8 ms·

There are two kinds of engineers. Those who can’t stop raving about how much of a superpower LLMs are for coding, how it’s made them 100x more productive, and

by gyomu 1y ago

There are two kinds of engineers. Those who can’t stop raving about how much of a superpower LLMs are for coding, how it’s made them 100x more productive, and is unlocking things they could’ve never done before. And those who, like you, find it to be an extremely finicky process that requires extreme amount of coddling to get average results at best. The only thing I don’t understand is why people from the former group aren’t all utterly dominating the market and obliterating their competitors with their revolutionary products and blazing fast iteration speed.

lm28469 1y ago
That's because they fall under the "engineers" category like my local postman and Michael Schumacher both fall under the "driver" category. Most people don't work on anything technically hard, most problems are business logic issues that aren't solved technically or legacy code workarounds for which you need to put 3-10 domain experts in a room for a few hours to solve.
- nobodywillobsrv 1y ago
  So true and it makes me feel better to have this mental model. I find it useful for simple things that are perhaps just beyond search and copy paste but sometimes wonder if the more technical stuff is not even quite right as it is possible to sort of brainwash yourself into not thinking clearly when nagging an llm into being reasonable on something it has a hard time with.
- sunrunner 1y ago
  There are a lot of terms in software development that have been co-opted from other disciplines and misrepresent a lot of development work, including 'engineering' and 'architecture'. I think it's helpful to think of engineering as a _process_ instead of a role, and the reality is that a lot of development work doesn't necessarily rely on the strong engineering methodology (e.g. measurement, material properties, tolerances, modelling, etc.) that the people developing the software might imagine just based on the number of job adverts for 'engineers'. This isn't a bad thing. There are hundreds or thousands of different but equally valid solutions to getting a program to do a thing, but not recognising that most code writing is somewhere between a art and engineering and is neither a purely artistic discipline but also rarely a purely engineering one is useful. The kinds of engineering and architecture that people think of in software development only really represent common practices and shared language (e.g. design patterns, architectural patterns) and not a strong engineering practice or any kind of truth about how software actually runs. (Unless you're writing software for launching rockets, in which case the engineering _process_ porbably should be strong).
  - cassianoleal 1y ago
    > the reality is that a lot of development work doesn't necessarily rely on the strong engineering methodology (e.g. measurement, material properties, tolerances, modelling, etc.) It's probably true that a lot of development work doesn't rely on those. It's probably also true that the work other kinds of engineers do also don't. That said, when engineering software systems, those are very important. Measurement: resource sizing, observability; tolerances: backoffs, back pressure, queues, topics, buffers; modelling: types, syntax, data analytics... There's a whole class of developers out there that are not aware or very good at those. And that's fine. There's a place for them in the market. You don't need an engineer to work on your floor joists or your plumbing. Sure you can have one, but you can also hire a builder or DIY it all yourself.
- voidhorse 1y ago
  Completely agree. If anything LLMs have just made this completely transparent and are revealing that actual engineering in software is limited to rare cases. All of theses middling developers who are so excited about these tools don't seem to realize that they are perfectly primed to eventually eliminate precisely the kind of worker responsible for hooking up existing APIs for solved problems into a web app, the kind of work the market hitherto greatly over esteemed and overpaid for isn't my going to be so esteemed or highly valued anymore. The only real work that will remain is actual hard engineering work (solving novel technical modeling problems, not just plugging apis together). All of these lazy devs are hyped for precisely the automation that's goin. to significantly reduce their pay and labor prospects in the long term lol. I'm shocked at times at how people can fail to see the writing on the wall when it's been written in gargantuan red ink
  - esafak 1y ago
    Software engineering work never ends, so they'll just work faster.
- mbac32768 1y ago
  Right yes, I imagine most people who program are in companies that have nothing to do with high tech, writing SQL or scripts or CRUD apps. LLMs are probably amazing here.
  - t917910 1y ago
    Writing SQL in the context of 20 years old application with 2500 stored procedures just inside one of many databases is not so easy for LLM.
SatvikBeri 1y ago
I find LLMs make me about 10% more productive – which is great, but nowhere near as extreme as some claims. Cursor autocompletes a lot of stuff I would have typed anyway, and LLM search is a strong complement to Google, though not a direct replacement. Generating more than a few lines usually gives me bad code, but often suggests libraries I didn't know about, especially outside my area of expertise. There's just not a lot of people talking about middling results because the extremes tend to eat up the conversation.
- codr7 1y ago
  It's not a free lunch though, you're sacrificing reinforcement learning and becoming more dependent on your tools.
- jimbokun 1y ago
  I do find AI a replacement to Google most of the time, because now when I search on Google the Gemini results at the top generally give me what I wanted to know.
loveparade 1y ago
That's because the reality is somewhere in the middle. It obviously isn't 100x or even 10x except for very specific toy tasks, but for me it's probably around ~1.5-2x in everyday work. And I work on mostly long-tail scientific stuff, I imagine it's more if you do something like frontend dev with a lot of boilerplate. I'm absolutely convinced that people who say LLMs are not making them more productive either don't understand how to use them correctly, or they are working on very specific niche problems/infrastructure where they aren't useful. 2x may not sound like much compared to what you read in the media, but if a few years ago you had told companies that you can provably make their engineers even 2x more productive on average you'd probably be swimming in billions of dollars now. That kind of productivity increase is absolutely huge.
- tonyhart7 1y ago
  "That kind of productivity increase is absolutely huge." then company decide to hire less, the market flooded with junior AI powered skill, mid level programmer cant asking rise as easy anymore unless you are senior dealing with really specific stuff that need know how
- codr7 1y ago
  2x is also .5x in learning, nothing is free in this world.
- bluefirebrand 1y ago
  > I'm absolutely convinced that people who say LLMs are not making them more productive either don't understand how to use them correctly, or they are working on very specific niche problems/infrastructure where they aren't useful I understand how to use LLMs, it's just a much worse workflow. Writing code isn't the hard part, reviewing code is much slower and painful process than writing it from scratch, when you actually know what you're doing. I'm absolutely convinced that people who are saying LLMs are making them more productive either weren't very productive to begin with or they don't know what they're doing enough to realize what a bad job the LLM is doing
- FiberBundle 1y ago
  Where's the actual evidence for the productivity boost though? Wouldn't one expect a huge increase in valuable software products or a dramatic increase in open source contributions if llms provide this kind of productivity increase?
bobxmax 1y ago
They are. People are building sophisticated products in days not months now. If you can't see that, it's your issue.
- gyomu 1y ago
  Would you be so kind as to link to some of these sophisticated products? Would love to check them out.
  - bobxmax 1y ago
    Go look at the latest YC batch. 30% of them were built with 90% of the code being written by LLMs.
    - Jensson 1y ago
      Where did you see that stat? And why just 30%, shouldn't almost all of them be built by LLM if LLM is that much better? Edit: This says a quarter, where did you get the 30% figure from? https://techcrunch.com/2025/03/06/a-quarter-of-startups-in-ycs-current-cohort-have-codebases-that-are-almost-entirely-ai-generated/ https://techcrunch.com/2025/03/06/a-quarter-of-startups-in-y...
      - bookofjoe 1y ago
        Are you seriously pointing out the difference between 25% and 30% as meaningful?
      - bobxmax 1y ago
        wow 25% not 30% ya sure got me > shouldn't almost all of them be built by LLM if LLM is that much better a) not every yc company is a software company b) these things take time, and not everyone knows how to use these tools this stuff should be pretty self evident.
      - viridian 1y ago
        Perhaps bobxmax should have asked an LLM what the exact number was instead of relying on his outdated & error prone human memory.
deleted 1y ago
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melvinroest 1y ago
I'm learning math through Math Academy, ChatGPT and YouTube (mostly 3Blue1Brown and similar channels). Without that specifc combination, it would've been hell. Now it's just nice. A few years I did it with something similar to Math Academy (from the Universiteit of Amsterdam). ChatGPT wasn't intelligent enough back then so I didn't use it. It felt a lot tougher. ChatGPT answers questions that a teacher would find obscure nonsense, but I'm the type of student that needs to know the culture behind math so I can empathize with the people doing it and can think of solutions via that way. Like why is the letter m used when talked about an angle? Mathematically, it shouldn't matter. But it irritates me as I'd use a instead, it makes more sense as you want to lower the cognitive burden and focus as much on the math as possible. So I ask ChatGPT and one of the things it said is that historically it meant "measure". Now, I get it and can focus on the actual math again. Another example is the fast variance calculation: average_of_squares - square_of_the_average how does that come from (1/n) * sum((x - mean)^2) ? It just shows it. World domination? Nope, but I'm learning something I otherwise wouldn't. But as you can tell, YouTube and Math Academy play their own roll in it.
- namaria 1y ago
  > So I ask ChatGPT and one of the things it said is that historically it meant "measure" This is a great example of why using LLMs to learn is perilous. This is nonsense.
  - melvinroest 1y ago
    Is it? I didn't bother to check as it wasn't too relevant. I wasn't stuck anymore. Worst-case it's a memnonic that is untrue. Best case: it is. For actual derivations I check the work. This is what it wrote by the way. I posted an image and asked: Why is this m m for angle mangle? ChatGPT: In geometry we often need to distinguish the angle itself (a geometric object) from the numerical size of that angle (a real-number measurement, usually in degrees or radians). To keep the two ideas separate we put a little “m” in front of the angle symbol: m∠B reads as “the measure of angle B.” * ∠B refers to the actual wedge-shaped region determined by the two rays that meet at vertex . * m∠B is the single number—e.g. \90∘—that tells you how “wide” that wedge is. So in the worked example the author writes m∠A + m∠B + m∠C + m∠D = 360∘ because they are adding the sizes of the four interior angles of a quadrilateral, not the angles themselves. If you dropped the m you’d be adding geometric objects, which doesn’t make sense; the m reminds the reader that what’s being added are real-number measures. (Some textbooks use ∣∠B∣ or simply write the letter with a bar over it, but “m∠” is by far the most common in U.S. and many international curricula.) So the mysterious “m” isn’t another variable—just a shorthand for “measure of.” --- Edit: this took quite some work to write well as ChatGPT answers don't copy/paste neatly into HN. So I won't be doing this for the other example.
Philip-J-Fry 1y ago
I find LLMs 100x more productive for greenfield work. If I want to create a React app with X amount of pages, some Redux stores, Auth, etc. then it can smash that out in minutes. I can say "now add X" and it'll do it. Generally with good results. But when it comes to maintaining existing systems, or adding more complicated features, or needing to know business domain details, a LLM is usually not that great for me. They're still great as a code suggestion tool, finishing lines and functions. But as far as delivering whole features, they're pretty useless once you get past the easy stuff. And you'll spend as much time directing the LLM to do this kind of this as you would just writing it yourself. What I tend to do is write stubbed out code in the design I like, then I'll get an LLM to just fill in the gaps. These people who say LLMs make them 100x more productive probably are only working on greenfield stuff and haven't got to the hard bit yet. Like everyone says, the first 90% is the easy bit. The last 10% is where you'll spend most of your time, and I don't see LLMs doing the hard bit that well currently.
- joshstrange 1y ago
  I couldn’t agree more and I’ve said the same thing many times. I have yet to see an LLM-generated app not collapse under it’s own weight after enough iterations/prompts. It gets stuck in loops (removing and adding the same code/concept), it gets hung up on simple errors, etc. For greenfield it’s amazing, no doubt, but unless you are watching it very closely and approving/reviewing the code along the way it will go off the rails. At a certain point it’s easier to add the new feature or make the modification yourself. Even if the LLM could do it, it would burn tons of money and time. I expect things to get better, this will not always be the state of things, but for now “vibe coding” (specifically not reviewing/writing code yourself) is not sustainable. Most people doing it have a github profile that is a mile wide and a meter deep. LLM’s are amazing and useful, but “vibe coding” with them is not sustainable currently.
  - RajT88 1y ago
    Coding LLM's: Ruby off rails?
  - bredren 1y ago
infecto 1y ago
You’ll usually find three kinds of people talking about LLMs online: 1. The ones writing Medium posts about being 100x more productive. 2. The skeptics, like yourself, who construct made-up examples to prove it’s impossible to get value, framing things in a way where failure is the only plausible outcome. 3. And then there’s the middle group, people quietly getting real, uneven, but worthwhile value. You’d be surprised. The vast majority of engineering isn’t that unique. Usefulness often depends on the age of the codebase and the dynamics of the team. I’m firmly in the middle camp: far from perfect, but I get real value from these tools as they stand today. It’s like having a junior engineer who can scaffold out the basics so I can spend my mental energy on the parts that actually matter.
- soraminazuki 1y ago
  GP didn't "construct made-up examples" anywhere in this thread as of the time of writing this post. Where did you hallucinate that from? Seriously, why is every comment hyping up AI generated code like this. No concrete evidence, just turtles all the way down. But when actual examples do show up, it's a complete clown show [1][2]. [1]: https://news.ycombinator.com/item?id=44050152 https://news.ycombinator.com/item?id=44050152 [2]: https://news.ycombinator.com/item?id=43907376 https://news.ycombinator.com/item?id=43907376
  - infecto 1y ago
    He definitely crafted the narrative he wanted, you see that just as much from skeptics as from the hype crowd. I still stand by what I said: there are always three camps in any LLM discussion. No hallucinations here, I can see where you land. I’m here for a constructive argument, and I believe there’s a real middle ground where LLMs are creating tangible value, even for experienced engineers. Your links about GitHub agents may be spot-on, I haven’t used them myself, so no strong opinion there. Same with Codex; it’s early. But why not mention tools like Cursor or Windsurf? Or the open source alternatives. That’s where people are seeing actual gains today. Why go all-in on anti-hype without talking about your direct experience?
    - 1y ago
traceroute66 1y ago
> And those who, like you, find it to be an extremely finicky process that requires extreme amount of coddling to get average results at best. I am very much in that category. I would describe my experience as the old adage "why keep a dog and bark yourself". And no, I don't buy into people who say you have to engage in "prompt engineering". That's bullshit. Let's face it "prompt engineering" is a synonym for "I have to waste time coming up with hacky workarounds to make this dumb LLM come up with something usable". In the end, its always quicker and easier to do it myself. And unless you are fresh out of school, if you've got more than 10 minutes of coding experience, you will always do a better job than the LLM. Every time a new LLM model comes out, I fall into the same old trap "oh well, this one must be better, surely" I say to myself. And yet its always the same "you can't polish a turd but you can roll it in glitter" experience. I'm sick and tired of LLMs hallucinating shit. I'm sick and tired of LLMs inventing functions that have been available in stdlib for years. I'm sick and tired of LLMs generating useless code with boilerplate subfunctions that just contain a single commented "implement it yourself" line. On top of that LLMs are simply terrible for the environment, guzzling up all that electricity, producing nothing but hot air and bullshit results in return. Basically I'm sick and tired of LLMs and all the LLM fetishists that surround them.
exe34 1y ago
I've managed to use ChatGPT to write a small app in Kotlin, and later used Cursor to fix some issues around performance. Personally I'm not a finish-it kind of guy unless I'm getting paid for it. To be fair, I'm not sure the quality of the code is much worse than all the boatloads of crapware in all the app stores out there. I suspect that the reason "revolutionary products" and "blazing fast iteration" isn't blowing our minds is because the problem was never the code - it was always the people around it, who know better than their customers. The best way to make good software is to test it on real people and iterate - that's going to take a long time no matter how fast the coding part is.
dogtierstatus 1y ago
This is a quality vs quantity issue. There were x number of extremely competent and ~10x moderately competent programmers. LLMs have only made this to 100x moderately competent programmers and may be 2xed extremely competent ones?
- analog31 1y ago
  Even 2x would be an improvement. It would mean that a project goes from being 3 years late, to only 1.5 years late.
binary132 1y ago
I think a big part of the problem is the devx / workflows that these tools tend to encourage or impose. Watching my coworkers fumble with terrible copilot autoshit in a pairing session makes me want to scream on a daily basis. That doesn’t necessarily mean such a DX is the only way it could or should work, or that it’s even a good DX at all. Most of the time, it actually imposes more cost, both in lost momentum and in corrective action, than it relieves. I think something like a graph where nodes are specified clearly with inputs and outputs (or pre- and post-conditions, if you prefer), and agents iteratively generate and refine the implementation details could have some utility, but that would require programmers to know how to think about program design (and have a usable UI/UX for such a tool) instead of hastily stumbling through patches and modifications to existing code they already don’t understand. The new LLM systems are carrying us farther from that world, not closer to it.
kordlessagain 1y ago
“If we do not invent the future, someone else will do it for us - and it will be their future, not ours.” – Duncan D. Bruce & Dr. Geoff Crook, The Dream Café
wooque 1y ago
Because code is small part of making successful business and usually not the bottleneck.
michaelrpeskin 1y ago
A little snarky but: In my experience, the folks who are 100x more productive are multiplying 100 times a small number. I've found great success with LLMs in the research phase of coding. Last week I needed to write some domain-specific linear algebra and because of some other restrictions, I couldn't just pull in LAPACK. So I had to hand code the work (yes, I know you shouldn't hand code this kind of stuff, but it was a small slice and the domain didn't require the fully-optimized LAPACK stuff). I used an LLM to do the research part that I normally would have had to resort to a couple of math texts to fully understand. So in that case it did make me 100x more effective because it found what I needed and summarized it so that I could convert it to code really quickly. For the fun of it, I did ask the LLM to generate the code for me too, and it made very subtle mistakes that wouldn't have been obvious unless you were already an expert in the field. I could see how a junior engineer would have been impressed by it and probably just check it in and go on. I'm still a firm believer in understanding every bit of code you check in, so even if LLMs get really good, the "code writing" part of my work probably won't ever get faster. But for figuring out what code to write - I think LLMs will make people much faster. The research and summarize part is amazing. The real value in the world is synthesis and novel ideas. And maybe I'm a luddite, but I still think that takes human creativity. LLMs will be a critical support structure, but I'm not sold on them actually writing high-value code.
- data-ottawa 1y ago
  They definitely help raise the floor, but have a much lower ceiling. If you’re near that ceiling you get most value out of code reviews and those side features you don’t care about that allow you to focus more on the actual problem. That’s a 10-20% lift instead of 10/100x.
- ryandrake 1y ago
  To steal the joke from that vintage coffee tin: LLMs help you "do stupid things faster." If all you want to do is dump terrible code into the world as fast as possible, then you've got a great tool there. If you're looking to slowly and carefully build a quality product on a solid foundation, or create a programming work of art, LLMs are at best a marginally better autocomplete.
- 1y ago
jghn 1y ago
> There are two kinds of engineers. Apparently there are at least three as I fit neither of these molds of yours. They are neither making me 100x more productive, nor am I putting in an extreme amount of coddling. For context, in the 30ish years I've been writing code professionally, I've *always* needed to look stuff up constantly. I know what I want, and I know what's possible. I just can never remember things like syntax, specifics, that sort of thing. It doesn't help that I often wind up in jobs where I'm switching between languages often. Before quality search engines like Google, this meant I was always flipping through reference books, man pages, and worse. Then came search engines and that was a step more productive. Then came things like SO, and that was a step more productive. I find LLMs to be another step forward, but not like a quantum leap or anything. Take autocomplete suggestions for instance: Sometimes it's exactly the prompt (pardon the pun) that I need to jog my memory. Other times I know it's not what i want and I ignore it. And the chat interfaces I find better than Googling SO as I can have a back & forth with it.
- mattfca 1y ago
  this describes me and my experience exactly
GenerocUsername 1y ago
At a macro level, people from the former group might be doing better than you think. I have gone from Senior II, to Lead Architect, to Principal Engineer in the years LLMs have been available. It's not literally a 100x multiplier, but I have leveraged their ability to both fill in details where I lack specific knowledge, and as a first point of contact when researching a new space. LLMs have enabled me to widen my sphere of influence by deepening my abilities at every point of my T-shaped knowledge curve.
- codr7 1y ago
  Except you're not deepening anything, rather becoming increasingly dependent on wacky tools to keep up your fake status. Go for it, but it's a recipe for disaster imo.
conductr 1y ago
My guess is it depends on what you work with. I’ve been impressed by my ability to spin up nice looking websites and landing pages. Yet, I’ve tried to build a game and despite it appearing to work, it eventually has gotten confused about its own codebase and I end up with a ball of spaghetti where requirements of yesterday get lost or broken when adding functionality today. It feels more like I’m chasing my tail, vibe coding as they say, it’s just seems endlessly circular. My game dev is going to be a hybrid approach going forward. I’ll ask it to create a new project with the desired functionality and see how the solution was proposed and then incorporate that into my main code branch. I find this easier than the Cursor roll back or being overly cautious with suggested edits. I’m just getting to this realization so tbd if it works well. FYI, I’m good at programming generally but game dev is my Achilles. We’ll see how it it goes.
peacebeard 1y ago
I don’t fit in either category. My experience is that LLMs are good at writing code that is easy to write. This is not game-changing, but it is useful.
emp17344 1y ago
The fact that no one can agree on how useful these things are makes me think they aren’t that useful.
slightwinder 1y ago
Even if someone seriously works 100 times faster with AI, it doesn't mean they can easily dominate any established market. A product is more than just code, and a company is more than their headcount. There is domain knowledge about the product and market. There is the personal ability of the workers and the synergy coming with, also the strength of established structures grown over years and decades. Just the habit and trust of users is a big fortress every new product has to conquer. That's not something some random dev or team of devs can easily reach just by being 100 times faster. And realistically, there is also the question of where this claim is coming from. Someone being 100 times faster with AI is probably not starting from a place of high competence. Of the 100x, 90x are probably just for filling the gap to peak devs. So at the end they would be probably as productive as a highly competent dev of some domain, just that they can more easily access any domain without years of experience in that domain. And if we are honest, we somewhat have that already without AI, just by people copying and cookie cutting any sh*t
max_on_hn 1y ago
(disclaimer: I have a vested interest in the space as the purveyor of an AI software development agent) The result you described is coming soon. CheepCode[0] agents already produce working code in a satisfying percentage of cases, and I am at most 3 months away from it producing end-to-end apps and complex changes that are at least human-quality. It would take way less if I got funded to work on it full time. Given that I'm this close as a solo founder with no employees, you can imagine what's cooking inside large companies. [0] My product, cloud-based headless coding agents that connect directly to Linear, accept tickets, and submit GitHub PRs
- johnecheck 1y ago
  "At most 3 months away" This is a strong claim. I'm not aware of any AI model or system that can consistently and correctly make complex changes in existing codebases. What makes you so confident that you can achieve this, alone, in the next 3 months?
  - t917910 1y ago
    The circumference of a circle is easily calculated as piradius2, so calculating the same length of an ellipse would not be much more difficult. Hehe.
sanderjd 1y ago
Nah, there's a third kind, which is probably the quieter majority. These are people who find these to be useful but imperfect tools, whose expectations remain realistic, and for whom the continuing progress these tools are making is pleasant and not exactly surprising but also not taken as a given. I'm finding that these tools are supporting an ever expanding portion of my work, while remaining frustrating frequently.
tom_m 1y ago
It's a good sounding board and productivity tool...but it's over hyped. I think it'll calm down a bit. People are just making huge claims right now which is music to the ears of many. I like the tools don't get me wrong, but they aren't going to make a huge difference day to day. The area they will make a huge difference is in helping people work out issues faster. You know those multi-hour or multi-day issues you run into where you go crazy reading and re-reading the code. The LLMs are good at giving you a fresh look at the code that you'd otherwise need to get more people to help with or go sleep on it for a day. This time savings here is probably the most significant time savings AI code editors or agents will ever provide.
gofreddygo 1y ago
There's a third kind that agrees these are stupid but strongly believes, beyond doubt, they'll be 100x better in 2 years. Everything AI generated today is hot garbage but hey, just wait 2 years and the road to eldorado will show itself. pfft.
jf22 1y ago
There is a gradient in here. The difference between the two isn't that extreme.

by gyomu 1y ago

lm28469 1y ago

That's because they fall under the "engineers" category like my local postman and Michael Schumacher both fall under the "driver" category. Most people don't work on anything technically hard, most problems are business logic issues that aren't solved technically or legacy code workarounds for which you need to put 3-10 domain experts in a room for a few hours to solve.

nobodywillobsrv 1y ago
So true and it makes me feel better to have this mental model. I find it useful for simple things that are perhaps just beyond search and copy paste but sometimes wonder if the more technical stuff is not even quite right as it is possible to sort of brainwash yourself into not thinking clearly when nagging an llm into being reasonable on something it has a hard time with.
sunrunner 1y ago
There are a lot of terms in software development that have been co-opted from other disciplines and misrepresent a lot of development work, including 'engineering' and 'architecture'. I think it's helpful to think of engineering as a _process_ instead of a role, and the reality is that a lot of development work doesn't necessarily rely on the strong engineering methodology (e.g. measurement, material properties, tolerances, modelling, etc.) that the people developing the software might imagine just based on the number of job adverts for 'engineers'. This isn't a bad thing. There are hundreds or thousands of different but equally valid solutions to getting a program to do a thing, but not recognising that most code writing is somewhere between a art and engineering and is neither a purely artistic discipline but also rarely a purely engineering one is useful. The kinds of engineering and architecture that people think of in software development only really represent common practices and shared language (e.g. design patterns, architectural patterns) and not a strong engineering practice or any kind of truth about how software actually runs. (Unless you're writing software for launching rockets, in which case the engineering _process_ porbably should be strong).
- cassianoleal 1y ago
  > the reality is that a lot of development work doesn't necessarily rely on the strong engineering methodology (e.g. measurement, material properties, tolerances, modelling, etc.) It's probably true that a lot of development work doesn't rely on those. It's probably also true that the work other kinds of engineers do also don't. That said, when engineering software systems, those are very important. Measurement: resource sizing, observability; tolerances: backoffs, back pressure, queues, topics, buffers; modelling: types, syntax, data analytics... There's a whole class of developers out there that are not aware or very good at those. And that's fine. There's a place for them in the market. You don't need an engineer to work on your floor joists or your plumbing. Sure you can have one, but you can also hire a builder or DIY it all yourself.
voidhorse 1y ago
Completely agree. If anything LLMs have just made this completely transparent and are revealing that actual engineering in software is limited to rare cases. All of theses middling developers who are so excited about these tools don't seem to realize that they are perfectly primed to eventually eliminate precisely the kind of worker responsible for hooking up existing APIs for solved problems into a web app, the kind of work the market hitherto greatly over esteemed and overpaid for isn't my going to be so esteemed or highly valued anymore. The only real work that will remain is actual hard engineering work (solving novel technical modeling problems, not just plugging apis together). All of these lazy devs are hyped for precisely the automation that's goin. to significantly reduce their pay and labor prospects in the long term lol. I'm shocked at times at how people can fail to see the writing on the wall when it's been written in gargantuan red ink
- esafak 1y ago
  Software engineering work never ends, so they'll just work faster.
mbac32768 1y ago
Right yes, I imagine most people who program are in companies that have nothing to do with high tech, writing SQL or scripts or CRUD apps. LLMs are probably amazing here.
- t917910 1y ago
  Writing SQL in the context of 20 years old application with 2500 stored procedures just inside one of many databases is not so easy for LLM.

SatvikBeri 1y ago

I find LLMs make me about 10% more productive – which is great, but nowhere near as extreme as some claims. Cursor autocompletes a lot of stuff I would have typed anyway, and LLM search is a strong complement to Google, though not a direct replacement. Generating more than a few lines usually gives me bad code, but often suggests libraries I didn't know about, especially outside my area of expertise. There's just not a lot of people talking about middling results because the extremes tend to eat up the conversation.

codr7 1y ago
It's not a free lunch though, you're sacrificing reinforcement learning and becoming more dependent on your tools.
jimbokun 1y ago
I do find AI a replacement to Google most of the time, because now when I search on Google the Gemini results at the top generally give me what I wanted to know.

loveparade 1y ago

That's because the reality is somewhere in the middle. It obviously isn't 100x or even 10x except for very specific toy tasks, but for me it's probably around ~1.5-2x in everyday work. And I work on mostly long-tail scientific stuff, I imagine it's more if you do something like frontend dev with a lot of boilerplate. I'm absolutely convinced that people who say LLMs are not making them more productive either don't understand how to use them correctly, or they are working on very specific niche problems/infrastructure where they aren't useful. 2x may not sound like much compared to what you read in the media, but if a few years ago you had told companies that you can provably make their engineers even 2x more productive on average you'd probably be swimming in billions of dollars now. That kind of productivity increase is absolutely huge.

tonyhart7 1y ago
"That kind of productivity increase is absolutely huge." then company decide to hire less, the market flooded with junior AI powered skill, mid level programmer cant asking rise as easy anymore unless you are senior dealing with really specific stuff that need know how
codr7 1y ago
2x is also .5x in learning, nothing is free in this world.
bluefirebrand 1y ago
> I'm absolutely convinced that people who say LLMs are not making them more productive either don't understand how to use them correctly, or they are working on very specific niche problems/infrastructure where they aren't useful I understand how to use LLMs, it's just a much worse workflow. Writing code isn't the hard part, reviewing code is much slower and painful process than writing it from scratch, when you actually know what you're doing. I'm absolutely convinced that people who are saying LLMs are making them more productive either weren't very productive to begin with or they don't know what they're doing enough to realize what a bad job the LLM is doing
FiberBundle 1y ago
Where's the actual evidence for the productivity boost though? Wouldn't one expect a huge increase in valuable software products or a dramatic increase in open source contributions if llms provide this kind of productivity increase?

bobxmax 1y ago

They are. People are building sophisticated products in days not months now. If you can't see that, it's your issue.

gyomu 1y ago
Would you be so kind as to link to some of these sophisticated products? Would love to check them out.
- bobxmax 1y ago
  Go look at the latest YC batch. 30% of them were built with 90% of the code being written by LLMs.
  - Jensson 1y ago
    Where did you see that stat? And why just 30%, shouldn't almost all of them be built by LLM if LLM is that much better? Edit: This says a quarter, where did you get the 30% figure from? https://techcrunch.com/2025/03/06/a-quarter-of-startups-in-ycs-current-cohort-have-codebases-that-are-almost-entirely-ai-generated/ https://techcrunch.com/2025/03/06/a-quarter-of-startups-in-y...
    - bookofjoe 1y ago
      Are you seriously pointing out the difference between 25% and 30% as meaningful?
    - bobxmax 1y ago
      wow 25% not 30% ya sure got me > shouldn't almost all of them be built by LLM if LLM is that much better a) not every yc company is a software company b) these things take time, and not everyone knows how to use these tools this stuff should be pretty self evident.
    - viridian 1y ago
      Perhaps bobxmax should have asked an LLM what the exact number was instead of relying on his outdated & error prone human memory.

deleted 1y ago

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melvinroest 1y ago

I'm learning math through Math Academy, ChatGPT and YouTube (mostly 3Blue1Brown and similar channels). Without that specifc combination, it would've been hell. Now it's just nice. A few years I did it with something similar to Math Academy (from the Universiteit of Amsterdam). ChatGPT wasn't intelligent enough back then so I didn't use it. It felt a lot tougher. ChatGPT answers questions that a teacher would find obscure nonsense, but I'm the type of student that needs to know the culture behind math so I can empathize with the people doing it and can think of solutions via that way. Like why is the letter m used when talked about an angle? Mathematically, it shouldn't matter. But it irritates me as I'd use a instead, it makes more sense as you want to lower the cognitive burden and focus as much on the math as possible. So I ask ChatGPT and one of the things it said is that historically it meant "measure". Now, I get it and can focus on the actual math again. Another example is the fast variance calculation: average_of_squares - square_of_the_average how does that come from (1/n) * sum((x - mean)^2) ? It just shows it. World domination? Nope, but I'm learning something I otherwise wouldn't. But as you can tell, YouTube and Math Academy play their own roll in it.

namaria 1y ago
> So I ask ChatGPT and one of the things it said is that historically it meant "measure" This is a great example of why using LLMs to learn is perilous. This is nonsense.
- melvinroest 1y ago
  Is it? I didn't bother to check as it wasn't too relevant. I wasn't stuck anymore. Worst-case it's a memnonic that is untrue. Best case: it is. For actual derivations I check the work. This is what it wrote by the way. I posted an image and asked: Why is this m m for angle mangle? ChatGPT: In geometry we often need to distinguish the angle itself (a geometric object) from the numerical size of that angle (a real-number measurement, usually in degrees or radians). To keep the two ideas separate we put a little “m” in front of the angle symbol: m∠B reads as “the measure of angle B.” * ∠B refers to the actual wedge-shaped region determined by the two rays that meet at vertex . * m∠B is the single number—e.g. \90∘—that tells you how “wide” that wedge is. So in the worked example the author writes m∠A + m∠B + m∠C + m∠D = 360∘ because they are adding the sizes of the four interior angles of a quadrilateral, not the angles themselves. If you dropped the m you’d be adding geometric objects, which doesn’t make sense; the m reminds the reader that what’s being added are real-number measures. (Some textbooks use ∣∠B∣ or simply write the letter with a bar over it, but “m∠” is by far the most common in U.S. and many international curricula.) So the mysterious “m” isn’t another variable—just a shorthand for “measure of.” --- Edit: this took quite some work to write well as ChatGPT answers don't copy/paste neatly into HN. So I won't be doing this for the other example.

Philip-J-Fry 1y ago

I find LLMs 100x more productive for greenfield work. If I want to create a React app with X amount of pages, some Redux stores, Auth, etc. then it can smash that out in minutes. I can say "now add X" and it'll do it. Generally with good results. But when it comes to maintaining existing systems, or adding more complicated features, or needing to know business domain details, a LLM is usually not that great for me. They're still great as a code suggestion tool, finishing lines and functions. But as far as delivering whole features, they're pretty useless once you get past the easy stuff. And you'll spend as much time directing the LLM to do this kind of this as you would just writing it yourself. What I tend to do is write stubbed out code in the design I like, then I'll get an LLM to just fill in the gaps. These people who say LLMs make them 100x more productive probably are only working on greenfield stuff and haven't got to the hard bit yet. Like everyone says, the first 90% is the easy bit. The last 10% is where you'll spend most of your time, and I don't see LLMs doing the hard bit that well currently.

joshstrange 1y ago
I couldn’t agree more and I’ve said the same thing many times. I have yet to see an LLM-generated app not collapse under it’s own weight after enough iterations/prompts. It gets stuck in loops (removing and adding the same code/concept), it gets hung up on simple errors, etc. For greenfield it’s amazing, no doubt, but unless you are watching it very closely and approving/reviewing the code along the way it will go off the rails. At a certain point it’s easier to add the new feature or make the modification yourself. Even if the LLM could do it, it would burn tons of money and time. I expect things to get better, this will not always be the state of things, but for now “vibe coding” (specifically not reviewing/writing code yourself) is not sustainable. Most people doing it have a github profile that is a mile wide and a meter deep. LLM’s are amazing and useful, but “vibe coding” with them is not sustainable currently.
- RajT88 1y ago
  Coding LLM's: Ruby off rails?
- bredren 1y ago

infecto 1y ago

You’ll usually find three kinds of people talking about LLMs online: 1. The ones writing Medium posts about being 100x more productive. 2. The skeptics, like yourself, who construct made-up examples to prove it’s impossible to get value, framing things in a way where failure is the only plausible outcome. 3. And then there’s the middle group, people quietly getting real, uneven, but worthwhile value. You’d be surprised. The vast majority of engineering isn’t that unique. Usefulness often depends on the age of the codebase and the dynamics of the team. I’m firmly in the middle camp: far from perfect, but I get real value from these tools as they stand today. It’s like having a junior engineer who can scaffold out the basics so I can spend my mental energy on the parts that actually matter.

soraminazuki 1y ago
GP didn't "construct made-up examples" anywhere in this thread as of the time of writing this post. Where did you hallucinate that from? Seriously, why is every comment hyping up AI generated code like this. No concrete evidence, just turtles all the way down. But when actual examples do show up, it's a complete clown show [1][2]. [1]: https://news.ycombinator.com/item?id=44050152 https://news.ycombinator.com/item?id=44050152 [2]: https://news.ycombinator.com/item?id=43907376 https://news.ycombinator.com/item?id=43907376
- infecto 1y ago
  He definitely crafted the narrative he wanted, you see that just as much from skeptics as from the hype crowd. I still stand by what I said: there are always three camps in any LLM discussion. No hallucinations here, I can see where you land. I’m here for a constructive argument, and I believe there’s a real middle ground where LLMs are creating tangible value, even for experienced engineers. Your links about GitHub agents may be spot-on, I haven’t used them myself, so no strong opinion there. Same with Codex; it’s early. But why not mention tools like Cursor or Windsurf? Or the open source alternatives. That’s where people are seeing actual gains today. Why go all-in on anti-hype without talking about your direct experience?
  - 1y ago

traceroute66 1y ago

> And those who, like you, find it to be an extremely finicky process that requires extreme amount of coddling to get average results at best. I am very much in that category. I would describe my experience as the old adage "why keep a dog and bark yourself". And no, I don't buy into people who say you have to engage in "prompt engineering". That's bullshit. Let's face it "prompt engineering" is a synonym for "I have to waste time coming up with hacky workarounds to make this dumb LLM come up with something usable". In the end, its always quicker and easier to do it myself. And unless you are fresh out of school, if you've got more than 10 minutes of coding experience, you will always do a better job than the LLM. Every time a new LLM model comes out, I fall into the same old trap "oh well, this one must be better, surely" I say to myself. And yet its always the same "you can't polish a turd but you can roll it in glitter" experience. I'm sick and tired of LLMs hallucinating shit. I'm sick and tired of LLMs inventing functions that have been available in stdlib for years. I'm sick and tired of LLMs generating useless code with boilerplate subfunctions that just contain a single commented "implement it yourself" line. On top of that LLMs are simply terrible for the environment, guzzling up all that electricity, producing nothing but hot air and bullshit results in return. Basically I'm sick and tired of LLMs and all the LLM fetishists that surround them.

exe34 1y ago

I've managed to use ChatGPT to write a small app in Kotlin, and later used Cursor to fix some issues around performance. Personally I'm not a finish-it kind of guy unless I'm getting paid for it. To be fair, I'm not sure the quality of the code is much worse than all the boatloads of crapware in all the app stores out there. I suspect that the reason "revolutionary products" and "blazing fast iteration" isn't blowing our minds is because the problem was never the code - it was always the people around it, who know better than their customers. The best way to make good software is to test it on real people and iterate - that's going to take a long time no matter how fast the coding part is.

dogtierstatus 1y ago

This is a quality vs quantity issue. There were x number of extremely competent and ~10x moderately competent programmers. LLMs have only made this to 100x moderately competent programmers and may be 2xed extremely competent ones?

analog31 1y ago
Even 2x would be an improvement. It would mean that a project goes from being 3 years late, to only 1.5 years late.

binary132 1y ago

I think a big part of the problem is the devx / workflows that these tools tend to encourage or impose. Watching my coworkers fumble with terrible copilot autoshit in a pairing session makes me want to scream on a daily basis. That doesn’t necessarily mean such a DX is the only way it could or should work, or that it’s even a good DX at all. Most of the time, it actually imposes more cost, both in lost momentum and in corrective action, than it relieves. I think something like a graph where nodes are specified clearly with inputs and outputs (or pre- and post-conditions, if you prefer), and agents iteratively generate and refine the implementation details could have some utility, but that would require programmers to know how to think about program design (and have a usable UI/UX for such a tool) instead of hastily stumbling through patches and modifications to existing code they already don’t understand. The new LLM systems are carrying us farther from that world, not closer to it.

kordlessagain 1y ago

“If we do not invent the future, someone else will do it for us - and it will be their future, not ours.” – Duncan D. Bruce & Dr. Geoff Crook, The Dream Café

wooque 1y ago

Because code is small part of making successful business and usually not the bottleneck.

michaelrpeskin 1y ago

A little snarky but: In my experience, the folks who are 100x more productive are multiplying 100 times a small number. I've found great success with LLMs in the research phase of coding. Last week I needed to write some domain-specific linear algebra and because of some other restrictions, I couldn't just pull in LAPACK. So I had to hand code the work (yes, I know you shouldn't hand code this kind of stuff, but it was a small slice and the domain didn't require the fully-optimized LAPACK stuff). I used an LLM to do the research part that I normally would have had to resort to a couple of math texts to fully understand. So in that case it did make me 100x more effective because it found what I needed and summarized it so that I could convert it to code really quickly. For the fun of it, I did ask the LLM to generate the code for me too, and it made very subtle mistakes that wouldn't have been obvious unless you were already an expert in the field. I could see how a junior engineer would have been impressed by it and probably just check it in and go on. I'm still a firm believer in understanding every bit of code you check in, so even if LLMs get really good, the "code writing" part of my work probably won't ever get faster. But for figuring out what code to write - I think LLMs will make people much faster. The research and summarize part is amazing. The real value in the world is synthesis and novel ideas. And maybe I'm a luddite, but I still think that takes human creativity. LLMs will be a critical support structure, but I'm not sold on them actually writing high-value code.

data-ottawa 1y ago
They definitely help raise the floor, but have a much lower ceiling. If you’re near that ceiling you get most value out of code reviews and those side features you don’t care about that allow you to focus more on the actual problem. That’s a 10-20% lift instead of 10/100x.
ryandrake 1y ago
To steal the joke from that vintage coffee tin: LLMs help you "do stupid things faster." If all you want to do is dump terrible code into the world as fast as possible, then you've got a great tool there. If you're looking to slowly and carefully build a quality product on a solid foundation, or create a programming work of art, LLMs are at best a marginally better autocomplete.
1y ago

jghn 1y ago

> There are two kinds of engineers. Apparently there are at least three as I fit neither of these molds of yours. They are neither making me 100x more productive, nor am I putting in an extreme amount of coddling. For context, in the 30ish years I've been writing code professionally, I've *always* needed to look stuff up constantly. I know what I want, and I know what's possible. I just can never remember things like syntax, specifics, that sort of thing. It doesn't help that I often wind up in jobs where I'm switching between languages often. Before quality search engines like Google, this meant I was always flipping through reference books, man pages, and worse. Then came search engines and that was a step more productive. Then came things like SO, and that was a step more productive. I find LLMs to be another step forward, but not like a quantum leap or anything. Take autocomplete suggestions for instance: Sometimes it's exactly the prompt (pardon the pun) that I need to jog my memory. Other times I know it's not what i want and I ignore it. And the chat interfaces I find better than Googling SO as I can have a back & forth with it.

mattfca 1y ago
this describes me and my experience exactly

GenerocUsername 1y ago

At a macro level, people from the former group might be doing better than you think. I have gone from Senior II, to Lead Architect, to Principal Engineer in the years LLMs have been available. It's not literally a 100x multiplier, but I have leveraged their ability to both fill in details where I lack specific knowledge, and as a first point of contact when researching a new space. LLMs have enabled me to widen my sphere of influence by deepening my abilities at every point of my T-shaped knowledge curve.

codr7 1y ago
Except you're not deepening anything, rather becoming increasingly dependent on wacky tools to keep up your fake status. Go for it, but it's a recipe for disaster imo.

conductr 1y ago

My guess is it depends on what you work with. I’ve been impressed by my ability to spin up nice looking websites and landing pages. Yet, I’ve tried to build a game and despite it appearing to work, it eventually has gotten confused about its own codebase and I end up with a ball of spaghetti where requirements of yesterday get lost or broken when adding functionality today. It feels more like I’m chasing my tail, vibe coding as they say, it’s just seems endlessly circular. My game dev is going to be a hybrid approach going forward. I’ll ask it to create a new project with the desired functionality and see how the solution was proposed and then incorporate that into my main code branch. I find this easier than the Cursor roll back or being overly cautious with suggested edits. I’m just getting to this realization so tbd if it works well. FYI, I’m good at programming generally but game dev is my Achilles. We’ll see how it it goes.

peacebeard 1y ago

I don’t fit in either category. My experience is that LLMs are good at writing code that is easy to write. This is not game-changing, but it is useful.

emp17344 1y ago

The fact that no one can agree on how useful these things are makes me think they aren’t that useful.

slightwinder 1y ago

Even if someone seriously works 100 times faster with AI, it doesn't mean they can easily dominate any established market. A product is more than just code, and a company is more than their headcount. There is domain knowledge about the product and market. There is the personal ability of the workers and the synergy coming with, also the strength of established structures grown over years and decades. Just the habit and trust of users is a big fortress every new product has to conquer. That's not something some random dev or team of devs can easily reach just by being 100 times faster. And realistically, there is also the question of where this claim is coming from. Someone being 100 times faster with AI is probably not starting from a place of high competence. Of the 100x, 90x are probably just for filling the gap to peak devs. So at the end they would be probably as productive as a highly competent dev of some domain, just that they can more easily access any domain without years of experience in that domain. And if we are honest, we somewhat have that already without AI, just by people copying and cookie cutting any sh*t

max_on_hn 1y ago

(disclaimer: I have a vested interest in the space as the purveyor of an AI software development agent) The result you described is coming soon. CheepCode[0] agents already produce working code in a satisfying percentage of cases, and I am at most 3 months away from it producing end-to-end apps and complex changes that are at least human-quality. It would take way less if I got funded to work on it full time. Given that I'm this close as a solo founder with no employees, you can imagine what's cooking inside large companies. [0] My product, cloud-based headless coding agents that connect directly to Linear, accept tickets, and submit GitHub PRs

johnecheck 1y ago
"At most 3 months away" This is a strong claim. I'm not aware of any AI model or system that can consistently and correctly make complex changes in existing codebases. What makes you so confident that you can achieve this, alone, in the next 3 months?
- t917910 1y ago
  The circumference of a circle is easily calculated as piradius2, so calculating the same length of an ellipse would not be much more difficult. Hehe.

sanderjd 1y ago

Nah, there's a third kind, which is probably the quieter majority. These are people who find these to be useful but imperfect tools, whose expectations remain realistic, and for whom the continuing progress these tools are making is pleasant and not exactly surprising but also not taken as a given. I'm finding that these tools are supporting an ever expanding portion of my work, while remaining frustrating frequently.

tom_m 1y ago

It's a good sounding board and productivity tool...but it's over hyped. I think it'll calm down a bit. People are just making huge claims right now which is music to the ears of many. I like the tools don't get me wrong, but they aren't going to make a huge difference day to day. The area they will make a huge difference is in helping people work out issues faster. You know those multi-hour or multi-day issues you run into where you go crazy reading and re-reading the code. The LLMs are good at giving you a fresh look at the code that you'd otherwise need to get more people to help with or go sleep on it for a day. This time savings here is probably the most significant time savings AI code editors or agents will ever provide.

gofreddygo 1y ago

There's a third kind that agrees these are stupid but strongly believes, beyond doubt, they'll be 100x better in 2 years. Everything AI generated today is hot garbage but hey, just wait 2 years and the road to eldorado will show itself. pfft.

jf22 1y ago

There is a gradient in here. The difference between the two isn't that extreme.