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There's a thru-line to commentary from experienced programmers on working with LLMs, and it's confusing to me:

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.

So in my interactions with gpt, o3 and o4 mini, I am the organic middle man that copy and pastes code into the repl and reports on the output back to gpt if anything should be the problem. And for me, past a certain point, even if you continually report back problems it doesn't get any better in its new suggestions. It will just spin its wheels. So for that reason I'm a little skeptical about the value of automating this process. Maybe the llms you are using are better than the ones I tried this with?

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.

> And for me, past a certain point, even if you continually report back problems it doesn't get any better in its new suggestions. It will just spin its wheels. So for that reason I'm a little skeptical about the value of automating this process.

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).

In other words don't use the context window. Treat it as a command line with input/output, in which the purpose of the command is to extract information signal or knowledge manipulation or data mining and so on.

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.

I think “don’t use the context window” might be too simple. It can be incredibly useful. But avoid getting in a context window loop. When iterations stop showing useful progress toward the goal, it’s time to abandon the context. LLMs tend to circle back to the same dead end solution path at some point. It also helps to jump between LLMs to get a feel for how they perform on different problem spaces.

This is my experience as well, as has been for over a year now.

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 refused to stop being a middle man, because I can often catch really bad implementation early and can course correct. E.g. a function which solves a problem with a series of nested loops that can be done several orders of magnitude faster by using vectorised operations offered by common packages like numpy.

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.

> If I mentioned that the code wouldn't compile it would start suggesting very implausible scenarios

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.

It seems no discussion of LLMs on HN these days is complete without a commenter wryly observing how that one specific issue someone is pointing to with an LLM is also, funnily enough, an issue they've seen with humans. The implication always seems to be that this somehow bolsters the idea that LLMs are therefore in some sense and to some degree human-like.

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?

> 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.

> The implication always seems to be that this somehow bolsters the idea that LLMs are therefore in some sense and to some degree human-like.

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.

I think it's natural for programmers to hold LLMs to a higher standard, because we're used to software being deterministic, and we aim to make it reliable.

> the idea that LLMs are therefore in some sense and to some degree human-like.

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.

>It is based on the corpus of humankind knowledge and interaction

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.

You can have agent search the web for documentation and then provide it to the LLM. That is how Context7 is currently very popular in the AI user crowd.

I used o4 to generate nixos config files from the pasted modules source files. At first it did outdated config stuff, but with context files it worked very good.

Kagi Assistant can do this too but I find it's mostly useful because the traditional search function can find the pages the LLM loaded into its context before it started to output bullshit.

It's nice when the LLM outputs bullshit, which is frequent.

I wonder if LLMs have been seen claiming “THERE’S A BUG IN THE COMPILER!”

A stage every developer goes through early in their development.

Gemini 2.5 got into as close to a heated argument with me as possible about the existence of a function in the kotlin coroutines library that was never part of the library (but does exist as a 5 year old PR still visible in github that was never merged in).

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.

He does partially address this elsewhere in the blog post. It seems that he's mostly concerned about surprise costs:

> 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.

Less surprise costs, more wasting money and not getting proportionate value out of it.

For several moments in the article I had to struggle to continue. He is literally saying "as an experienced LLM user I have no experience with the latest tools". He gives a rationale as to why he hasn't used the latest tools which is basically that he doesn't believe they will help and doesn't want to pay the cost to find out.

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.

There’s an argument that library authors should consider implementing those hallucinated functions, not because it’ll be easier for LLMs but because the hallucination is a statement about what an average user might expect to be there.

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.

There's also an argument that developers of new software, including libraries, should consider making an earnest attempt to do The Right Thing instead of re-implementing old, flawed designs and APIs for familiarity's sake. We have enough regression to the mean already.

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.

Polars has their own LLM customised for the docs:

https://docs.pola.rs/api/python/stable/reference/

I would say that this is pretty good approach to combat the previous.

That sort of "REPL" system is why I really liked when they integrated a Python VM into ChatGPT - it wasn't perfect, but it could at least catch itself when the code didn't execute properly.

Sure. But it's 2025 and however you want to get this feature, be it as something integrated into VSCode (Cursor, Windsurf, Copilot), or a command line Python thing (aider), or a command line Node thing (OpenAI codex and Claude Code), with a specific frontier coding model or with an abstracted multi-model thingy, even as an Emacs library, it's available now.

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.

The churn of staying on top of this means to me that we'll also chew through experts of specific times much faster. Gone are the day of established, trusted top performers, as every other week somebody creates a newer, better way of doing things. Everybody is going to drop off the hot tech at some point. Very exhausting.

The answer is simple: have your AI stay on top of this for you.

Mostly joke, but also not joke: https://news.smol.ai/

I like using Jupyter Console as a primary interpreter, and then dropping into SQLite/duckdb to save data

Easy to to script/autogenerate code and build out pipelines this way

It is a little crazy how fast this has changed in the past year. I got VSCode's agent mode to write, run, and read the output of unit tests the other day and boy it's a game changer.

Cursor also can read and store documentation so it's always up to date [0]. Surprised that many people I talk to about Cursor don't know about this, it's one of its biggest strengths compared to other tools.

[0] https://docs.cursor.com/context/@-symbols/@-docs

>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.

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 has been my experience with any LLM I use as a code assistant. Currently I mostly use Claude 3.5, although I sometimes use Deepseek or Gemini.

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).

> 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.

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.

I ran into this as well, but now I have given standing instructions for the llm to pull the latest RR docs anytime it needs to work with RR. That has solved the entire issue.

In addition to choosing languages, patterns and frameworks that the LLM is likely to be well trained in, I also just ask it how it wants to do things.

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.

Have you tried it? In my experience they just go off on a hallucination loop, or blow up the code base with terrible re-implementations.

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.

Yes, they're part of my daily toolset. And yes, they can spin out. I just hit the "reject" button when they do, and revise my prompt. Or, sometimes, I just take over and fill in some of the structure of the problem I'm trying to solve myself.

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.

> 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.

> 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.

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.

> 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.

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).

> ChatGPT search was only released in November last year

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.

If you've only been using AI for a couple of weeks, that's quite likely a factor. AI services have been improving incredibly quickly, and many people have a bad impression of the whole field from a time when it was super promising, but basically unusable. I was pretty dismissive until a couple of months ago, myself.

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.

> 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.

> I feel like you can divide people in a couple of groups.

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.

So is the real engineering work in the agents rather than in the LLM itself then? Or do they have to be paired together correctly? How do you go about choosing an LLM/agent pair efficiently?

> How do you go about choosing an LLM/agent pair efficiently?

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.

I also wonder how hard it would be to create your own agent that remembers your preferences and other stuff that you can make sure stays in the LLM context.

...Maybe a good first LLM assisted project.

No need to write your own whole thing (though it is a good exercise) — the existing tools all support ways of customizing the prompting with preferences and conventions, whether globally or per-project.

The Aider leaderboards are quite helpful, performance there seems to match people's subjective experience pretty well.

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.

> anti-LLM people saying very obviously untrue things, which is uh, kind of hilarious in a meta way.

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.

EDIT: wait, I think you're tptacek's parent. I was not talking about your post, I was talking about the post I linked to. I'm leaving my reply here but there's some serious confusion going on.

> 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 think it is pretty simple: people tried it a few times a few months ago in a limited setting, formed an opinion based on those limited experiences and cannot imagine a world where they are wrong.

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.

Yeah, I try to stay away from reaching for these sorts of explanations, because it feels uncharitable. I saw a lot of very smart people repost the quoted post! They're not the kind who "cannot imagine a world where they are wrong."

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.

My language is inflammatory for certain, but I believe it is true. I don't think most minds are capable of having to reevaluate their opinions as quickly as AI is demanding. There is some evidence that stress is strongly correlated to uncertainty. AI is complicated, the tools are complicated, the trade-off are complicated. So that leaves a few options: live in uncertainty/stress, expend the energy to reevaluate or choose to believe in certainty based on past experience.

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.

These are mostly value judgments and people are using words that mean different things to different people but I would point that LLM boosters have been saying the same thing for each product release: "now it works, you are just using the last-gen model/technique which doesn't really work (even though I said the same thing for that model/technique and every one before that). Moreover there still hasn't been significant, objectively observable impact: no explosion in products, no massive acceleration of feature releases, no major layoffs attributed to AI (to which the response every time is that it was just released and you will see the effects in a few months).

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.

> using words that mean different things to different people

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.

There is so much tacit understanding by both LLM-boosters and LLM-skeptics that only becomes apparent when you look at the explicit details of how they are trying to use the tools. That's why I've asked in the past for examples of recording of real-time development that would capture all the nuance explicitly. Cherry-picked chat logs are second best but even then I haven't been particularly impressed by the few examples I've seen.

> 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).

> an instance of this from a few days ago.

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.

I think this is related, but I'm more interested in the factual aspects than the subjective ones. That is, I don't disagree there's also arguments over "are LLMs good for the same things search engines for" but it's more of the more objective "they do not search the web" part. We need to have agreement on the objective aspects before we can have meaningful discussion of the subjective, in my mind.

> I think someone else likened it to a lawnmower that will run rampage over your flower bed at the first hiccup

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.

https://youtu.be/_Ha3fyDIXnc

Have you tried it? More than once?

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 use it all the time, multiple times daily. But the discussion is not being very honest, particularly for all the things that are being bolted on (agent mode, MCP). Like just upstream people dunk on others for pointing out that maybe giving the model an API call to read webpages isn't quite turning LLM into search engines. Just like letting it run shell commands has not made it into a full blown agent engineer.

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)

It's really weird for me to see people talking about using LLMs in coding situations in a frame where "agents" (we're not even at MCP yet!) are somehow an extra. People discussing the applicability of LLMs to programming, and drawing conclusions (even if only for themselves) about how well it works, should be experienced with a coding agent.

I mean, I have. I use them every day. You often see them literally saying "Oh there is a linter error, let me go fix it" and then a new code generation pass happens. In the worst case, it does exactly what you are saying, gets stuck in a loop. It eventually gets to the point where it says "let me try just once more" and then gives up.

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?

Someone gave me the tip to add "all source files should build without error", which you'd think would be implicit, but it seems not.

There's definitely a skill to using them well (I am not yet expert); my only frustration is with people who (like me) haven't refined the skill but have also concluded that there's no benefit to the tool. No, really, in this case, you're mostly just not holding it right.

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.

How much money do you spend a day working like this?

I haven't spent many days or full days, but when I've toyed with this, it ends up at about $10/hour or maybe a bit less.

> the program doesn't compile

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?

Python is, in fact, compiled (to bytecode, not native code); while this is mostly invisible, syntax errors will cause it to fail to compile, but the circumstances described (hallucinating a function) will not, because function calls are resolved by runtime lookup, not at compile time.

I get that, and in that sense most languages are compiled, but generally speaking, I've always understood "compiled" as compiled-ahead-of-time - Python certainly doesn't do that and the official docs call it an interpreted language.

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".

> I get that, and in that sense most languages are compiled, but generally speaking, I've always understood "compiled" as compiled-ahead-of-time

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, (edit: calling) 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.

(Edit: A sibling comment points out that importing a hallucinated function would cause a compilation failure, and that's a good point.)

When a module is first loaded, it's compiled to bytecode and then executed. Importing a non existing function will throw an error right away.

It's not a compilation error but it does feel like one, somewhat. It happens at more or less the same time.

Yes but it gets feedback from the IDE. Cursor is the best here

Re vibe coding, I agree with your comments but where I've used it is when I needed to mock up a UI or a website. I have no front end experience so making a 80% (probably 20%) but live demo is still a valuable thing, to show to others to get the point across, obviously not to deploy. It's a replacement for drawing a picture of what I think the UI should look like. I feel like this is an under-appreciated use. LLM coding is not remotely ready for real products but it's great for mock-ups that further internal discussions.

Same. As somebody who doesn't really enjoy frontend work at all, they are surprisingly good at being able to spit out something that is relatively visually appealing - even if I'll end up rewriting the vast majority of react spaghetti code in Svelte.

In the settings for Claude, I tell it to use Svelte and TypeScript whenever possible because I got tired of telling it I don't use React.

If I need a quick mockup I’ll do it in react

But if I have time I’ll ask for it to be built using best practices from the programming language it’ll be built in as the final product whether that’s svelte, static astro, or even php

I’m not even seeing this react spaghetti code you’re referring to. It’s pretty impressive front ends with what looks like concise, well organized code.

I love front-end work, and I'm really good at it, but I now let the AI do CSS coding for me. It seems to make nice looking buttons and other design choices that are good enough for development. My designer has their own opinions, so they always will change the CSS when they get their hands on it, but at least I'm not wasting my time creating really ugly styles that always get replaced anymore. The rest of the coding is better if I do it, but sometimes the AI surprises me - though most often it gets it completely wrong, and then I'm wasting time letting it try and that just feels counterproductive. It's like a really stupid intern that almost never pays attention to what the goal is.

They're pretty good at following direction. For example you can say:

'Usw React, typescript, materialUi, prefer functions over const, don't use unnecessary semi colons, 4 spaces for tabs, build me a UI that looks like this sketch'

And it'll do all that.

I think it would be faster/easier to use a website builder or various templating libraries to build a quick UI rather than having to babysit an LLM with prompts over and over again.

> However, for more complex code questions particularly around less popular libraries which have fewer code examples scraped from Stack Overflow and GitHub, I am more cautious of the LLM’s outputs.

That's changed for me in the past couple of months. I've been using the ChatGPT interface to o3 and o4-mini for a bunch of code questions against more recent libraries and finding that they're surprisingly good at using their search tool to look up new details. Best version of that so far:

"This code needs to be upgraded to the new recommended JavaScript library from Google. Figure out what that is and then look up enough documentation to port this code to it."

This actually worked! https://simonwillison.net/2025/Apr/21/ai-assisted-search/#la...

The other trick I've been using a lot is pasting the documentation or even the entire codebase of a new library directly into a long context model as part of my prompt. This works great for any library under about 50,000 tokens total - more than that and you usually have to manually select the most relevant pieces, though Gemini 2.5 Pro can crunch through hundreds of thousands of tokens pretty well with getting distracted.

Here's an example of that from yesterday: https://simonwillison.net/2025/May/5/llm-video-frames/#how-i...

I think they might have made a change to Cursor recently as well. A few times I've caught it using an old API of popular libraries that have updates. Shout out to all the library developers that are logging deprecations and known incorrect usages, that has been a huge win with LLMs. In most cases I can paste the deprecation warning back into the agent and it will say "Oh, looks like that API changed in vX.Y.Z, we should be doing <other thing>, let me fix that ..."

So it is capable of integrating new API usage, it just isn't a part of the default "memory" of the LLM. Given how quickly JS libraries tend to change (even on the API side) that isn't ideal. And given that the typical JS server project has dozens of libs, including the most recent documentation for each is not really feasible. So for now, I am just looking out for runtime deprecation errors.

But I give the LLM some slack here, because even if I was programming myself using an library I've used in the past, I'm likely to make the same mistake.

You can just use @Docs [0] to import the correct documentation for your libraries.

[0] https://docs.cursor.com/context/@-symbols/@-docs

Sure, however I made the point "how quickly JS libraries tend to change" and "the typical JS server project has dozens of libs". That can add up to a lot of context.

I suppose a point could be made that I could keep the docs up-to-date using Cursor's custom docs feature and hope that whatever magic they are doing to manage context doesn't blow out the daily token budget. And I haven't tried that since the flow I mentioned about tends to work (as long as the libraries in question provide good diagnostic logs).

A good augmentation to my workflow might be periodically prompting "In file X I am using lib Y at version vN.M.P, check the docs for that version and report any errors or misuses ...".

I’ve had similar experiences in recent weeks, enabling search in ChatGPT with o4-mini-high to fix previously insurmountable hurdles I’d run into wrt breaking changes in libraries that have occurred outside the model cutoff dates, and for which the errors and fixes are non-obvious. It’s worked far better than I’d expected for what feels like such a simple UI toggle.

> Over the years I’ve utilized all the tricks to get the best results out of LLMs.

The poster tips their hand early in the article and I can see there won't be much substance here. I work on writing prompts for production solutions that use LLMs to QA various text inputs that would be very hard to do using traditional NLP techniques. Good prompt engineering had very little to do with thinking up ridiculous scenarios to "trick" the LLM into being better. Those are actually counterproductive because their efficacy can vary widely across model versions.

I like that the author included the chat logs. I know there's a lot of times where people can't share them because they'd expose too much info, but I really think it's important when people make big claims about what they've gotten an LLM to do that they back it up.

That is a relatively new workflow for me since getting the logs out of the Claude UI is a more copy/paste manual process. I'm likely going to work on something to automate it a bit.

I use this:

  llm -m claude-3.7-sonnet "prompt"
  llm logs -c | pbcopy

Isn't your Observable notebook more applicable to what he's talking about (scraping the Claude UI)? https://x.com/simonw/status/1821649481001267651

I'm interested in any approach to better organize LLM outputs offline, but it would probably be more pragmatic to use some sort of scripting tool.

> I never use ChatGPT.com or other normal-person frontends for accessing LLMs because they are harder to control. Instead, I typically access the backend UIs provided by each LLM service, which serve as a light wrapper over the API functionality which also makes it easy to port to code if necessary.

Yes, I also often use the "studio" of each LLM for better results because in my experience OpenAI "nerfs" models on the ChatGPT UI (models keep forgetting things—probably a limited context length set by OpenAI to reduce costs, generally the model is less chatty (again, probably to reduce their costs), etc. But I've noticed Gemini 2.5 Pro is the same on the studio and the Gemini app.

> Any modern LLM interface that does not let you explicitly set a system prompt is most likely using their own system prompt which you can’t control: for example, when ChatGPT.com had an issue where...

ChatGPT does have system prompts but Claude doesn't (one of its many, many UI shortcomings which Anthropic never addressed).

That said, I've found system prompts less and less useful with newer models. I can simply preface my own prompt with the instructions and the model follows them very well.

> Specifying specific constraints for the generated text such as “keep it to no more than 30 words” or “never use the word ‘delve’” tends to be more effective in the system prompt than putting them in the user prompt as you would with ChatGPT.com.

I get that LLMs have a vague idea of how many words are 30 words, but they never do a good job in these tasks for me.

I've tried it out a ton but the only thing I end up using it for these days is teaching me new things (which I largely implement myself; it can rarely one-shot it anyway). Or occasionally to make short throwaway scripts to do like file handling or ffmpeg.

> I typically access the backend UIs provided by each LLM service, which serve as a light wrapper over the API functionality

Hey Max, do you use a custom wrapper to interface with the API or is there some already established client you like to use?

If anyone else has a suggestion please let me know too.

I'm going to plug my own LLM CLI project here: I use it on a daily basis now for coding tasks like this one:

llm -m o4-mini -f github:simonw/llm-hacker-news -s 'write a new plugin called llm_video_frames.py which takes video:path-to-video.mp4 and creates a temporary directory which it then populates with one frame per second of that video using ffmpeg - then it returns a list of [llm.Attachment(path="path-to-frame1.jpg"), ...] - it should also support passing video:video.mp4?fps=2 to increase to two frames per second, and if you pass ?timestamps=1 or &timestamps=1 then it should add a text timestamp to the bottom right conner of each image with the mm:ss timestamp of that frame (or hh:mm:ss if more than one hour in) and the filename of the video without the path as well.' -o reasoning_effort high

Any time I use it like that the prompt and response are logged to a local SQLite database.

More on that example here: https://simonwillison.net/2025/May/5/llm-video-frames/#how-i...

Seconded, everyone should be using CLIs.

Simon: while that example is impressive, it is also complicated and hard to read in an HN comment.

I was developing an open-source library for interfacing with LLMs agnostically (https://github.com/minimaxir/simpleaichat) and although it still works, I haven't had the time to maintain it unfortunately.

Nowadays for writing code to interface with LLMs, I don't use client SDKs unless required, instead just hitting HTTP endpoints with libraries such as requests and httpx. It's also easier to upgrade to async if needed.

most services has a "studio mode" for their models served.

As an alternative you could always use OpenWebUI

I built an open source CLI coding agent for this purpose[1]. It combines Claude/Gemini/OpenAI models in a single agent, using the best/most cost effective model for different steps in the workflow and different context sizes. You might find it interesting.

It uses OpenRouter for the API layer to simplify use of APIs from multiple providers, though I'm also working on direct integration of model provider API keys—should release it this week.

1 - https://github.com/plandex-ai/plandex

you are using llms for anything more than generating boilerplate, you are usually wasting your time

JSON response doesn’t always work as expected unless you have few items to return. In Max’s example it’s classification.

For anyone trying to return consistent json, checkout structured data where you define a json schema with required field and that would return the same structure all the time.

I have tested it with high success using GPT-4o-mini.

This was an interesting quote from the blog post: "There is one silly technique I discovered to allow a LLM to improve my writing without having it do my writing: feed it the text of my mostly-complete blog post, and ask the LLM to pretend to be a cynical Hacker News commenter and write five distinct comments based on the blog post."

I do a good deal of my blog posts while walking my husky and just dictating using speech-to-text on my phone. The problem is that its an unformed blob of clay and really needs to be shaped on the wheel.

I then feed this into an LLM with the following prompt:

  You are a professional editor. You will be provided paragraphs of text that may 
  contain spelling errors, grammatical issues, continuity errors, structural 
  problems, word repetition, etc. You will correct any of these issues while 
  still preserving the original writing style. Do not sanitize the user. If they 
  use profanities in their text, they are used for emphasis and you should not 
  omit them. 

  Do NOT try to introduce your own style to their text. Preserve their writing 
  style to the absolute best of your ability. You are absolutely forbidden from 
  adding new sentences.

I do something similar. But I make sure the LLM doesn't know I wrote the post. That way the LLM is not sycophantic.

Make sure the check out the LLM chat log he shared for the current article! The tone is cynical HN commenter indeed.

https://github.com/minimaxir/llm-use/blob/main/criticism_hn....

What roleplayed feedback providers have people had best and worst luck with? I can imagine asking for the personality could help the LLM come up with different kinds of criticisms...

As a data scientist, this mirrors my experience. Prompt engineering is surprisingly important for getting expected output - and use LLM POCs have quick turnaround times.

"To that end, I never use ChatGPT.com or other normal-person frontends for accessing LLMs because they are harder to control. Instead, I typically access the backend UIs provided by each LLM service, which serve as a light wrapper over the API functionality which also makes it easy to port to code if necessary."

How do you do this? Do you have to be on a paid plan for this?

I think they're talking about the Sandbox/Playground/Editor thingy that almost all companies who expose APIs also offer to quickly try out API features. For OpenAI it's https://platform.openai.com/playground/prompts?models=gpt-4...., Anthropic has https://console.anthropic.com/workbench and so on.

If you log into the API backend, there is usually a link to the UI. For OpenAI/ChatGPT, it's https://platform.openai.com/playground

This is independent of ChatGPT+. You do need to have a credit card attached but you only pay for your usage.

>Ridiculous headline implying the existance of non-generative LLMs

>Baited into clicking

>Article about generative LLMs

>It's a buzzfeed employee

The reason I added "generative" to the headline is because of the mention about the important use of text embedding models that are indeed non-generative LLMs and I did not want to start a semantics war if I did not explicitly specify "generative LLMs." (the headline would flow better without it)

Emmm... why has Claude 'improved' the code by setting SQLite to be threadsafe and then adding locks on every db operation? (You can argue that maybe the callbacks are invoked from multiple threads, but they are not thread safe themselves).

Interns don't understand concurrency either.

Listen, I can fail to understand your questions for half the price you're paying the LLM.

But if you teach them the right way to do it today and have them fix it, they won't go and do it the wrong way again tomorrow and the next day amd every day for the rest of the summer.

With concurrency, they still might get it wrong the rest of the summer. It's a hard topic. But at least they might learn they need to ask for feedback when they're doing that looks similar to stuff that's previously caused problems.

This article reads like "I'm not like other LLM users" tech writing. There are good points about when LLMs are actually useful vs. overhyped, but the contrarian framing undermines what could have been straightforward practical advice. The whole "I'm more discerning than everyone else" positioning gets tiresome in tech discussions, especially when the actual content is useful.

I was not explicitly intending to be contrarian, but unfortunately the contrarian framing is inevitable when the practical advice is counterintuitive and against modern norms. I was second-guessing publishing this article at all because "I don't use ChatGPT.com" and "I don't see a use for Agents/MCP/Vibe coding" are both statements that are potentially damaging to my career as an engineer, but there's no point in writing if I can't be honest.

Part of the reason I've been blogging about LLMs for so long is that a lot of it is counterintuitive (which I find interesting!) and there's a lot of misinformation and suboptimal workflows that results from it.

> "I don't use ChatGPT.com" and "I don't see a use for Agents/MCP/Vibe coding" are both statements that are potentially damaging to my career as an engineer

This is unfortunate, though I don't blame you. Tech shouldn't be about blind faith in any particular orthodoxy.

Tone and word choice is actually the problem here. :)

One example: “normal-person frontends” immediately makes the statement a judgement about people. You could have said regular, typical, or normal instead of “normal-person”.

Saying your coworkers often come to you to fix problems and your solutions almost always work can come off as saying you’re more intelligent than your coworkers.

The only context your readers have are the words you write. This makes communication a damned nuisance because nobody knows who you are and they only know about you from what they read.

That descriptor was more intended to be a self-deprecation joke at my expense but your interpretation is fair.

Your defense of the contrarian framing feels like it's missing the point. What you're describing as "counterintuitive" is actually pretty standard for anyone who's been working deeply with LLMs for a while.

Most experienced LLM users already know about temperature controls and API access - that's not some secret knowledge. Many use both the public vanilla frontends and specialized interfaces (various HF workflows, custom setups, sillytavern, oobabooga (̵r̵i̵p̵)̵, ollama, lmstudio, etc) depending on the task.

Your dismissal of LLMs for writing comes across as someone who scratched the surface and gave up. There's an entire ecosystem of techniques for effectively using LLMs to assist writing without replacing it - from ideation to restructuring to getting unstuck on specific sections.

Throughout the article, you seem to dismiss tools and approaches after only minimal exploration. The depth and nuance that would be evident to anyone who's been integrating these tools into their workflow for the past couple years is missing.

Being honest about your experiences is valuable, but framing basic observations as contrarian insights isn't counterintuitive - it's just incomplete.

Do you have links to any resources for using LLM for writing?

> oobabooga (rip)

Why (rip) here?

Ah, I have intuitively confused it with a1111. oobabooga is alive and well :)

... but when I do, I let it write regex, SQL commands, simple/complex if else stuff, apply tailwind classes, feed it my console log errors, propose frontend designs ... and other little stuff. Saves brain power for the complex problems.

> "feed it the text of my mostly-complete blog post, and ask the LLM to pretend to be a cynical Hacker News commenter and write five distinct comments based on the blog post."

It feels weird to write something positive here...given the context...but this is a great idea. ;)

This is the kind of task where, before LLMs, I wouldn't have done it. Maybe if it was something really important I'd circulate it to a couple friends to get rough feedback, but mostly it was just let it fly. I think it's pretty revolutionary to be able to get some useful feedback in seconds, with a similar knock-on effect in the pull request review space.

The other thing I find LLMs most useful for is work that is simply unbearably tedious. Literature reviews are the perfect example of this - Sure, I could go read 30-50 journal articles, some of which are relevant, and form an opinion. But my confidence level in letting the AI do it in 90 seconds is reasonable-ish (~60%+) and 60% confidence in 90 seconds is infinitely better than 0% confidence because I just didn't bother.

A lot of the other highly hyped uses for LLMs I personally don't find that compelling - my favorite uses are mostly like a notebook that actually talks back, like the Young Lady's Illustrated Primer from Diamond Age.

> But my confidence level in letting the AI do it in 90 seconds is reasonable-ish (~60%+) and 60% confidence in 90 seconds is infinitely better than 0% confidence because I just didn't bother.

So you got the 30 to 50 articles summarized by the LLM, now how do you know what 60% you can trust and what’s hallucinated without reading it? It’s hard to be usable at all unless you already do know what is real and what is not.

So, generally how I use it is to get background for further research. So you're right, you do have to do further reading, but it's at the second tier - "now that I know roughly what I'm looking for, I have targets for further reading", rather than "How does any of this work and what are relevant articles"

While I think the title is misleading/clickbaity (no surprise given the buzzfeed connection), I'll say that the substance of the article might be one of the most honest take on LLMs I've seen from someone who actually works in the field. The author describes exactly how I use LLMs - strategically, for specific tasks where they add value, not as a replacement for actual thinking.

What resonated most was the distinction between knowing when to force the square peg through the round hole vs. when precision matters. I've found LLMs incredibly useful for generating regex (who hasn't?) and solving specific coding problems with unusual constraints, but nearly useless for my data visualization work.

The part about using Claude to generate simulated HN criticism of drafts is brilliant - getting perspective without the usual "this is amazing!" LLM nonsense. That's the kind of creative tool use that actually leverages what these models are good at.

I'm skeptical about the author's optimism regarding open-source models though. While Qwen3 and DeepSeek are impressive, the infrastructure costs for running these at scale remain prohibitive for most use cases. The economics still don't work.

What's refreshing is how the author avoids both the "AGI will replace us all" hysteria and the "LLMs are useless toys" dismissiveness. They're just tools - sometimes useful, sometimes not, always imperfect.

Just about the point about the prohibitive infrastructure at scale, why does it need to be at scale?

Over few years, we went from literally impossible to being able to run a 72B model locally on a laptop. Give it 5-10 years and we might not need to have any infrastructure at all, all served locally with switchable (and different sized) open source models.

Side topic : I didn’t see a serious article about prompt engineering for senior software development pop up on HN. Yet a lot of users here have their own techniques unshared with others

It's maddening to me how little good writing there is out there on effective prompting.

Here's an example: what's the best prompt to use to summarize an article?

That feels like such an obvious thing, and yet I haven't even seen that being well explored.

It's actually a surprisingly deep topic. I like using tricks like "directly quote the sentences that best illustrate the overall themes" and "identify the most surprising ideas", but I'd love to see a thorough breakdown of all the tricks I haven't seen yet.

This whitepaper is the best I've found so far, at least for covering general prompting techniques.

https://www.kaggle.com/whitepaper-prompt-engineering

This comment has some advice from Simon Willison on this topic:

https://news.ycombinator.com/item?id=43897666

Maybe you should ask him. lol

It seems a little counterintuitive, but you can ask an LLM to improve a prompt. They are quite good at it.

I was just listening to Simon Willison on Software Misadventures,[0, 1] and he said the best resource he knows of is Anthropic's guide to prompt engineering.[2]

[0] https://softwaremisadventures.com/p/simon-willison-llm-weird...

[1] https://youtu.be/6U_Zk_PZ6Kg?feature=shared&t=56m29 (the exact moment)

[2] https://docs.anthropic.com/en/docs/build-with-claude/prompt-...

An unfortunate casualty of the importantance of prompt engineering is that there is less of an incentive to share good prompts publicly.

You can read all of the prompts in Plandex (open source CLI coding agent focusing on larger projects): https://github.com/plandex-ai/plandex/tree/main/app/server/m...

They're pretty long and extensive, and honestly could use some cleaning up and refactoring at this point, but they are being used heavily in production and work quite well, which took a fairly extreme amount of trial-and-error to achieve.

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Well, you certainly live up to your username.

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>Discourse about LLMs and their role in society has become bifuricated enough such that making the extremely neutral statement that LLMs have some uses is enough to justify a barrage of harrassment.

Honestly true and I’m sick of it.

A very vocal group of people are convinced AI is a scheme by the evil capitalists to make you train your own replacement. The discussion gets very emotional very quickly because they feel personally threatened by the possibility that AI is actually useful.

> people are convinced AI is a scheme by the evil capitalists to make you train your own replacement. The discussion gets very emotional very quickly because they feel personally threatened by the possibility that AI is actually useful.

Are not mutually exclusive. LLMs will train people's replacements while same people pay for the privilege of training those replacement. LLMs also allows me to auto-complete a huge volume of boilerplate, which would take me several hours. It also helps people step out of writer's block, generate a first draft of prototype/mvp/poc etc quickly without wasting long hours bike shedding. It also helps my previously super confident cousin, who blamed me for killing his dream of next AirBnB for dogs, Uber for groceries, instagram for cats not materializing due to me being selfish hoarding my privileges and knowledge, to finally create those ideas and kill his own dreams and definitely ignoring/avoiding me these days.

LLMs are same as knives, crimes will happen, but also necessary in the kitchen and industries.

Should they not feel threatened? I'm somewhat sympathetic to the view that even the current state of the art is threatening to people's livelihood.

And of course it will only become more powerful. It's a dangerous game.

> A very vocal group of people are convinced AI is a scheme by the evil capitalists to make you train your own replacement. The discussion gets very emotional very quickly because they feel personally threatened by the possibility that AI is actually useful

I read this like you are framing this as though it is irrational. However, history is littered with examples of capitalists replacing labour with automation and using any productivity gains of new technology to push salaries lower

Of course people who see this playing out again are personally threatened. If you aren't feeling personally threatened, you are either part of the wealthy class or for some reason you think this time will be different somehow

You may be thinking "Even if I lose my job to automation, there will be other work to do like piloting the LLMs", but you should know that the goal is to eventually pay LLM operators peanuts in comparison to what you currently make in whatever role you do

Setting up automation as the enemy is an odd thing for programmers to be doing. I mean, if you’re a programmer and you’re not automating away tasks, both for yourself and other people, what are you even doing?

Also, “this time it’s different” depends on the framing. A cynical programmer who has seen new programming tools hyped too many times would make a different argument: At the beginning of the dot-com era, you could get a job writing HTML pages. That’s been automated away, so you need more skill now. It hasn’t resulting in fewer software-engineering jobs so far.

But that’s not entirely convincing either. Predicting the future is difficult. Sometimes the future is different. Making someone else’s scenario sound foolish won’t actually rule anything out.

> Setting up automation as the enemy is an odd thing for programmers to be doing. I mean, if you’re a programmer and you’re not automating away tasks, both for yourself and other people, what are you even doing?

I'm earning a salary so I can have a nice life

Anything that threatens my ability to earn a salary so I can have a nice life is absolutely my fucking enemy

I have no interest in automating myself out of existence, and I am deeply resentful of the fact that so many people are gleefully trying to do so.

What happens when you use an LLM to generate prompts for an LLM?

>history is littered with examples of capitalists replacing labour with automation and using any productivity gains of new technology to push salaries lower

Nonsense. We make far, far more than people did in the past entirely because of the productivity gains from automation.

The industrial revolution led to the biggest increase in quality of life in history, not in spite of but because it automated 90% of jobs. Without it we'd all still be subsistence farmers.

> Nonsense. We make far, far more than people did in the past entirely because of the productivity gains from automation.

"We" were never subsistence farmers, our ancestors were

I'm talking about real changes that have happened in our actual lifetimes.

In our actual lifetimes we have watched wages stagnate for decades, pur purchasing power is dramatically lower than our parents. In order to afford even remotely close to the same standard of living as our parents had, we have to go into much larger amounts of debt

We have watched jobs move overseas as automation lowered the skill requirements so that anyone could perform them, so we sought the cheapest possible labour to do them

We have watched wealthy countries shift from higher paying production economies into lower paying service economies

We have watched the wealth gap widen as the rich get richer the poor get poorer and the middle class shrinks. Some of the shrinking middle class moved up, but most moved down

The fact is that automation is disruptive, which is great for markets but bad for people who are relying on consistency. Which is most people

> In our actual lifetimes we have watched wages stagnate for decades, pur purchasing power is dramatically lower than our parents.

This graph is going up.

https://fred.stlouisfed.org/series/MEPAINUSA672N

The reason you believe otherwise is that people on social media think they're only allowed to say untrue negative things about the economy, because if they ever say anything positive it'd be disrespectful to poor people.

> We have watched wealthy countries shift from higher paying production economies into lower paying service economies

Service work is higher paying, which is why factory workers try to get their children educated enough to do it.

> We have watched the wealth gap widen as the rich get richer the poor get poorer and the middle class shrinks. Some of the shrinking middle class moved up, but most moved down

They mostly moved up. But notice this graph is positive for every group.

https://realtimeinequality.org/?id=wealth&wealthend=03012023...

> Service work is higher paying, which is why factory workers try to get their children educated enough to do it

You think service workers at a fast food restaurant or working the till at Walmart are higher paid than Factory workers?

> They mostly moved up. But notice this graph is positive for every group.

The reason it is positive for (almost) every group is because it isn't measuring anything meaningful

Salaries may have nominally gone up but this is clearly not weighing the cost of living into the equation

> You think service workers at a fast food restaurant or working the till at Walmart are higher paid than Factory workers?

Fast food workers aren't service-economy workers, they're making burgers back there.

More importantly, factory work destroys your body and email jobs don't, so whether or not it's high earning at the start… it isn't forever.

> Salaries may have nominally gone up but this is clearly not weighing the cost of living into the equation

That's not a salary chart. The income chart I did post is adjusted for cost of living (that's what "real" means).

Also see https://www.epi.org/blog/wage-growth-since-1979-has-not-been...

> Fast food workers aren't service-economy workers, they're making burgers back there.

What exactly do you consider the "service industry" if you don't consider food services (like restaurants and fast food) as part of it?

I suspect we have very different ideas of what "service industry" means

> Also see https://www.epi.org/blog/wage-growth-since-1979-has-not-been..

Did you even read the whole thing?

```Dropping “wage stagnation” as a descriptive term for the full post-1979 period doesn’t mean we think the wage problem for American workers has been solved. Wage growth in the post-1979 period has been slow and unequal, largely as a result of intentional policy decisions. This policy-induced wage suppression has stifled growth in living standards and generated inequality. The last five years saw rapid and welcome progress reversing some of these trends—but it will take a long time to heal the previous damage, even if the post-2019 momentum can be sustained, which looks very unlikely at the moment.```

Admitting there is a problem but saying "it isn't stagnation" is just splitting hairs.

"Wage growth has been slow and unequal, but it isn't stagnation!"

"policy-induced wage suppression has stifled growth in living standards and generated inequality" there was wage suppression but it's not stagnation!

What a stupid article.