Which of course they are going to try to brush it all away. Better than admitting that this problem very much still exists and isn’t going away anytime soon.

The section about hallucinations is deeply relevant.

Namely, Claude sometimes provides a plausible but incorrect chain-of-thought reasoning when its “true” computational path isn’t available. The model genuinely believes it’s giving a correct reasoning chain, but the interpretability microscope reveals it is constructing symbolic arguments backward from a conclusion.

https://en.wikipedia.org/wiki/On_Bullshit

This empirically confirms the “theory of bullshit” as a category distinct from lying. It suggests that “truth” emerges secondarily to symbolic coherence and plausibility.

This means knowledge itself is fundamentally symbolic-social, not merely correspondence to external fact.

Knowledge emerges from symbolic coherence, linguistic agreement, and social plausibility rather than purely from logical coherence or factual correctness.

Recognizing the relevance of coherence and plausibility does not need to imply that other aspects are any less relevant. Redefining truth merely because coherence is important and sometimes misinterpreted is not at all reasonable.

Logically, a falsehood can validly be derived from assumptions when those assumptions are false. That simple reasoning step alone is sufficient to explain how a coherent-looking reasoning chain can result in incorrect conclusions. Also, there are other ways a coherent-looking reasoning chain can fail. What you're saying is just not a convincing argument that we need to redefine what truth is.

Sounds very human. It's quite common that we make a decision based on intuition, and the reasons we give are just post-hoc justification (for ourselves and others).

well yes, of course it does, that article goes out of its way to anthropomorphize LLMs, while providing very little substance

To me it feels like people that benefit from or at least enjoy that sort of assistance and I solve vastly different problems and code very differently.

I’ve done exhausting code reviews on juniors’ and middles’ PRs but what I’ve been feeling lately is that I’m reviewing changes introduced by a very naive poster. It doesn’t even type-check. Regardless of whether it’s Claude 3.7, o1, o3-mini, or a few models from Hugging Face.

I don’t understand how people find that useful. Yesterday I literally wasted half an hour for a test suite setup a colleague of mine introduced to the codebase that wasn’t good, and I tried delegating that fix to several of the Copilot models. All of them missed the point, some even introduced security vulnerabilities in the process invalidating JWT validation, I tried “vide coding” it till it works, until I gave up in frustration and just used an ordinary search engine, which led me to the docs, in which I immediately found the right knob. I reverted all that crap and did the simple and correct thing. So my conclusion was simple: vibe coding and LLMs made the codebase unnecessarily more complicated and wasted my time. How on earth do people code whole apps with that?

There is no world in which a compiler or tooling will save you from the absolute mayhem it can do. I’ve had it routinely try to re-implement third party libraries, modify code unrelated to what it was asked, quietly override functions etc.

It’s like a developer who is on LSD.

But in reality hallucinations either make people using AI lose a lot of their time trying to stuck the LLMs from dead ends or render those tools unusable.

Yes they didn't push it as hard as, say, copilot. I still think they got in way too deep way too fast.

    > Apple made an out of character misstep by releasing a terrible UX to everyone

Yes they knew Apple maps was bad and not up to standard yet, but they didn't really have any other choice.

Quite plausibly they just didn't realize how rocky the start would be, or perhaps they valued that immediate strategic autonomy more in the short-term that we think, and willingly chose to take the hit to their reputation rather than wait.

Regardless, they had choices.

I don’t necessarily agree with the post you’re responding to, but what I will give Apple credit for is making their AI offering unobtrusive.

I tried it, found it unwanted and promptly shut it off. I have not had to think about it again.

Contrast that with Microsoft Windows, or Google - both shoehorning their AI offering into as many facets of their products as possible, not only forcing their use, but in most cases actively degrading the functionality of the product in favor of this required AI functionality.

Henry Ford was 23 years “slow” relative to Karl Benz.

Apple fumbled a bit with Siri, and I'm guessing they're not too keen to keep chasing everyone else, since outside of limited applications it turns out half baked at best.

Sadly, unless something shinier comes along soon, we're going to have to accept that everything everywhere else is just going to be awful. Hallucinations in your doctor's notes, legal rulings, in your coffee and laundry and everything else that hasn't yet been IoT-ified.

I installed a logitech mouse driver (sigh) the other day, and in addition to being obtrusive and horrible to use, it jams an LLM into the UI, for some reason.

AI has reached crapware status in record time.

"OK Replicator, make me one espresso with creamer"

"Making one espresso with LSD"

Every week for the last few months, I get a recruiter for a healthcare startup note taking app with AI. It's just a rehash of all the existing products out there, but "with AI". It's the last place I want an overworked non-technical user relying on the computer to do the right thing, yet I've had at least four companies reach out with exactly that product. A few have been similar. All of them have been "with AI".

It's great that it is getting better, but at the end of the day, there's only so much it can be relied upon for, and I can't wait for something else to take away the spotlight.

- An extremely dedicated and high achieving professional, at the very top of her game with deep industry/sectoral knowledge: Successful and with outstanding connections. - Mother of a young child. - Tradition/requirement for success within the sector was/is working extremely long hours: 80-hour weeks are common.

She's implemented AI to automate many of her previous laborious tasks and literally cut down her required hours by 90%. She's now able to spend more time with her family, but also - able to now focus on growing/scaling in ways previously impossible.

Knowing how to use it, what to rely upon, what to verify and building in effective processes is the key. But today AI is at its worst and it already exceeds human performance in many areas.. it's only going in one direction.

Hopefully the spotlight becomes humanity being able to focus on what makes us human and our values, not mundane/routine tasks and allows us to better focus on higher-value/relationships.

Yes. Finally! Now it's real BS. I wouldn't touch it with 8 meter pole.

[0] https://www.bbc.com/news/articles/cq5ggew08eyo

I'm certain they'll get it right soon enough though. People were writing off Google in terms of AI until this year.. and oh how attitudes have changed.

Just give Google a year or two.

Google has a pretty amazing history of both messing up products generally and especially "ai like" things, including search.

(Yes I used to defend Google until a few years ago.)

that's the problem noo?? big company is sucks at that, you cant do that in certain company because sometimes its just not possible

What do you mean? Code shouldn't degrade if it's not changed. But the iOS spell checker is actively getting worse, meaning someone is updating it.

If they don't then I'd hope they get absolutely crucified by trade comissions everywhere, currently there are bilboards in my city advertising Apple AI even though it doesn't even exist yet - if it's never brought to the market then it's a serious case of misleading advertising.

Don't get me wrong, I use AI every day, but it's mostly as a localized code complete or to help me debug tricky issues. Meaning I've written and understand the code myself, and the AI is there to augment my abilities. AI works great if it's used as a deductive tool.

Where it runs into issues is when it's used inductively, to create things that aren't there. When it does this, I feel the hallucinations can be off the charts -- inventing APIs, function names, entire libraries, and even entire programming languages on occasion. The AI is more than happy to deliver any kind of information you want, no matter how wrong it is.

AI is not a tool, it's a tiny Kafkaesque bureaucracy inside of your codebase. Does it work today? Yes! Why does it work? Who can say! Will it work tomorrow? Fingers crossed!

I use AI for rather complex tasks. It's impressive. It can make a bunch of non-trivial changes to several files, and have the code compile without warnings. But I need to iterate a few times so that the code looks like what I want.

That being said, I also lose time pretty regularly. There's a learning curve, and the tool would be much more useful if it was faster. It takes a few minutes to make changes, and there may be several iterations.

It sounds like the guys in this article should not have trusted AI to go fully open loop on their customer support system. That should be well understood by all "customers" of AI. You can't trust it to do anything correctly without human feedback/review and human quality control.

2) No matter what the learning curve, you're using a statistical tool that outputs in probabilities. If that's fine for your workflow/company, go for it. It's just not what a lot of developers are okay with.

Of course it's a spectrum with the AI deniers in one corner and the vibe coders in the other. I personally won't be relying 100% on a tool and letting my own critical thinking atrophy, which seems to be happening, considering recent studies posted here.

Where the AI fails is in doing anything which requires having a model of the world. I'm writing a simulator which involves agents moving through an environment. A small change in agent behaviour may take many steps of the simulator to produce consequential effects, and thinking through how that happens -- or the reverse: reasoning about the possible upstream causes of some emergent macroscopic behaviour -- requires a mental model of the simulation process, and AI absolutely does _not_ have that. It doesn't know that it doesn't have that, and will therefore hallucinate wildly as it grasps at an answer. Sometimes those hallucinations will even hit the mark. But on the whole, if a mental model is required to arrive at the answer, AI wastes more time than it saves.

Not op but yes. It sometimes takes a lot of time but I read everything. It still faster than nothing. Also, I ask very precise changes to the AI so it doesn’t generate huge diffs anyway.

Also for new code, TDD works wonders with AI : let it write the unit tests (you still have to be mindful of what you want to implement) and ask it to implement the code that run the tests. Since you talk the probabilistic output, the tool is incredibly good at iterating over things (running and checking tests) and also, unit tests are, in themselves, a pretty perfect prompt.

Opposite experience for me. It reliably fails at more involved tasks so that I don't even try anymore. Smaller tasks that are around a hundred lines maybe take me longer to review that I can just do it myself, even though it's mundane and boring.

The only time I found it useful is if I'm unfamiliar with a language or framework, where I'd have to spend a lot of time looking up how to do stuff, understand class structures etc. Then I just ask the AI and have to slowly step through everything anyways, but at least there's all the classes and methods that are relevant to my goal and I get to learn along the way.

Unless you're thinking of repetitive code I can't imagine the process (I'm not arguing, I'm just curious of what you're flow looks like).

2) I find the tool analogy helpful but it has limits. Yes, it’s a stochastic tool, but in that sense it’s more like another mind, not a tool. And this mind is neither junior nor senior, but rather a savant.

This is just an incredible statement. I can't think of another development tool we'd say this about. I'm not saying you're wrong, or that it's wrong to have tools we can't just, just... wow... what a sea change.

Imagine if your calculator app randomly and non-deterministically performed arithmetic incorrectly, and you similarly couldn't get correctness expectations from the developer.

Imagine if any of your communication tools randomly and non-deterministically translated your messages into gibberish...

I think we'd all throw away such tools, but we are expected to accept it if it's an "AI tool?"

They keep telling you that any employee who highlights problems with the tools are just trying to save their job.

Your investors tell you that the toolmakers are already saving money for your competitors.

Now, do you want that second house and white lotus vacation or not?

Making good tools is difficult. Bending perception (“is reality”) is easier and enterprise sales, just like good propaganda, work. The gold rush will leave a lot of bodies behind but the shovelmakers will make a killing.

If you think of AI like a programmer, no we shouldn't throw away such tools because we accept them as imperfect and we still need to review.

This is a common argument but I don't think it holds up. A human learns. If one of my teammates or I make a mistake, when we realize it we learn not to make that mistake in the future. These AI tools don't do that. You could use a model for a year, and it'll be just as unreliable as it is today. The fact that they can't learn makes them a nonstarter compared to humans.

Unreliable tools have a good deal of utility. That's an example of them helping reduce the problem space, but they also can be useful in situations where having a 95% confidence guess now matters more that a 99.99% confidence one in ten minutes- firing mortars in active combat, say.

There's situations where validation is easier than computation; canonically this is factoring, but even division is much simpler than multiplication. It could very easily save you time to multiply all of the calculator's output by the dividend while performing both a multiplication and a division for the 5% that are wrong.

edit: I submit this comment and click to go the front page and right at the top is Unsure Calculator (no relevance). Sorry, I had to mention this

The problem is that you don't know which 5% are wrong. The AI is confidently wrong all the time. So the only way to be sure is to double check everything, and at some point its easier to just do it the right way.

Sure, some things don't need to be perfect. But how much do you really want to risk? This company thought a little bit of potential misinformation was acceptable, and so it caused a completely self inflicted PR scandal, pissed off their customer base, and lost them a lot of confidence and revenue. Was that 5% error worth it?

Stories like this are going to keep coming the more we rely on AI to do things humans should be doing.

Someday you'll be affected by the fallout of some system failing because you happen to wind up in the 5% failure gap that some manager thought was acceptable (if that manager even ran a calculation and didn't just blindly trust whatever some other AI system told them) I just hope it's something as trivial as an IDE and not something in your car, your bank, or your hospital. But certainly LLMs will be irresponsibly shoved into all three within the next few years, if it's not there already.

No, you still have to do 100% of the work.

This is generally true when you can quantify the unreliability. E.g. random prime number tests with a specific error rate can be combined so that the error rates multiply and become negligible.

I'm not aware that we can quantify the uncertainty coming out of LLM tools reliably.

Of course there is a review system for a reason, but we frequently use "untrusted" tools in development.

That one guy in a github issue that said "this worked for me"

If there is such a tool, programmers will be on path of immediate reskilling or lose their jobs very quickly.

Because no other dev tool actually generates unique code like AI does. So you treat it like the other components of your team that generates code, the other developers. Do you trust other developers to write good code without mistakes without getting it reviewed by others. Of course not.

And then you say "by the time the rest of the team reviews it. Most code review is uneventful."

So you trust your team to develop without the need for code review but yet, your team does code review.

So what is the purpose of these code reviews? Is it the case that you actually don't think they are necessary, but perhaps management insists on them? You actually answer this question yourself:

> Most code review is uneventful.

Keyword here is "most" as opposed to "all" So based your team's applied practices and your own words, code review is for the purpose of catching mistakes and other needed corrections.

But it seems to me if you trust your team not to make mistakes, code review is superfluous.

As an aside, it seems your team culture doesn't make room for juniors because if your team had juniors I think it would be even more foolish to trust them not to make mistakes. Maybe a junior free culture works for your company, but that's not the case for every company.

My main point is code review is not superfluous no matter the skill level; junior, senior, or AI simply because everyone and every AI makes mistakes. So I don't trust those three classes of code emitters to not ever make mistakes or bad choices (i.e. be perfect) and therefore I think code review is useful.

Have some honesty and humility and you'll amazed at what's possible.

The purpose of the review is to find and fix occasional small details before it goes to physical testing. It does not involve constant babysitting of the developer. It's a little silly to bring up honesty when you spent that entire comment dancing around the reality that AI makes an inordinately large number of mistakes. I will pick the domain expert who refuses to touch AI over a generic programmer with access to it ten times out of ten.

The entire team as it is now (me included) were juniors. It's a traditional engineering environment in a location where people don't aggressively move between jobs at the drop of a hat. You don't need to constantly train younger developers when you can retain people.

> I "trust other developers to write good code without mistakes without getting it reviewed by others". Of course I can trust them to do the right thing even when nobody's looking, and review it anyway in the off-chance they overlooked something.

You're saying yes, I trust other developers to not make mistakes, but I'll check anyways in case they do. If you really trusted them not to make mistakes, you wouldn't need to check. They (eventually) will. How can I assert that? Because everyone makes mistakes.

It's absurd to expect anyone to not make mistakes. Engineers build whole processes to account for the fact that people, even very smart people make mistakes.

And it's not even just about mistakes. Often times, other developers have more context, insight or are just plain better and can offer suggestions to improve the code during review. So that's about teamwork and working together to make the code better.

I fully admit AI makes mistakes, sometimes a lot of them. So it needs code review . And on the other hand, sometimes AI can really be good at enhancing productivity especially in areas of repetitive drudgery so the developer can focus on higher level tasks that require more creativity and wisdom like architectural decisions.

> I will pick the domain expert who refuses to touch AI over a generic programmer with access to it ten times out of ten.

I would too, but I won't trust them not to make mistakes or occasional bad decisions because again, everybody does.

> You don't need to constantly train younger developers when you can retain people.

But you do need to train them initially. Or do you just trust them to write good code without mistakes too?

Literally yes. Test coverage and QA to catch bugs sure but needing everything manually reviewed by someone else sounds like working in a sweatshop full of intern-level code bootcamp graduates, or if you prefer an absolute dumpster fire of incompetence.

Building on AI seems more like building on a foundation of sand, or building in a swamp. You can probably put something together, but it's going to continually sink into the bog. Better to build on a solid foundation, so you don't have to continually stop the thing from sinking, so you can build taller.

Am I arguing in favor of egalitarian commit food fights with no adults in the room? Absolutely not. But demanding literally every change go through a formal review process before getting committed, like any other coding dogma, has a tendency to generate at least as much bullshit as it catches, just a different flavor.

Additionally, in the example you share, where only one person knows the context of the change, code review is an excellent tool for knowledge sharing.

[0]: https://dl.acm.org/doi/10.1145/2597073.2597076, for example

Then it's not a useful tool, and I will decline to waste time on it.

It does poorly without heavy instruction, though, especially with anything more than toy projects.

Still a valuable tool, but far from the dreamy autonomous geniuses that they often get described as.

  find -name '*somepattern*' -exec clobbering command ...

When a tool starts confidently inserting random wrong code into my 100% correct code, there's not much more I need to see to know it's not a tool for me. That's less like a tool and more like a vandal. That's not something I need in my toolbox, and I'm certainly not going to replace my other tools with it.

I was offered in writing a refund by the team who cold reached out to me to ask me why I cancelled my sub one week after start. Then they ignored my 3+ emails in response asking them to refund, and other means of trying to communicate with them. Offering me a refund as a bait to gain me back, then when I accept it they ghost me. Wow. Very low.

The product is not terrible but the team responses are. And this, if you see how they handled it, is also a very poor response. First thing you notice if you open the link is that the Cursor team removed the reddit post! As if we were not going to see it or something? Who do they think they are? Censoring bad comments which are 100% legit.

I am giving it a go to competitors just out of sheer frustration with how they handle customers, and I do recommend everybody to explore other products before you settle on Cursor. I don't intend to ever re-subscribe and have recommended friends to do the same, most of which agree with my experience.

You just don't know how to prompt it correctly.

Just because you use AI for customer service doesn't mean you don't have to honour its offers to customers. Air Canada recently lost a case where its AI offered a discount to a customer but then refused to offer it "IRL"

https://www.forbes.com/sites/marisagarcia/2024/02/19/what-ai...

Very strange behavior honestly.

Apologies - something very clearly went wrong here. We’ve already begun investigating, and some very early results:

* Any AI responses used for email support are now clearly labeled as such. We use AI-assisted responses as the first filter for email support.

* We’ve made sure this user is completely refunded - least we can do for the trouble.

For context, this user’s complaint was the result of a race condition that appears on very slow internet connections. The race leads to a bunch of unneeded sessions being created which crowds out the real sessions. We’ve rolled out a fix.

Appreciate all the feedback. Will help improve the experience for future users.

Don't use AI. Actually care. Like, take a step back, and realise you should give a shit about support for a paid product.

Don't get me wrong: AI is a very effective tool, *for doing things you don't care about*. I had to do a random docker compose change the the other day. It's not production code, it will be very obvious whether or not AI output works, and I very rarely touch docker and don't care to become a super expert in it. So I prompted the change, and it was good enough and so I ran with it.

You using AI for support tells me that you don't care about support. Which tells me whether or not I should be your customer.

Do they advertise that there's no support when you pay $20? I'm gonna take a guess that they don't.

They are getting paid by their customers and if they can't sustain their business (which includes support) with it they are under pricing their product and should have consequences for it.

A business is a business and we should stop treating startups as special. They operate on the same rules and standards that everyone else does.

I've gotten a lot of value out of it over the past year, and often feel that I'm underpaying for what I'm getting.

To me, any type of business is a business. I'd treat Cursor as special because it is special.

I agree with you, they should care.

(Today I learned)

This person is not the only one to experiencing this bug. As this thread has pointed out.

HN goes a step further. It has a function that allows moderators to kill or boost a post by subtracting or adding a large amount to the post's score. HN is primarily a place for Y Combinator to hype their latest venture, and a "safe" place for other startups and tech companies.

The team I work with it took nearly 3 months to get basic questions answered correctly when it came to a sales contract. They never gave our Sec team acceptable answers around privacy and security.

Reddit is free to play for marketing firms. Perhaps they could add extra statistics, analytics, promotions for these commercial users.

Edit: he did refund 22 mins after seeing this

Idk. It’s just growing pains. Companies that grow quickly have problems. Imma keep using https://cline.bot and Claude 3.7.

The best case scenario is that you lied about having people answer support. LLMs pretending to be people (you named it Sam!) and not labeled as such is clearly intended to be deceptive. Then you tried to control the narrative on reddit. So forgive me if I hit that big red DOUBT button.

Even in your post you call it "AI-assisted responses" which is as weaselly as it gets. Was it a chatbot response or was a human involved?

But 'a chatbot messed up' doesn't explain how users got locked out in the first place. EDIT: I see your comment about the race condition now. Plausible but questionable.

So the other possible scenario is that you tried to hose your paying customers then when you saw the blowback blamed it on a bot.

'We missed the mark' is such a trope non-apology. Write a better one.

I had originally ended this post with "get real" but your company's entire goal is to replace the real with the simulated so I guess "you get what you had coming". Maybe let your chatbots write more crap code that your fake software engineers push to paying customers that then get ignored and/or lied to when they ask your chatbots for help. Or just lie to everyone when you see blowback. Whatever. Not my problem yet because I can write code well enough that I'm embarrassed for my entire industry whenever I see the output from tools like yours.

This whole "AI" psyop is morally bankrupt and the world would be better off without it.

Also, illegal in the EU.

Seems like you are still blaming the user for his “very slow internet”.

How do you know the user internet was slow? Couldn’t a race condition like this exist anyway with regular 2 fast internet connections competing for the same sessions?

Something doesn’t add up.

this is a completely reasonable and seemingly quite transparent explaination.

if you want a conspiracy, there are better places to look.

Instead of saying "race condition that appears on very slow internet connections", you might say "race condition caused by real-world network latencies that our in-office testing didn't reveal" or some shit.

Also, from the first comment in the post:

> Unfortunately, this is an incorrect response from a front-line AI support bot.

Well, this actually hurts.. a lot! I believe one of the key pillars of making a great company is customer support, which represents the soul or the human part of the company.

Because we all know how well people pay attention to such clear labels, even seasoned devs not just “end users”⁰.

Also, deleting public view of the issue (locking & hiding the reddit thread) tells me a lot about how much I should trust the company and its products, and as such I will continue to not use them.

--------

[0] though here there the end users are devs

Slightly related to this; I just wanted to ask whether all Cursor email inboxes are gated by AI agents? I've tried to contact Cursor via email a few times in the past, but haven't even received an AI response :)

Cheers!

Are you truely that cheap? Is this why it took you guys 3 months to get a basic contract back to us?

AI fixes most of that... Most of the time? Clearly not, but hey.

That an LLM then invented a reason when asked by users why they're being logged out isn't that surprising. While not impossible, I don't think there's currently indication that they intended to change policy and are just blaming it on a hallucination as a scape goat.

And what’s a customer supposed to do with that information? Know that they can’t trust it? What’s the point then?

Note that I have absolutely no knowledge or reason to believe this other than general distrust of companies.

Yeah, who puts an AI in charge of support emails with no human checks and no mention that it's an AI generated reply in the response email?

The bot is now called "UltralyticsAssistant" and discloses that it's automated, which is welcome. The bad advice is all still there though.

(I don't know if they're really _famous_ for this, but among friends and colleagues I have talked to multiple people who independently found and were frustrated by the useless github issues.)

VibeSupport bots, how well did that work out for Canada Air?

https://thehill.com/business/4476307-air-canada-must-pay-ref...

More evidence: all of the ignorant uses of "hallucinate" here, when what's happening is FABRICATION.

I believe it’s pronounced VibeOops.

"Caution: Any of this could be wrong."

Then again paying users might wonder "what exactly am I paying for then?"

It also seems like a weird thing to lie about, since it's just another very public example of AI fucking up something royally, coming from a company whose whole business model is selling AI.

Which is crazy. Support is part of marketing so it should get the same kind of consideration.

Why do people think Amazon is hard to beat? Price? nope. Product range? nope. Delivery time? In part. The fact if you have a problem with your product they'll handle it? Yes. After getting burned multiple times by other retailers you're gonna pay the Amazon tax so you don't have to ask 10 times for a refund or be redirected to the supplier own support or some third party repair shop.

Everyone knows it. But people are still stuck on the "support is a cost center" way of life so they keep on getting beat by the big bad Amazon.

Other tickets get replied within the day.

I am also running it by myself; I wonder why big companies with 50+ employees like cursor cheaps out with support.

The thing is, what the AI hallucinated (if it was an AI-hallucinating), was the kind of sleezy thing companies do do. However, the thing with sleezy license changes is they only make money if the company publicizes them. Of course, that doesn't mean a company actually thinks that far ahead (X many managers really think "attack users ... profit!"). Riddles in enigmas...

I'll just keep chugging along, with debian, python and vim, as I always have. No LLM, no LSP, heck not even autocompletion. But damn proud of every hand crafted, easy to maintain and fully understood line of code I'll write.

Now, I don't trust the output - I review everything, and it often goes wrong. You have to know how to use it. But I would never go back. Often it comes up with more elegant solutions than I would have. And when you're working with a new platform, or some unfamiliar library that it already knows, it's an absolute godsend.

I'm also damn proud of my own hand-crafted code, but to avoid LLMs out of principal? That's just luddite.

20+ years of experience across game dev, mobile and web apps, in case you feel it relevant.

Getting to sit down and write the code is the most enjoyable part of the job, why would I deprive myself of that? By the time the problem has been defined well enough to explain it to an LLM sitting down and writing the code is typically very simple.

Code review is difficult to get right, especially if the goal is judging correctness. Maybe this is a personal failing, but I find being actively engaged to be a critical part of the process; the more time I spend with the code I'm maintaining (and usually on call for!) the better understanding I have. Tedium can sometimes be a great signal for an abstraction!

Don't get me started on testcase generation.

What I've found frustrating about the narrative around these tools; I've watched them from afar with intrigue but ultimately found that method of working just isn't for me. Over the years I've trialed more tools than I can remember and adopted the ones I found useful, while casting aside ones that aren't a great fit. Sometimes I find myself wandering back to them once they're fully baked. Maybe that will be the case here, but is it not valid to say "eh...this isn't it for me"? Am I kidding myself?

I can imagine that LLM is really helpful in some cases for some people. But so far, I couldn’t find a single example when I and simple copy-pasting wouldn’t have been faster. Not even when I tried it, not when others showed me how to use it.

And yet the time it takes me to use the LLM and correct its output is usually faster than not using it at all.

Over time I've developed a good sense for what tasks it succeeds at (or is only trivially wrong) and what tasks it's just not up for.

Like how McDonalds makes a lot of burgers fast and they are very successful so that's all we really care about?

The folly of single ended metrics.

> but to avoid LLMs out of principal? That's just luddite.

Do you double check that the LLM hasn't magically recreated someone else's copyrighted code? That's just irresponsible in certain contexts.

> in case you feel it relevant.

Of course it's relevant. If a 19 year old with 1 year of driving experience tries to sell me a car using their personal anecdote as a metric I'd be suspicious. If their only salient point is that "it gets me to where I'm going faster!" I'd be doubly suspicious.

I frankly do not care, and I expect LLMs to become such ubiquitous table-stakes that I don't think anyone will really care in the long run.

Unless they develop entirely new technology they're stuck with linear growth of output capability for input costs. This will take a very long time. I expect it to be abandoned in favor of better ideas and computing interfaces. "AI" always seems to bloom right before a major shift in computing device capability and mobility and then gets left behind. I don't see anything special about this iteration.

> that I don't think anyone will really care in the long run.

There are trillions of dollars at stake and access to even the basics of this technology is far from egalitarian or well distributed. Until it is I would expect people who's futures and personal wealth depends on it to care quite a bit. In the meanwhile you might just accelerate yourself into a lawsuit.

In python I was scanning 1000’s of files each for thousands of keywords. A naive implementation took around 10 seconds, obviously the largest share of execution time after running instrumentation. A quick ChatGPT led me to Aho-Corasick and String searching algorithms, which I had never used before. Plug in a library and bam, 30x speed up for that part of the code.

I could have asked my knowledgeable friends and coworkers, but not at 11PM on a Saturday.

I could have searched the web and probably found it out.

But the LLM basically auto completed the web, which I appreciate.

Once I had to look up a research paper to implement a computational geometry algorithm because I couldn't find it any of the typical Web sources. There were also no library to use with a license for our commercial use.

I'm not against use of "AI". But this increasing refusal of those who aspire to work in specialist domains like software development to systematically learn things is not great. That's just compounding on an already diminished capacity to process information skillfully.

Many developers use libraries effectively without knowing every time consideration of O(n) comes into play.

Competently implemented, in the right context, LLMs can be an effective form of abstraction.

Get friends with weirder daily schedules. :-)