Specifically I was researching a lesser known kafka-mqtt connector: https://docs.lenses.io/latest/connectors/kafka-connectors/si..., and o1 was hallucinating the configuration needed to support dynamic topics. The docs said one thing, and I even mentioned it to o1 that the docs contradicted with it. But it would stick to its guns. If I mentioned that the code wouldn't compile it would start suggesting very implausible scenarios -- did you spell this correctly? Responses like that indicate you've reached a dead end. I'm curious how/if the "structured LLM interactions" you mention overcome this.
It sucks, but the trick is to always restart the conversations/chat with a new message. I never go beyond one reply, and also copy-paste a bunch. Got tired of copy-pasting, wrote something like a prompting manager (https://github.com/victorb/prompta) to make it easier, and not having to neatly format code blocks and so on.
Basically make one message, if they get the reply wrong, iterate on the prompt itself and start fresh, always. Don't try to correct by adding another message, but update initial prompt to make it clearer/steer more.
But I've noticed that every model degrades really quickly past the initial reply, no matter what length of each individual message. The companies seem to continue to increase the theoretical and practical context limits, but the quality degrades a lot faster even within the context limits, and they don't seem to try to address that (nor have a way of measuring it).
LLMs are so incredibly transformative when they're incredibly transformative. And when they aren't it's much better to fall back on the years of hard won experience I have - the sooner the better. For example I'll switch between projects and languages and even with explicit instruction to move to a strongly typed language they'll stick to dynamic answers. It's an odd experience to re-find my skills every once in a while. "Oh yeah, I'm pretty good at reading docs myself".
With all the incredible leaps in LLMs being reported (especially here on HN) I really haven't seen much of a difference in quite a while.
I’m not affiliated with aider. I just use it.
I have a bet that many of the pitfalls people experience at the moment are due to mismatched tools or immature tools.
Also special care has to be given to the number of tokens. Even with one-question/one-answer, 5 hundred to 1 thousand tokens can be focused at once by our artificial overlords. After that they start losing their marbles. There are exceptions to that rule with the reasoning models, but in essence they are not that different.
The difference of using the tool correctly versus not, might be that instead of getting 99.9% accuracy, the user gets just 98%. Probably that doesn't sound that big of a difference to some people. The difference is that it works 10 times better in the first case.
Other tasks can be more forgiving, like writing which i do all the time, then i load 3000 tokens in the context window pretty frequently. Small details in the accuracy do not matter so much for most people, for everyday casual tasks like rewriting text, summarizing etc.
In general, be wary of how much context you load into the chat, performance degrades faster than you can imagine. Ok, the aphorism i started with, was a little simplistic.
“Okay, now add another column which is the price of the VM based on current Azure costs and the CPU and Memory requirements listed.”
“This seems to only use a few Azure VM SKU. Use the full list.”
“Can you remove the burstable SKU?”
Though I will say simple error fixes with context windows with programming issues are resolved fine. On more than one occasion when I copy and paste an incorrect solution. Providing the LLM with the error is enough to fix the problem. But if it goes beyond that, it’s best to abandon the context.
And how funny that you comment seems to land perfectly together with this about people having very different experiences with using LLMs:
> I am still trying to sort out why experiences are so divergent. I've had much more positive LLM experiences while coding than many other people seem to, even as someone who's deeply skeptical of what's being promised about them. I don't know how to reconcile the two.
No, the pretend above 95% accuracy is not as good as the up to 50% rates of hallucinations reported by OpenAI for example.
The difference in experiences is easily explainable in my opinion. Much like some people swear by mediums and psychics and other easily see through it: it's easy to see what you want to see when a nearly random experience lands you a good outcome.
I don't appreciate your insinuation that I am making up numbers and I though it shouldn't go unanswered but do not mistake this for a conversation. I am not in the habit of engaging with such demeaning language.
It is "Gratuitous rudeness" to say these numbers without any sort of sourcing/backing are pulled from someone's ass? Then I guess so be it, but I'm also not a fan of people speaking about absolute numbers as some sort of truth, when there isn't any clear way of coming up with those numbers in the first place.
Just like there are "extremists" claiming LLMs will save us all, clearly others fall on the other extreme and it's impossible to have a somewhat balanced conversation with either of these two groups.
When a problem is far outside of my understanding, A.I. leads me towards a wrong path more often than not. Accuracy is terrible, because i don't know how to decompose the problem.
Jargon plays a crucial role there. LLM's need to guided using as much correct jargon of the problem as possible.
I have done this for decades on people. I read a book at some point that the most sure way for people to like you, is to speak to them in words they usually use themselves. No matter the concepts they are hearing with their ears, if the words belong belong in their familiar vocabulary they are more than happy to discuss anything.
So when i meet someone, i always try to absorb as much of their vocabulary as possible, as quickly as possible, and then i use it to describe ideas i am interested in. People understand much better like that.
Anyway, the same holds true for LLM's, they need to hear the words of the problem, expressed in that particular jargon. So when a programmer wants to use a library, he needs to absorb the jargon used in that particular library. It is only then that accuracy rates hit many nines.
I also noticed language matters a lot as well. It's pretty good with Python, pandas, matplotlib, etc. But ask it to write some PowerShell and it regularly hallucinates modules that don't exist, more than any other language I've tried to use it with.
And good luck if you're working with a stack that's not flavor of the month with plenty of online information available. ERP systems with its documentation living behind a paywall, so it's not in the training data - you know, real world enterprise CRUD use cases where I'd want to use it the most, it's the least helpful.
Still, at most I get it to help me with snippets. I wouldn't want it to just generate lots of code, for one it's pretty easy to write Elixir...
It really works, for me. It iterates by itself and fixes the problem.
rm -r .aider.chat.history.md .aider.tags.cache.v4 || true && aider --architect --model deepseek/deepseek-reasoner --editor-model deepseek/deepseek-chat --no-restore-chat-history
A stage every developer goes through early in their development.
It initially suggested I use the function as part of a solution, suggesting it was part of the base library and could be imported as such. When I told it that function didn't exist within the library it got obstinate and argued back and forth with me to the point where it told me it couldn't help me with that issue anymore but would love to help me with other things. It was surprisingly insistent that I must be importing the wrong library version or doing something else wrong.
When I got rid of that chat's context and asked it about the existence of that function more directly without the LLM first suggesting its use to me, it replied correctly that the function doesn't exist in the library but the concept being easy to implement... the joys(?) of using an LLM and having it go in wildly different directions depending upon the starting point.
I'm used to the opposite situation where an LLM will slide into sycophantic agreeable hallucinations so it was in a way kind of refreshing for Gemini to not do this, but on the other hand for it to be so confidently and provably wrong (while also standing its ground on its wrongness) got me unreasonably pissed off at it in a way that I don't experience when an LLM is wrong in the other direction.
Also, the recurring theme of "just wipe out context and re-start" places a hard ceiling on how complex an issue the LLM can be useful for.
It's nice when the LLM outputs bullshit, which is frequent.
I have to chuckle at that because it reminds me of a typical response on technical forums long before LLMs were invented.
Maybe the LLM has actually learned from those responses and is imitating them.
Humans not being infallible superintelligences does not mean that the thing that LLMs are doing is the same thing we do when we think, create, reason, etc. I would like to imagine that most serious people who use LLMs know this, but sometimes it's hard to be sure.
Is there a name for the "humans stupid --> LLMs smart" fallacy?
No one is saying "humans stupid --> LLMs smart". That's absolutely not the commenter above you said. Your whole comment is strawman fallacy.
This is 100% true, isn't it? It is based on the corpus of humankind knowledge and interaction, it is only expected that it would "repeat" human patterns. It also makes sense that the way to evolve the results we get from it is to mimic human organization, politics, sociology in the a new layer on top of LLMs to surpass current bottlenecks, just like they were used to evolve human societies.
Something being based on X or using it as source material doesen't guarantee any kind of similarity though. My program can also contains the entire text of wikipedia, and only ever outputs the number 5.
Assuming the text of wikipedia is meaningfully used in the program, of course. A definition of "meaningful" I will propose is code which survives an optimization loop into the final resulting machine code and isn't hidden behind some arbitrary conditional. That seems reasonable as a definition of a program "containing" something.
Nah, it's something else: it's that LLMs are being held to a higher standard than humans. Humans are fallible, and that's okay. The work they do is still useful. LLMs do not have to be perfect either to be useful.
The question of how good they are absolutely matters. But some error isn't immediately disqualifying.
Yes, humans and LLMs are fallible, and both useful.
I'm not saying the comment I responded was an egregious case of the "fallacy" I'm wondering about, but I am saying that I feel like it's brewing. I imagine you've seen the argument that goes:
Anne: LLMs are human-like in some real, serious, scientific sense (they do some subset of reasoning, thinking, creating, and it's not just similar, it is intelligence)
Billy: No they aren't, look at XYZ (examples of "non-intelligence", according to the commenter).
Anne: Aha! Now we have you! I know humans who do XYZ! QED
I don't like Billy's argument and don't make it myself, but the rejoinder which I feel we're seeing often from Anne here seems absurd, no?
It is sad that, inadvertendly or not, LLMs may have picked up on the traits of the loudest humans. abrasive, never admitting fault, always trying to bring something up that sounds plausible but falls under scrutiny. Only thing it holds back on is resorting to insults when cornered.
Even with all the coding agent magik people harp on about, I've never seen something that can write clean good quality code reliably. I'd prefer to tell an LLM what a functions purpose is, what kind of information and data structures it can expect and what it should output, see what it produces, provide feedback, and get a rather workable often perfect function in return.
If i get it to write the whole thing in one go, I cannot imagine the pain of having to find out where the fuckery is that slows everything down, without diving deep with profilers etc. all for a problem I could have solved by just playing middle man and keeping a close eye on how things are building up and being in charge of ensuring the overarching vision is achieved as required.
Because it’s directly editing code in the IDE instead of me transferring sections of code from a chat window the large amount of bad code it writes it much more apparent.
Question is would you rather find out it got stuck in a loop with 3 minutes with a coding agent or 40 minutes copy pasting. It can also get out of loops more often by being able to use tools to look up definitions with grep, ctags or language server tools, though you can copy paste commands for that too it will be much slower.
> On paper, coding agents should be able to address my complaints with LLM-generated code reliability since it inherently double-checks itself and it’s able to incorporate the context of an entire code project. However, I have also heard the horror stories of people spending hundreds of dollars by accident and not get anything that solves their coding problems. There’s a fine line between experimenting with code generation and gambling with code generation.
I think if you are going to claim you have an opinion based on experience you should probably, at the least, experience the thing you are trying to state your opinion on. It's probably not enough to imagine the experience you would have and then go with that.
I see people getting LLMs to generate code in isolation and like pasting it into a text editor and trying it, and then getting frustrated, and it's like, that's not how you're supposed to be doing it anymore. That's 2024 praxis.
Mostly joke, but also not joke: https://news.smol.ai/
Easy to to script/autogenerate code and build out pipelines this way
The more prominent and widely used a language/library/framework, and the more "common" what you are attempting, the more accurate LLMs tends to be. The more you deviate from mainstream paths, the more you will hit such problems.
Which is why I find them them most useful to help me build things when I am very familiar with the subject matter, because at that point I can quickly spot misconceptions, errors, bugs, etc.
It's when it hits the sweet spot of being a productivity tool, really improving the speed with which I write code (and sometimes improving the quality of what I write, for sometimes incorporating good practices I was unaware of).
One very interesting variant of this: I've been experimenting with LLMs in a react-router based project. There's an interesting development history where there's another project called Remix, and later versions of react-router effectively ate it, that is, in December of last year, react-router 7 is effectively also Remix v3 https://remix.run/blog/merging-remix-and-react-router
Sometimes, the LLM will be like "oh, I didn't realize you were using remix" and start importing from it, when I in fact want the same imports, but from react-router.
All of this happened so recently, it doesn't surprise me that it's a bit wonky at this, but it's also kind of amusing.
For example, I don't like ORMs. There are reasons which aren't super important but I tend to prefer SQL directly or a simple query builder pattern. But I did a chain of messages with LLMs asking which would be better for LLM based development. The LLM made a compelling case as to why an ORM with a schema that generated a typed client would be better if I expected LLM coding agents to write a significant amount of the business logic that accessed the DB.
My dislike of ORMs is something I hold lightly. If I was writing 100% of the code myself then I would have breezed past that decision. But with the agentic code assistants as my partners, I can make decisions that make their job easier from their point of view.
The issue you are addressing refers specifically to Python, which is not compiled... Are you referring to this workflow in another language, or by "compile" do you mean something else, such as using static checkers or tests?
Also, what tooling do you use to implement this workflow? Cursor, aider, something else?
In the context we are talking about (hallucinating Polars methods), if I'm not mistaken the compilation step won't catch that, Python will actually throw the error at runtime post-compilation.
So my question still stands on what OP means by "won't compile".
Python is AOT compiled to bytecode, but if a compiled version of a module is not available when needed it will be compiled and the compiled version saved for next use. In the normal usage pattern, this is mostly invisible to the user except in first vs. subsequent run startup speed, unless you check the file system and see all the .pyc compilation artifacts.
You can do AOT compilation to bytecode outside of a compile-as-needed-then-execute cycle, but there is rarely a good reason to do so explicitly for the average user (the main use case is on package installation, but that's usually handled by package manager settings).
But, relevant to the specific issue here, a hallucinated function would lead to a runtime failure not a compilation failure, since function calls aren't resolved at compile time, but by lookup by name at runtime.
It's not a compilation error but it does feel like one, somewhat. It happens at more or less the same time.
from foo import nonexistent
import foo
foo.nonexistent()
A lot of the time with Python you will be importing classes so it will be mostly run time errors.
Similarly Claude 3.5 was stuck on TensorRT 8, and not even pointing it at the documentation for the updated 10 APIs for RAG could ever get it to correctly use the new APIs (not that they were very complex; bind tensors, execute, retrieve results). The whole concept of the self-reinforcing Agent loop is more of a fantasy. I think someone else likened it to a lawnmower that will run rampage over your flower bed at the first hiccup.
I don't know about "self-reinforcing". I'm just saying: coding agents compile and lint the code they're running, and when they hallucinate interfaces, they notice. The same way any developer who has ever used ChatGPT knows that you can paste most errors into the web page and it will often (maybe even usually) come up with an apposite fix. I don't understand how anybody expects to convince LLM users this doesn't work; it obviously does work.
This is really one of the hugest divides I've seen in the discourse about this: anti-LLM people saying very obviously untrue things, which is uh, kind of hilarious in a meta way.
https://bsky.app/profile/caseynewton.bsky.social/post/3lo4td... is an instance of this from a few days ago.
I am still trying to sort out why experiences are so divergent. I've had much more positive LLM experiences while coding than many other people seem to, even as someone who's deeply skeptical of what's being promised about them. I don't know how to reconcile the two.
As with many topics, I feel like you can divide people in a couple of groups. You have people who try it, have their mind blown by it, so they over-hype it. Then the polar-opposite, people who are overly dismissive and cement themselves into a really defensive position. Both groups are relatively annoying, inaccurate, and too extremist. Then another group of people might try it out, find some value, integrate it somewhat and maybe got a little productivity-boost and moves on with their day. Then a bunch of other groupings in-between.
Problem is that the people in the middle tend to not make a lot of noise about it, and the extremists (on both ends) tend to be very vocal about their preference, in their ways. So you end up perceiving something as very polarizing. There are many accurate and true drawbacks with LLMs as well, but it also ends up poisoning the entire concept/conversation/ecosystem for some people, and they tend to be noisy as well.
Then the whole experience depends a lot on your setup, how you use it, what you expect, what you've learned and so many much more, and some folks are very quick to judge a whole ecosystem without giving parts of it an honest try. It took me a long time to try Aider, Cursor and others, and even now after I've tried them out, I feel like there are probably better ways to use this new category of tooling we have available.
In the end I think reality is a bit less black/white for most folks, common sentiment I see and hear is that LLMs are probably not hellfire ending humanity nor is it digital-Jesus coming to save us all.
This is probably a big chunk of it. I was pretty anti-LLM until recently, when I joked that I wanted to become an informed hater, so I spent some more time with things. It's put me significantly more in the middle than either extremely pro or extremely anti. It's also hard to talk about anything that's not purely anti in the spaces I seemingly run in, so that also contributes to my relative quiet about it. I'm sure others are in a similar boat.
> for most folks, common sentiment I see and hear is that LLMs are probably not hellfire ending humanity nor is it digital-Jesus coming to save us all.
Especially around non-programmers, this is the vibe I get as well. They also tend to see the inaccuracies as much less significant than programmers seem to, that is, they assume they're checking the output already, or see it as a starting point, or that humans also make mistakes, and so don't get so immediately "this is useless" about it.
Bro I've been using LLMs for search since before it even had search capabilities...
"LLMs not being for search" has been an argument from the naysayers for a while now, but very often when I use an LLM I am looking for the answer to something - if that isn't [information] search, then what is?
Whether they hallucinate or outright bullshit sometimes is immaterial. For many information retrieval tasks they are infinitely better than Google and have been since GPT3.
That might sound snarky, but it probably works out for people in 99% of cases. AI and LLMs are advancing at a pace that is so different from any other technology that people aren't yet trained to re-evaluate their assumptions at the high rate necessary to form accurate new opinions. There are too many tools coming (and going, to be fair).
HN (and certain parts of other social media) is a bubble of early adopters. We're on the front lines seeing the war in realtime and shaking our heads at what's being reported in the papers back home.
But at the same time, the pace of advancement is very fast, and so not having recently re-evaluated things is significantly more likely while also being more charitable, I think.
If someone is embracing uncertainty or expending the time/energy/money to reevaluate then they don't post such confidently wrong ideas on social media.
> https://bsky.app/profile/caseynewton.bsky.social/post/3lo4td... is an instance of this from a few days ago.
Not sure why this is so surprising? ChatGPT search was only released in November last year, was a different mode, and it sucked. Search in o3 and o4-mini came out like three weeks ago. Otherwise you were using completely different products from Perplexity or Kagi, which aren't widespread yet.
Casey Newton even half acknowledges that timing ("But it has had integrated web search since last year"...even while in the next comment criticising criticisms using the things "you half-remember from when ChatGPT launched in 2022").
If you give the original poster the benefit of the doubt, you can sort of see what they're saying, too. An LLM, on its own, is not a search engine and can not scan the web for information. The information encoded in them might be ok, but is not complete, and does not encompass the full body of the published human thought it was trained on. Trusting an offline LLM with an informational search is sometimes a really bad idea ("who are all the presidents that did X").
The fact that they're incorrect when they say that LLM's can't trigger search doesn't seem that "hilarious" to me, at least. The OP post maybe should have been less strident, but it also seems like a really bad idea to gatekeep anybody wanting to weigh in on something if their knowledge of product roadmaps is more than six months out of date (which I guarantee is all of us for at least some subject we are invested in).
It is entirely possible that I simply got involved at a particular moment that was crazy lucky: it's only been a couple of weeks. I don't closely keep up with when things are released, I had just asked ChatGPT something where it did a web search, and then immediately read a "it cannot do search" claim right after.
> An LLM, on its own, is not a search engine and can not scan the web for information.
In a narrow sense, this is true, but that's not the claim: the claim is "You cannot use it as a search engine, or as a substitute for searching." That is pretty demonstrably incorrect, given that many people use it as such.
> Trusting an offline LLM with an informational search is sometimes a really bad idea ("who are all the presidents that did X").
I fully agree with this, but it's also the case with search engines. They also do not always "encompass the fully body of the published human thought" either, or always provide answers that are comprehensible.
I recently was looking for examples of accomplishing things with a certain software architecture. I did a bunch of searches, which led me to a bunch of StackOverflow and blog posts. Virtually all of those posts gave vague examples which did not really answer my question with anything other than platitudes. I decided to ask ChatGPT about it instead. It was able to not only answer my question in depth, but provide specific examples, tailored to my questions, which the previous hours of reading search results had not afforded me. I was further able to interrogate it about various tradeoffs. It was legitimately more useful than a search engine.
Of course, sometimes it is not that good, and a web search wins. That's fine too. But suggesting that it's never useful for a task is just contrary to my actual experience.
> The fact that they're incorrect when they say that LLM's can't trigger search doesn't seem that "hilarious" to me, at least.
It's not them, it's the overall state of the discourse. I find it ironic that the fallibility of LLMs is used to suggest they're worthless compared to a human, when humans are also fallible. OP did not directly say this, but others often do, and it's the combination that's amusing to me.
It's also frustrating to me, because it feels impossible to have reasonable discussions about this topic. It's full of enthusiastic cheerleaders that misrepresent what these things can do, and enthusiastic haters that misrepresent what these things can do. My own feelings are all over the map here, but it feels impossible to have reasonable discussions about it due to the polarization, and I find that frustrating.
I think the other reason people are hostile to the field is that they're scared it's going to make them economically redundant, because a tsunami of cheap, skilled labor is now towering over us. It's loss-aversion bias, basically. Many people are more focused on that risk than on the amazing things we're able to do with all that labor.
tptacek shifted the goal posts from "correct a hallucination" to "solve a copy pasted error" (very different things!) and just a comment later theres someone assassinating me as an "anti-LLM person" saying "very obviously untrue things", "kind of hilarious". And you call yourself "charitable". It's a joke.
> theres someone assassinating me as an "anti-LLM person"
Is this not true? That's the vibe the comment gives off. I'm happy to not say that in the future if that's not correct, and if so, additionally, I apologize.
I myself was pretty anti-LLM until the last month or so. My opinions have shifted recently, and I've been trying to sort through my feelings about it. I'm not entirely enthusiastically pro, and have some pretty big reservations myself, but I'm more in the middle than where I was previously, which was firmly anti.
> "very obviously untrue things"
At the time I saw the post, I had just tabbed away from a ChatGPT session where it had relied on searching the web for some info, so the contrast was very stark.
> "kind of hilarious"
I do think that when people say that LLMs occasionally hallucinate things, and are therefore worthless, when others make false claims about them for the purpose of suggesting we shouldn't use them, that it is kind of funny. You didn't directly say this in your post, only handwaved towards it, but I'm talking about the discourse in general, not you specifically.
> And you call yourself "charitable"
I am trying to be charitable. A lot of people reached for some variant of "this person is stupid," and I do not think that's the case, or the good way to understand what people mean when they say things. A mistake is a mistake. I am actively not trying to simply dismiss arguments on either side of here, but take them seriously.
I googled "how do I use ai with VS: Code" and it pointed me at Cline. I've then swapped between their various backends, and just played around with it. I'm still far too new to this to have strong options about LLM/agent pairs, or even largely between which LLMs, other than "the free ChatGPT agent was far worse than the $20/month one at the task I threw it at." As in, choosing worse algorithms that are less idiomatic for the exact same task.
...Maybe a good first LLM assisted project.
https://aider.chat/docs/leaderboards/
Regarding choosing a tool, they're pretty lightweight to try and they're all converging in structure and capabilities anyway.
Finally, if it really were really true that some people know the special sauce of how to use LLMs to make a massive difference in productivity but many people didn't know how to do that then you could make millions or tens of millions per year as a consultant training everyone at big companies. In other words if you really believed what you were saying you should pick up the money on the ground.
This might be a good explanation for the disconnect!
> I would point that LLM boosters have been saying the same thing
I certainly 100% agree that lots of LLM boosters are way over-selling what they can accomplish as well.
> In other words if you really believed what you were saying you should pick up the money on the ground.
I mean, I'm doing that in the sense that I am using them. I also am not saying that I "know the special sauce of how to use LLMs to make a massive difference in productivity," but what I will say is, my productivity is genuinely higher with LLM assistance than without. I don't necessarily believe that means it's replicable, one of the things I'm curious about it "is it something special about my setup or what I'm doing or the technologies I'm using or anything else that makes me have a good time with this stuff when other smart people seem to only have a bad time?" Because I don't think that the detractors are just lying. But there is a clear disconnect, and I don't know why.
> I mean, I'm doing that in the sense that I am using them.
My point is whatever you are doing is worth millions of dollars less than teaching the non-believers how to do it if you could figure out how (actually probably even if you couldn't but sold snake-oil).
I am also trying to sort this out, but I'm probably someone you'd consider to be on the other, "anti-LLM" side.
I wonder if part of this is simply level of patience, or, similarly, having a work environment that's chill enough to allow for experimentation?
From my admittedly short attempts to use agentic coding so far, I usually give up pretty quickly because I experience, as others in the thread described, the agent just spinning its wheels or going off and mangling the codebase like a lawnmower.
Now, I could totally see a scenario where if I spent time tweaking prompts, writing rule files, and experimenting with different models, I could improve that output significantly. But this is being sold to me as a productivity tool. I've got code to write, and I'm pretty sure I can write it fairly quickly myself, and I simply don't have time at my start up to muck around with babysitting an AI all day -- I have human junior engineers that need babysitting.
I feel like I need to be a lot more inspired that the current models can actually improve my productivity in order to spend the time required to get there. Maybe that's a chicken-or-egg problem, but that's how it is.
I think this is it. Some people are willing to invest the time in writing natural language code for the LLM.
> I spent time tweaking prompts, writing rule files, and experimenting with different models, I could improve that output significantly. But this is being sold to me as a productivity tool. I've got code to write, and I'm pretty sure I can write it fairly quickly myself, and I simply don't have time at my start up to muck around with babysitting an AI all day -- I have human junior engineers that need babysitting.
I agree and this is the divide I think: skeptical people think this is a flimsy patch that will eventually collapse. I for one can't see how trying to maintain ever growing files in natural language won't lead to a huge cognitive load quite soon and I bet we're about to hear people discussing how to use LLMs to do that.
I think if you're trying stuff, you're not, otherwise, you wouldn't even use them. What I'd say is more that you're having a bad time, whereas I'm not.
> I wonder if part of this is simply level of patience, or, similarly, having a work environment that's chill enough to allow for experimentation?
Maybe? I don't feel like I've had to had a ton of patience. But maybe I'm just discounting that, or chiller or something, as you allude to.
I suspect part of it is that there is little to no established social context for how to interact with an LLM, and best practices are still being actively discovered at least compared to tools like search engines or word processors.
Search engines somewhat have this problem, but there's some social context around search engine skill, colloquially "google-fu", if it's even explicitly mentioned.
At some point, being able to get the results from a search engine stopped being entirely about the quality of the engine and instead became more about the skill of the user.
I imagine, as the UX for AI systems stabilizes, and as knowledge of the "right way", to use them diffuses through culture, experiences will be less divergent.
Likely, but I think another big reason for diverging experience is that natural language is ambiguous and human conversation leaves out a lot of explicit details because it can be inferred or assumed when using natural language.
I can't speak for others, but it's difficult for me to describe programming ideas and concepts using natural language - but, that's why we have programming languages for this. A language that is limited in vocabulary, and explicit in conveying your meaning.
Natural language is anything but, and it can be difficult to be exact. You can instinctively leave out all kinds of details using natural language, whereas leaving out those details in a programming language would cause a compiler error.
I've never really understood the push toward programming with natural language, even before LLMs. It's just not a good fit. And much like how you an pass specific parameters into Google, I think we'll end up getting to a place where LLMs have their own DSL for prompting to make it easier to get the result you want.
And when that happens I review the code and if it is bad then I "git revert". And if it is 90% of the way there I fix it up and move on.
The question shouldn't be "are they infallible tools of perfection". It should be "do I get value equal to or greater than the time/money I spend". And if you use git appropriately you lose at most five minutes on a agent looping. And that happens a couple of times a week.
And be honest with yourself, is getting stuck in a loop fighting a compiler, type-checker or lint something you have ever experienced in your pre-LLM days?
This reminds me of a scene from the recent animation movie "Wallace and Gromit: Vengeance Most Fowl" where Wallace actually uses a robot (Norbot) to do gardening tasks, and rampages over Gromit's flower bed.
I’m getting massive productivity gains with Cursor and Gemini 2.5 or Claude 3.7.
One-shotting whole features into my rust codebase.
I tried it again just now with Claude 3.7 in Cursors Agent/Compose (they change this stuff weekly). Write a simple C++ TensorRT app that loads an engine and runs inference 100 times for a benchmark, use this file to source a toolchain. It generated code with the old API & a CMake file and (warning light turns on) a build script. The compile fails because of the old API, but this time it managed to fix it to use the new API.
But now the linking fails, because it overwrote the TRT/CUDA directories in the CMakeLists with some home cooked logic (there was nothing to do, the toolchain script sets up the environment fully and just find_package would work).
And this is where we go off the rails; it messes with the build script and CMakeLists more, but still it can not link. It thinks hey it looks like we are cross-compiling and creates a second build script "cross-compile.sh" that tries to use the compiler directly, but of course that misses things that the find_package in CMake would setup and so fails with include errors.
It pretends its a 1970 ./configure script and creates source files "test_nvinfer.cpp" and "test_cudart.cpp" that are supposed to test for the presence of those libraries, then tries to compile them directly; again its missing directories and obviously fails.
Next we create a mashup build script "cross-compile-direct.sh". Not sure anymore what this one tried to achieve, didn't work.
Finally, and this is my favorite agent action yet, it decides fuck it, if the library won't link, why don't we just mock out all the actual TensorRT/CUDA functionality and print fake benchmark numbers to demonstrate LLMs can average a number in C++. So it writes, builds ands runs a "benchmark_mock.cpp" that subs out all the useful functionality for random data from std::mt19937. This naturally works, so the agent declares success and happily updates the README.md with all the crap it added and stops.
This is what running the lawnmower over the flower bed means; you have 5 more useless source files and a bunch more shell scripts and a bunch of crap in a README that were all generated to try and fail to fix a problem it could not figure out, and this loop can keep going and generate more nonsense ad infinitum.
(Why could it not figure out the linking error? We come back to the shitty bolted on integrations; it doesn't actually query the environment, search for files or look at what link directories are being used, as one would investigating a linking error. It could of course, but the balance in these integrations is 99% LLM and 1% tool use, and even context from the tool use often doesn't help)
The tools will get better, but what I see happening with people who are good at using them (and from my own code, even in my degraded LLM usage), we have an existence proof of the value of the tools.
I really dislike libraries that have their own bespoke ways of doing things for no especially good reason. Don’t try to be cute. I don’t want to remember your specific API, I want an intuitive API so I spend less time looking up syntax and more time solving the actual problem.
The more LLMs are entrenched and required, the less we're able to do The Right Thing in the future. Time will be frozen, and we'll be stuck with the current mean forever. LLMs are notoriously bad at understanding anything that isn't mappable in some way to pre-existing constructs.
> for no especially good reason
That's a major qualifier.
https://docs.pola.rs/api/python/stable/reference/
I would say that this is pretty good approach to combat the previous.
Funnily enough I was trying to deal with some lesser used parts of pandas via LLM and it kept sending me back through a deprecated function for everything. It was quite frustrating.
This describes the simplest, and most benign case of code assistents messing up. This isn't the problem.
The problem is when the code does compile, but contains logical errors, security f_ckups, performance dragdowns, or missed functionality. Because none of those will be caught by something as obvious as a compiler error.
And no, "let the AI write tests" wont catch them either, because that's not a solution, that's just kicking the can dwn the road...because if we cannot trust the AI to write correct code, why would we assume that it can write correct tests for that code?
What will ultimately catch those, is the poor sod in the data center, who, at 03:00 AM has to ring the on-call engineer out of his bed, because the production server went SNAFU.
And when the oncall then has to rely on "AI" to fix the mess, because he didn't actually write the code himself, and really doesn't even know the codebase any more (or even worse: Doesn't even understand the libraries and language used at all, because he is completely reliant on the LLM doing that for him), companies, and their customers, will be in real trouble. It will be the IT equivalent of attorneys showing up in court with papers containing case references that were hallucinated by some LLM.
How does this even make sense when the "agent" is generating Python. There are several ways it can generate code that runs and even does the thing and still has severe issues.
Although pandas is the standard for manipulating tabular data in Python and has been around since 2008, I’ve been using the relatively new polars library exclusively, and I’ve noticed that LLMs tend to hallucinate polars functions as if they were pandas functions which requires documentation deep dives to confirm which became annoying.
The post does later touch on coding agents (Max doesn't use them because "they're distracting", which, as a person who can't even stand autocomplete, is a position I'm sympathetic to), but still: coding agents solve the core problem he just described. "Raw" LLMs set loose on coding tasks throwing code onto a blank page hallucinate stuff. But agenty LLM configurations aren't just the LLM; they're also code that structures the LLM interactions. When the LLM behind a coding agent hallucinates a function, the program doesn't compile, the agent notices it, and the LLM iterates. You don't even notice it's happening unless you're watching very carefully.