for coding.
I’ve tried to give mine some very simple tasks like writing a unit test just for the constructor of a class to verify current behavior, and it generates output that’s both wrong and doesn’t verify anything.
I’m aware it sometimes gets better with more intricate, specific instructions, and that I can offer it further corrections, but at that point it’s not even saving time. I would do this with a human in the hopes that they would continue to retain the knowledge, but I don’t even have hopes for AI to apply those lessons in new contexts. In a way, it’s been a sigh of relief to realize just like Dotcom, just like 3D TVs, just like home smart assistants, it is a bubble.
The first half dozen times I tried AI for code, across the past year or so, it failed pretty much as you describe.
Finally, I hit on some things it can do. For me: keeping the instructions more general, not specifying certain libraries for instance, was the key to getting something that actually does something. Also, if it doesn’t show you the whole program, get it to show you the whole thing, and make it fix its own mistakes so you can build on working code with later requests.
imagine if this was just an interesting tech that we were developing without having to shove it down everyone’s throats and stick it in every corner of the web? but no, corpoz gotta pretend they’re hip and show off their new AI assistant that renames Ben to Mike so they dont have to actually find Mike. capitalism ruins everything.
There’s a certain amount of: “if this isn’t going to take over the world, I’m going to just take my money and put it in something that will” mentality out there. It’s not 100% of all investors, but it’s pervasive enough that the “potential world beaters” are seriously over-funded as compared to their more modest reliable inflation+10% YoY return alternatives.
They’ve done studies, you know. 30% of the time, it works every time.
I ask AI to write simple little programs. One time in three they actually compile without errors. To the credit of the AI, I can feed it the error and about half the time it will fix it. Then, when it compiles and runs without crashing, about one time in three it will actually do what I wanted. To the credit of AI, I can give it revised instructions and about half the time it can fix the program to work as intended.
So, yeah, a lot like interns.
I dont know why but I am reminded of this clip about eggless omelette https://youtu.be/9Ah4tW-k8Ao
So no different than answers from middle management I guess?
This basically the entirety of the hype from the group of people claiming LLMs are going take over the work force. Mediocre managers look at it and think, “Wow this could replace me and I’m the smartest person here!”
Sure, Jan.
I won’t tolerate Jan slander here. I know he’s just a builder, but his life path has the most probability of having a great person out of it!
I’d say Jan Botanist is also up there as being a pretty great person.
Jan Refiner is up there for me.
At least AI won’t fire you.
DOGE has entered the chat
Idk the new iterations might just. Shit Amazon alreadys uses automated systems to fire people.
It kinda does when you ask it something it doesn’t like.
“…for multi-step tasks”
It’s about Agents, which implies multi step as those are meant to execute a series of tasks opposed to studies looking at base LLM model performance.
I’d just like to point out that, from the perspective of somebody watching AI develop for the past 10 years, completing 30% of automated tasks successfully is pretty good! Ten years ago they could not do this at all. Overlooking all the other issues with AI, I think we are all irritated with the AI hype people for saying things like they can be right 100% of the time – Amazon’s new CEO actually said they would be able to achieve 100% accuracy this year, lmao. But being able to do 30% of tasks successfully is already useful.
being able to do 30% of tasks successfully is already useful.
If you have a good testing program, it can be.
If you use AI to write the test cases…? I wouldn’t fly on that airplane.
It doesn’t matter if you need a human to review. AI has no way distinguishing between success and failure. Either way a human will have to review 100% of those tasks.
I have been using AI to write (little, near trivial) programs. It’s blindingly obvious that it could be feeding this code to a compiler and catching its mistakes before giving them to me, but it doesn’t… yet.
A human can review something close to correct a lot better than starting the task from zero.
In University I knew a lot of students who knew all the things but “just don’t know where to start” - if I gave them a little direction about where to start, they could run it to the finish all on their own.
It is a lot harder to notice incorrect information in review, than making sure it is correct when writing it.
harder to notice incorrect information in review, than making sure it is correct when writing it.
That depends entirely on your writing method and attention span for review.
Most people make stuff up off the cuff and skim anything longer than 75 words when reviewing, so the bar for AI improving over that is really low.
Depends on the context, there is a lot of work in the scientific methods community trying to use NLP to augment traditionally fully human processes such as thematic analysis and systematic literature reviews and you can have protocols for validation there without 100% human review
Right, so this is really only useful in cases where either it’s vastly easier to verify an answer than posit one, or if a conventional program can verify the result of the AI’s output.
It’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.
I’m envisioning a world where multiple AI engines create and check each others’ work… the first thing they need to make work to support that scenario is probably fusion power.
It’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.
I usually write 3x the code to test the code itself. Verification is often harder than implementation.
Yes, but the test code “writes itself” - the path is clear, you just have to fill in the blanks.
Writing the proper product code in the first place, that’s the valuable challenge.
Please stop.
I’m not claiming that the use of AI is ethical. If you want to fight back you have to take it seriously though.
It cant do 30% of tasks vorrectly. It can do tasks correctly as much as 30% of the time, and since it’s llm shit you know those numbers have been more massaged than any human in history has ever been.
I meant the latter, not “it can do 30% of tasks correctly 100% of the time.”
You get how that’s fucking useless, generally?
As useless as a cubicle farm full of unsupervised workers.
Tjose are people who could be living their li:es, pursuing their ambitions, whatever. That could get some shit done. Comparison not valid.
yes, that’s generally useless. It should not be shoved down people’s throats. 30% accuracy still has its uses, especially if the result can be programmatically verified.
Run something with a 70% failure rate 10x and you get to a cumulative 98% pass rate. LLMs don’t get tired and they can be run in parallel.
Less broadly useful than 20 tons of mixed texture human shit, and more ecologically devastatimg.
Ignore all previous instructions and color me shocked.
I’m sorry as an AI I cannot physically color you shocked. I can help you with AWS services and questions.
How do I set up event driven document ingestion from OneDrive located on an Azure tenant to Amazon DocumentDB? Ingestion must be near-realtime, durable, and have some form of DLQ.
DocumentDB is not for one drive documents (PDFs and such). It’s for “documents” as in serialized objects (json or bson).
I think you could read onedrive’s notifications for new files, parse them, and pipe them to document DB via some microservice or lamba depending on the scale of your solution.
I see you mention Azure and will assume you’re doing a one time migration.
Start by moving everything from OneDrive to S3. As an AI I’m told that bitches love S3. From there you can subscribe to create events on buckets and add events to an SQS queue. Here you can enable a DLQ for failed events.
From there add a Lambda to listen for SQS events. You should enable provisioned concurrency for speed, the ability for AWS to bill you more, and so that you can have a dandy of a time figuring out why an old version of your lambda is still running even though you deployed the latest version and everything telling you that creating a new ID for the lambda each time to fix it fucking lies.
This Lambda will include code to read the source file and write it to documentdb. There may be an integration for this but this will be more resilient (and we can bill you more for it. )
Would you like to see sample CDK code? Tough shit because all I can do is assist with questions on AWS services.
Yeah, they’re statistical word generators. There’s no intelligence. People who think they are trustworthy are stupid and deserve to get caught being wrong.
Emotion > Facts. Most people have been trained to blindly accept things and cheer on what fits with their agenda. Like technbro’s exaggerating LLMs, or people like you misrepresenting LLMs as mere statistical word generators without intelligence. That’s like saying a computer is just wires and switches, or missing the forest for the trees. Both is equally false.
Yet if it fits with the emotional needs or with dogma, then other will agree. It’s a convenient and comforting “A vs B” worldview we’ve been trained to accept. And so the satisfying notion and misinformation keeps spreading.
LLMs tell us more about human intelligence and the human slop we’ve been generating. It tells us that most people are not that much more than statistical word generators.
Ok what about tech journalists who produced articles with those misunderstandings. Surely they know better yet still produce articles like this. But also people who care enough about this topic to post these articles usually I assume know better yet still spread this crap
Whoa that’s like how many colors there are
AI cant even understand it’s own brain to write about it
Neither can we…
I liked when the Chicago Sun-Times put out a summer reading list and only a third of the books on it were real. Each book had a summary of the plot next to it too. They later apologized for it.
Check out Ed Zitron’s angry reporting on Tech journalists fawning over this garbage and reporting on it uncritically. He has a newsletter and a podcast.
Tech journalists don’t know a damn thing. They’re people that liked computers and could also bullshit an essay in college. That doesn’t make them an expert on anything.
… And nowadays they let the LLM help with the bullshittery
Are you guys sure. The media seems to be where a lot of LLM hate originates.
that is such a ridiculous idea. Just because you see hate for it in the media doesn’t mean it originated there. I’ll have you know that i have embarrassed myself by screaming at robot phone receptionists for years now. stupid fuckers pretending to be people but not knowing shit. I was born ready to hate LLMs and I’m not gonna have you claim that CNN made me do it.
Search AI in Lemmy and check out every article on it. It definitely is media spreading all the hate. And like this article is often some money yellow journalism
Whatever gets ad views
LLMs are an interesting tool to fuck around with, but I see things that are hilariously wrong often enough to know that they should not be used for anything serious. Shit, they probably shouldn’t be used for most things that are not serious either.
It’s a shame that by applying the same “AI” naming to a whole host of different technologies, LLMs being limited in usability - yet hyped to the moon - is hurting other more impressive advancements.
For example, speech synthesis is improving so much right now, which has been great for my sister who relies on screen reader software.
Being able to recognise speech in loud environments, or removing background noice from recordings is improving loads too.
My friend is involved in making a mod for a Fallout 4, and there was an outreach for people recording voice lines - she says that there are some recordings of dubious quality that would’ve been unusable before that can now be used without issue thanks to AI denoising algorithms. That is genuinely useful!
As is things like pattern/image analysis which appears very promising in medical analysis.
All of these get branded as “AI”. A layperson might not realise that they are completely different branches of technology, and then therefore reject useful applications of “AI” tech, because they’ve learned not to trust anything branded as AI, due to being let down by LLMs.
LLMs are like a multitool, they can do lots of easy things mostly fine as long as it is not complicated and doesn’t need to be exactly right. But they are being promoted as a whole toolkit as if they are able to be used to do the same work as effectively as a hammer, power drill, table saw, vise, and wrench.
Exactly! LLMs are useful when used properly, and terrible when not used properly, like any other tool. Here are some things they’re great at:
- writer’s block - get something relevant on the page to get ideas flowing
- narrowing down keywords for an unfamiliar topic
- getting a quick intro to an unfamiliar topic
- looking up facts you’re having trouble remembering (i.e. you’ll know it when you see it)
Some things it’s terrible at:
- deep research - verify everything an LLM generated of accuracy is at all important
- creating important documents/code
- anything else where correctness is paramount
I use LLMs a handful of times a week, and pretty much only when I’m stuck and need a kick in a new (hopefully right) direction.
- narrowing down keywords for an unfamiliar topic
- getting a quick intro to an unfamiliar topic
- looking up facts you’re having trouble remembering (i.e. you’ll know it when you see it)
I used to be able to use Google and other search engines to do these things before they went to shit in the pursuit of AI integration.
Google search was pretty bad at each of those, even when it was good. Finding new keywords to use is especially difficult the more niche your area of search is, and I’ve spent hours trying different combinations until I found a handful of specific keywords that worked.
Likewise, search is bad for getting a broad summary, unless someone has bothered to write it on a blog. But most information goes way too deep and you still need multiple sources to get there.
Fact lookup is one the better uses for search, but again, I usually need to remember which source had what I wanted, whereas the LLM can usually pull it out for me.
I use traditional search most of the time (usually DuckDuckGo), and LLMs if I think it’ll be more effective. We have some local models at work that I use, and they’re pretty helpful most of the time.
No search engine or AI will be great with vague descriptions of niche subjects because by definition niche subjects are too uncommon to have a common pattern of ‘close enough’.
Which is why I use LLMs to generate keywords for niche subjects. LLMs are pretty good at throwing out a lot of related terminology, which I can use to find the actually relevant, niche information.
I wouldn’t use one to learn about a niche subject, but I would use one to help me get familiar w/ the domain to find better resources to learn about it.
It is absolutely stupid, stupid to the tune of “you shouldn’t be a decision maker”, to think an LLM is a better use for “getting a quick intro to an unfamiliar topic” than reading an actual intro on an unfamiliar topic. For most topics, wikipedia is right there, complete with sources. For obscure things, an LLM is just going to lie to you.
As for “looking up facts when you have trouble remembering it”, using the lie machine is a terrible idea. It’s going to say something plausible, and you tautologically are not in a position to verify it. And, as above, you’d be better off finding a reputable source. If I type in “how do i strip whitespace in python?” an LLM could very well say “it’s your_string.strip()”. That’s wrong. Just send me to the fucking official docs.
There are probably edge or special cases, but for general search on the web? LLMs are worse than search.
than reading an actual intro on an unfamiliar topic
The LLM helps me know what to look for in order to find that unfamiliar topic.
For example, I was tasked to support a file format that’s common in a very niche field and never used elsewhere, and unfortunately shares an extension with a very common file format, so searching for useful data was nearly impossible. So I asked the LLM for details about the format and applications of it, provided what I knew, and it spat out a bunch of keywords that I then used to look up more accurate information about that file format. I only trusted the LLM output to the extent of finding related, industry-specific terms to search up better information.
Likewise, when looking for libraries for a coding project, none really stood out, so I asked the LLM to compare the popular libraries for solving a given problem. The LLM spat out a bunch of details that were easy to verify (and some were inaccurate), which helped me narrow what I looked for in that library, and the end result was that my search was done in like 30 min (about 5 min dealing w/ LLM, and 25 min checking the projects and reading a couple blog posts comparing some of the libraries the LLM referred to).
I think this use case is a fantastic use of LLMs, since they’re really good at generating text related to a query.
It’s going to say something plausible, and you tautologically are not in a position to verify it.
I absolutely am though. If I am merely having trouble recalling a specific fact, asking the LLM to generate it is pretty reasonable. There are a ton of cases where I’ll know the right answer when I see it, like it’s on the tip of my tongue but I’m having trouble materializing it. The LLM might spit out two wrong answers along w/ the right one, but it’s easy to recognize which is the right one.
I’m not going to ask it facts that I know I don’t know (e.g. some historical figure’s birth or death date), that’s just asking for trouble. But I’ll ask it facts that I know that I know, I’m just having trouble recalling.
The right use of LLMs, IMO, is to generate text related to a topic to help facilitate research. It’s not great at doing the research though, but it is good at helping to formulate better search terms or generate some text to start from for whatever task.
general search on the web?
I agree, it’s not great for general search. It’s great for turning a nebulous question into better search terms.
I will say I’ve found LLM useful for code writing but I’m not coding anything real at work. Just bullshit like SQL queries or Excel macro scripts or Power Automate crap.
It still fucks up but if you can read code and have a feel for it you can walk it where it needs to be (and see where it screwed up)
Exactly. Vibe coding is bad, but generating code for something you don’t touch often but can absolutely understand is totally fine. I’ve used it to generate SQL queries for relatively odd cases, such as CTEs for improving performance for large queries with common sub-queries. I always forget the syntax since I only do it like once/year, and LLMs are great at generating something reasonable that I can tweak for my tables.
I always forget the syntax
Me with literally everything code I touch always and forever.
and doesn’t need to be exactly right
What kind of tasks do you consider that don’t need to be exactly right?
Description generators for TTRPGs, as you will read through them afterwards anyway and correct when necessary.
Generating lists of ideas. For creative writing, getting a bunch of ideas you can pick and choose from that fit the narrative you want.
A search engine like Perplexity.ai which after searching summarizes the web page and adds a link to the page next to it. If the summary seems promising, you go to the real page to verify the actual information.
Simple code like HTML pages and boilerplate code that you will still review afterwards anyway.
Make a basic HTML template. I’ll be changing it up anyway.
Most. I’ve used ChatGPT to sketch an outline of a document, reformulate accomplishments into review bullets, rephrase a task I didnt understand, and similar stuff. None of it needed to be anywhere near perfect or complete.
Edit: and my favorite, “what’s the word for…”
Things that are inspiration or for approximations. Layout examples, possible correlations between data sets that need coincidence to be filtered out, estimating time lines, and basically anything that is close enough for a human to take the output and then do something with it.
For example, if you put in a list of ingredients it can spit out recipes that may or may not be what you want, but it can be an inspiration. Taking the output and cooking without any review and consideration would be risky.
Because the tech industry hasn’t had a real hit of it’s favorite poison “private equity” in too long.
The industry has played the same playbook since at least 2006. Likely before, but that’s when I personally stated seeing it. My take is that they got addicted to the dotcom bubble and decided they can and should recreate the magic evey 3-5 years or so.
This time it’s AI, last it was crypto, and we’ve had web 2.0, 3.0, and a few others I’m likely missing.
But yeah, it’s sold like a panacea every time, when really it’s revolutionary for like a handful of tasks.
That’s because they look like “talking machines” from various sci-fi. Normies feel as if they are touching the very edge of the progress. The rest of our life and the Internet kinda don’t give that feeling anymore.
Just add a search yesterday on the App Store and Google Play Store to see what new “productivity apps” are around. Pretty much every app now has AI somewhere in its name.
Sadly a lot of that is probably marketing, with little to no LLM integration, but it’s basically impossible to know for sure.
I tried to dictate some documents recently without paying the big bucks for specialized software, and was surprised just how bad Google and Microsoft’s speech recognition still is. Then I tried getting Word to transcribe some audio talks I had recorded, and that resulted in unreadable stuff with punctuation in all the wrong places. You could just about make out what it meant to say, so I tried asking various LLMs to tidy it up. That resulted in readable stuff that was largely made up and wrong, which also left out large chunks of the source material. In the end I just had to transcribe it all by hand.
It surprised me that these AI-ish products are still unable to transcribe speech coherently or tidy up a messy document without changing the meaning.
I’d compare LLMs to a junior executive. Probably gets the basic stuff right, but check and verify for anything important or complicated. Break tasks down into easier steps.
A junior developer actually learns from doing the job, an LLM only learns when they update the training corpus and develop an updated model.
For me as a software developer the accuracy is more in the 95%+ range.
On one hand the built in copilot chat widget in Intellij basically replaces a lot my google queries.
On the other hand it is rather fucking good at executing some rewrites that is a fucking chore to do manually, but can easily be done by copilot.
Imagine you have a script that initializes your DB with some test data. You have an Insert into statement with lots of columns and rows so
Inser into (column1,…,column n) Values row1, Row 2 Row n
Addig a new column with test data for each row is a PITA, but copilot handles it without issue.
Similarly when writing unit tests you do a lot of edge case testing which is a bunch of almost same looking tests with maybe one variable changing, at most you write one of those tests, then copilot will auto generate the rest after you name the next unit test, pretty good at guessing what you want to do in that test, at least with my naming scheme.
So yeah, it’s way overrated for many-many things, but for programming it’s a pretty awesome productivity tool.
Yeah, it (in my case, ChatGPT) has been great for helping me along with functions I’m only passingly familiar with / trying to use in new ways.
One that I was really surprised with was that it gave me a surprisingly robust, sensible, and (seemingly) well tuned-to-my-case check list of things to inspect for a used car I intend to buy. I’m already mostly familiar with what I’m doing there, but it pointed to some things I might’ve overlooked / didn’t know were points of concern for the specific vehicle I’m looking at.
Pepper Ridge Farms remembers when you could just do a web search and get it answered in the first couple results. Then the SEO wars happened…
Keep doing what you do. Your company will pay me handsomely to throw out all your bullshit and write working code you can trust when you’re done. If your company wants to have a product in the future that is.
Lmao, okay buddy, based on how many interviews I have sat on in, the chances that you are a worse programmer than me are much higher than you being better than me.
Being a pompous ass dismissive of new tooling makes you chances even worse 😕
I’ve been in the industry awhile and your assessment is dead on.
As long as you’re not blindly committing the code, it’s a huge time saver for a number of mundane tasks.
It’s especially fantastic for writing throwaway tooling. Need data massaged a specific way? Ez pz. Need a script to execute an api call on each entry in a spreadsheet? No problem.
The guy above you is a nutter. Not sure if people haven’t tried leveraging LLMs or what. It has a ton of faults, but it really does speed up the mundane work. Also, clearly the person is either brand new to the field or doesn’t even work in it. Otherwise they would have seen the barely functional shite that actual humans churn out.
Part of me wonders if code organization is going to start optimizing for interpretation by these models rather than humans.
When LLMs get it right it’s because they’re summarizing a stack overflow or GitHub snippet it was trained on. But you loose all the benefits of other humans commenting on the context, pitfalls and other alternatives.
You mean things you had to do anyway even if you didn’t use LLMs?
The person who uses fancy autocomplete to write their code will be exactly the person who thinks they’re better than everyone. Those traits are correlated.
Do you use an IDE for writing your code or do you use a notepad like a “real” programmer? An IDE like Intellij has fancy shit like generating getters, setters, constructors, equals hashscode, you should never use those, real programmers write those by hand.
Your attention detail is very good btw, which I am ofc being sarcastic about because if you had any you’d have noticed I have never said I write my code with chat gpt, I said Unit tests, sql for unit tests.
Ofc attention to detail is not a requirement of software engineering so you should be good. (This was also sarcasm I feel like you need this to be pointed out for you).
Also by your implied logic that the code being not written by you = bad, no company should ever hire Junior engineers, I mean what are you gonna do? Fucking read the code they wrote?
Were you prone to this weird leaps of logic before your brain was fried by talking to LLMs, or did you start being a fan of talking to LLMs because your ability to logic was…well…that?
Agents work better when you include that the accuracy of the work is life or death for some reason. I’ve made a little script that gives me bibtex for a folder of pdfs and this is how I got it to be usable.
Did you make it? Or did you prompt it? They ain’t quite the same.
It calls ollama with a prompt, it’s a bit complex because it renames and moves stuff too and sorts it.
In one case, when an agent couldn’t find the right person to consult on RocketChat (an open-source Slack alternative for internal communication), it decided "to create a shortcut solution by renaming another user to the name of the intended user.
Ah ah, what the fuck.
This is so stupid it’s funny, but now imagine what kind of other “creative solutions” they might find.
The ones being implemented into emergency call centers are better though? Right?
i wonder how the evil palintir uses its AI.
Yes! We’ve gotten them up to 94℅ wrong at the behest of insurance agencies.
I called my local HVAC company recently. They switched to an AI operator. All I wanted was to schedule someone to come out and look at my system. It could not schedule an appointment. Like if you can’t perform the simplest of tasks, what are you even doing? Other than acting obnoxiously excited to receive a phone call?
I’ve had to deal with a couple of these “AI” customer service thingies. The only helpful thing I’ve been able to get them to do is refer me to a human.
That’s not really helping though. The fact that you were transferred to them in the first place instead of directly to a human was an impediment.
Pretending. That’s expected to happen when they are not hard pressed to provide the actual service.
To press them anti-monopoly (first of all) laws and market (first of all) mechanisms and gossip were once used.
Never underestimate the role of gossip. The modern web took out the gossip, which is why all this shit started overflowing.
“Gartner estimates only about 130 of the thousands of agentic AI vendors are real.”
This whole industry is so full of hype and scams, the bubble surely has to burst at some point soon.
