9 ms·

OpenAI Researchers Find That AI Is Unable to Solve Most Coding Problems

151 points by colinprince 2y ago

_def 2y ago
> even though CEO Sam Altman insists they will be able to beat "low-level" software engineers by the end of this year. "low/high level" starts to lose its meaning to me because it gets used in opposite ways
- mrayycombi 2y ago
  Where are the low level CEOs vs high level CEOs? I'll bet AI could do their jobs right now. Can SOMEONE please write AI software to replace these people?
  - ITB 2y ago
    Are you bothered by the fact that software engineers might be easier to automate?
    - rpmisms 2y ago
      Considering that there are chickens who outperform stockbrokers, no.
    - throwaway290 2y ago
      It's the opposite. An LLM is better at CEO stuff than working code. A good developer + LLM instead of CEO can succeed. A good CEO + LLM instead of developer cannot succeed. (For a tech company)
    - rsynnott 2y ago
      Is that a fact? I mean, see the linked article; even the company whose whole business model lies in convincing people that that _is_ a fact is kinda saying “yeah, perhaps not”, with vague promises of jam tomorrow.
  - richardw 2y ago
    Management consultants. Main attributes are confidence and ability to generate content. No need to stick around to see it through.
- realitysballs 2y ago
  Yeah low-level language conflated with low-level coders , means the opposite in some sense
- anonymoushn 2y ago
  Even better, if you click through to the linked source he doesn't say "low-level" at all, or make any claim that is at all like the claim he is cited as making!
chasing0entropy 2y ago
The models were restricted from accessing the internet and forced to develop their own solutions internally. I think researchers will find that human coders are unable to solve most coding problems without access to the internet.
- njovin 2y ago
  If you're requiring human coders to write valid, compilable code, maybe. But if you're doing that, you're doing coding interviews wrong. Any interviews I've run or been a part of have required the interviewee to demonstrate their problem-solving skills using pseudo-code.
- croes 2y ago
  And you created the internet then?
- mrayycombi 2y ago
  Damn. How did they write code before 1992?
  - xarope 2y ago
    We did have usenet in the 80s and gopher in the 90s. But yes, in those days it was that mythical "paper" stuff (or were we still using papyrus? I forget)
- Demiurge 2y ago
  What kinds of problems are you talking about? There are problems that require you to learn new libraries and services constantly, accessing documentation, and there are actual software problems when you have to reflect on your own large code base. I work on both kinds of problems, and in the first case, the models are actually well versed in say, all of CloudFormation syntax, that I would have to look up. On the opposite end, I have written many features on trips, unable to be distracted by the internet, just me and the code, and being able to read library source code. The fact is, programming requires abstract modeling that language models aren’t demonstrating the capability of fully replicating. At least, not that we can see, yet.
aszantu 2y ago
it's so much easier to learn from examples than from documentation in my opinion, documentation is, what I use when I want to know additional parameters or downsides of a functionality. I'm no coder though.
- xarope 2y ago
  what I'd like LLMs to do is present examples using acceptable design standards, e.g. whats the pythonic way to do this, and what are exceptions that might yield better performance/optimization (at what does it cost), or what is the best go(lang) JSON parser (since the built-in isn't very good). But instead, I get average to below-average examples (surprise surprise, this is what happens when you train on a high noise-to-signal set of data), which are either subtly or wildly incorrect. I can't see this improving, with reddit and other forums trying to introduce AI bot written posts. Surely these companies are aware of how LLM output degenerates when fed its own input within a few (not even dozen) generations?!?
nostrebored 2y ago
This mirrors what I've seen. I've found that LLMs are most helpful in places where I have the most experience. Maybe this is because of explicitness in prompt and preempting edge cases. Maybe it's because I know exactly what should be done. In these cases, I will still sometimes be surprised by a more complete answer then I was envisioning, a few edge cases that weren't front of mind. But if I have _no_ idea things go wildly off course. I was doing some tricky frontend work with dynamically placed reactflow nodes and bezier curve edges. It took me easily 6 hours of bashing my head against the problem, and it was hard to stop using the assistant because of sunk cost. But I probably would have gotten more out of it and been faster if I'd just sat down and really broken down the problem for a few hours and then moved to implement. The most tempting part of LLMs is letting them figure out design when you're in a time crunch. And the way it solves things when you understand the domain and the bottoms-up view of the work is deceptive in terms of capability. And in this case, it's hoping that people on upwork understand their problems deeply. If they did, they probably wouldn't be posting on upwork. That's what they're trying to pay for.
- georgemcbay 2y ago
  > But if I have _no_ idea things go wildly off course. This is the key to getting some amount of productivity from LLMs in my experience, the ability to spot very quickly when they veer off course into fantasyland and nip it in the bud. Then you point out the issue to them, they agree that they made a dumb mistake and fix it, then you ask them to build on what you just agreed to and they go and reintroduce the same issue they just agreed with you was an obvious problem... because ultimately they are more fancy auto complete machines than they are actual thinking machines. I have found them to be a time saver on the whole even when working with new languages but I think this may in large part be helped by the fact that I have literally decades of coding experience that sets off my spidey senses as soon as they start going rampant. I can't begin to imagine how comical it must be when someone who doesn't have a strong programming foundation just blindly trusts these things to produce useful code until the runtime or compile time bugs become unavoidably obvious.
- ipython 2y ago
  I just had this conversation with a customer. And it’s hard to avoid anthropomorphizing ai. Once you equate the ai system with a human - a human who creates perfectly pep8 formatted python is probably a decent python programmer, whereas someone who bangs out some barely readable code with mixed spacing and variable naming styles is most likely a novice. We use these signals to indicate how much we should trust the code - same with written text. Poorly constructed sentences? Gaps or pauses? Maybe that person isn’t as knowledgeable. These shortcuts fail miserably on a system that generates perfect grammar, so when you bring your stereotypes gleaned from dealing with humans into the ai world, you’re in for an unpleasant surprise when you unpack the info and find it’s only about 75% correct, despite the impeccable grammar.
WiSaGaN 2y ago
So this is an in-house benchmarks after their undisclosed partnership with a previous benchmark company. Really hope they do not have their next model to vastly outperform on this benchmark in the coming weeks.
pton_xd 2y ago
Well, OpenAI does currently have 288 job openings, including plenty of software engineers, so that says something.
- jr-ai-interview 2y ago
  Lol, that place is causing so many worldwide horrors misrepresenting AI I would't give sama an answer to an email at this point.
mrayycombi 2y ago
Despite the lack luster coding performance, AI has PROVEN its able to provide a rationale for profit taking job cuts, layoffs, reduced stock grants, and increased executive bonuses. So it's not ALL bad news.
marban 2y ago
I prompted up a very basic Flask scaffold via Windsurf and once it reached a certain code size, it just started to remove or weirdly rewrite old parts to handle the context. ("You're right let's move that back in"). Didn't end well.
blindriver 2y ago
They should feed it bootcamp study materials and Cracking the Coding Interview book in order to improve its ability to code.
- Ozzie_osman 2y ago
  If it can master Binary Search Trees, it can master anything.
  - blindriver 2y ago
    "If you need to improve speed, add Hash Tables."
axelfontaine 2y ago
... so far
jasonthorsness 2y ago
Half of the work is specification and iteration. I think there’s a focus on full SWE replacement because it’s sensational, but we’ll more end up with SWE able to focus on the less patterned or ambiguous work and made way more productive with the LLM handling subtasks more efficiently. I don’t see how full SWE replacement can happen unless non-SWE people using LLMs become technical enough to get what they need out of them, in which case they probably have just become SWE anyway.
- petesergeant 2y ago
  > in which case they probably have just become SWE anyway or learn to use something like Bubble
- tkgally 2y ago
  > unless non-SWE people using LLMs become technical enough to get what they need out of them Non-SWE person here. In the past year I've been able to use LLMs to do several tasks for which I previously would have paid a freelancer on Fiverr. The most complex one, done last spring, involved writing a Python program that I ran on Google Colab to grab the OCR transcriptions of dozens of 19th-century books off the Internet Archive, send the transcriptions to Gemini 1.5, and collect Gemini's five-paragraph summary of each book. If I had posted the job to Fiverr, I would have been willing to pay several hundred dollars for it. Instead, I was able to do it all myself with no knowledge of Python or previous experience with Google Colab. All it cost was my subscription to ChatGPT Plus (which I would have had anyway) and a few dollars of API usage. I didn't put any full-time SWEs out of work, but I did take one job away from a Fiverr freelancer.
  - ripped_britches 2y ago
    This is a good anecdote but most software engineering is not scripting. It’s getting waist (or neck) deep in a large codebase and many intricacies. That being said I’m very bullish on AI being able to handle more and more of this very soon. Cursor definitely does a great job giving us a taste of cross codebase understanding.
realitysballs 2y ago
I believe the outcome of this type of article is actually positive. The ‘SWE-Lancer’ benchmark provides visibility into a more pragmatic assessment of LLM capabilities. Ironically it actually refutes Altman’s claims mentioned in the same article . Hard to replace engineers when you create a benchmark you can’t score decently on.
- throw234234234 2y ago
  Or it could be a case of: Never prepare a benchmark/prep a comparison which you think you won't succeed at. This is especially true when you are funded by mostly private/VC investors. Time will tell. I think they are trying to frame the narrative; then succeed at it. Let's see. This helps justify OpenAPI's validation and efforts to investors/VC's. After all; IMO without coding as a use case for LLM's AI wouldn't nearly have the same hype/buzz as it does now. Greed (profit) and fear (losing jobs) are a great motivator to keep investment hype and funds coming in.
rvz 2y ago
The benchmark for AI models to assess their 'coding' ability should be on actual real world production-grade repositories and fixing bugs in them such as the Linux kernel, Firefox, sqlite or other large scale well known repositories. Not these Hackerrank, Leetcode or previous IOI and IMO problems which we already have the solutions to them and reproducing the most optimal solution copied from someone else. If it can't manage most unseen coding problems with no previous solutions to them, what hope does it have against explaining and fixing bugs correctly on very complex repositories with over 1M-10M+ lines of code?
- ashu1461 2y ago
  The new Lancer benchmark is on actual problems and that is where it is failing by a huge margin.
- cjblomqvist 2y ago
  For anyone who don't know what IOI and IMO refers to; IOI refers to the International Olympiad in Informatics, a prestigious annual computer science competition for high school students, while IMO refers to the International Mathematical Olympiad, which is a world-renowned mathematics competition for pre-college students. (Ironically, provided by ChatGPT)
m3kw9 2y ago
It solved a lot of mines
spartanatreyu 2y ago
Link to the original paper: https://arxiv.org/pdf/2502.12115 https://arxiv.org/pdf/2502.12115 TL;DR: They tested with programming tasks and manager's tasks. The vast majority of tasks given require bugfixes. Claude 3.5 Sonnet (the best performing LLM) passed 21.1% of programmer tasks and 47.0% of manager tasks. The LLMs have a higher probability of passing the tests when they are given more attempts, but there's not a lot of data showing where the improvement tails off. (probably due to how expensive it is to run the tests) Personally, I have other concerns: - A human being asked to review repeated LLM attempts to resolve a problem is going to lead that human to review things less thoroughly after a few attempts and over time is going to let false positives slip through - An LLM being asked to review repeated LLM attempts to resolve a problem is going to lead to the LLM convincing itself that it is correct with no regard for the reality of the situation. - LLM use increases code churn in a code base - Increased code churn is known to be bad the health of projects
jr-ai-interview 2y ago
This has been obvious for a couple years to anyone in the industry that has been faced with an onslaught of PRs to review from AI enabled coders who sometimes can't even explain the changes being made at all. Great job calling it AI.
ldjkfkdsjnv 2y ago
I’ve got 15 years of coding experience at some of the biggest tech companies. My personal opinion is that most people have no clue how good these AI coding systems already are. If you use something like RepoPrompt, where you selectively choose which files to include in the prompt, and then also provide a clear description of what changes you want to make—along with a significant portion of the source code—a model like O1Pro will nail the solution the first time. The real issue is that people are not providing proper context to the models. Take any random coding library you’re interfacing with, like a Postgres database connection client. The LLM isn’t going to inherently know all of the different configurations and nuances of that client. However, if you pass in the source code for the client along with the relevant portions of your own codebase, you’re equipping the model with the exact information it needs. Every time you do this, including a large prompt size—maybe 50,000 to 100,000 tokens—you dramatically improve the model’s ability to generate an accurate and useful response. With a strong model like O1Pro, the results can be exceptional. The key isn’t that these models are incapable; it’s that users aren’t feeding them the right data.
- Dban1 2y ago
  the limiting factor is no longer the answers but the questions
- tsimionescu 2y ago
  Are you suggesting that OpenAI published a paper assessing their own models on real-world problems, but failed to properly use their own models? And/or that you know better than OpenAI scientists how to use OpenAI models most effectively?
  - ldjkfkdsjnv 2y ago
    thou shall not question the high priests
    - tsimionescu 2y ago
      That's not what I'm saying. But telling us that the designers of a product are stupid and don't know how to use their own product when they're disclosing its limitations should really come with more than a "trust me bro" as evidence.
yieldcrv 2y ago
I saw this still, chain of thought is great for LeetCode 75 Since interviewers “want to see how you think” (and get the right answer in less time than other candidates on average) I can now see how you’re supposed to think (and get the right answer in less time than other candidates on average, for now)
mohsen1 2y ago
I wonder how many of the solutions that passes SWE-lancer evals would not be accepted by the poster due to low quality I’ve been trying so many things to automate solving bugs and adding features 100% by AI and I have to admit it’s been a failure. Without someone that can read the code and fully understand the AI generated code and suggests improvements (SWE in the loop) AI code is mostly not good.
pzo 2y ago
> The models weren't allowed to access the internet How many software developers could solve most even simple programming problems (except 'Hello world') with zero shot style (you write in notepad then can compile only once and execute once) without access to internet (stackoverflow, google search, documentation), tools (terminal, debugger, linter, cli)? I think then it's not the best comparison to make any judgement. Future benchmark should test agents where they allowed to solve the problem in 5-10 minutes, allow give access to internet, documentation, linter, terminal with MCP servers.
- rurp 2y ago
  It depends a lot on the type of problem. If we're talking about fixing a bug or adding a new feature to a large existing code base, which probably describes a huge portion or professional software engineering work, I would say most engineers could do most of those tasks without the internet. Especially if the goal is to simply pass a benchmark test of getting it working without future considerations.
- inykt 2y ago
  These models are held at higher standards than humans. They should be above to solve any coding problem with just the documentation.
  - Ekaros 2y ago
    Isn't point of the training that they already have all the information they could have. So they do not need the Internet as on Internet there would only be information they already "know"...
- kristopolous 2y ago
  I used to be able to. The web's made me lazy. I was better before it and I'm better when I don't use it. Really, the stuff you think helps you is often just holding you back.
- achierius 2y ago
  Isn't this how interviews tend to work? So I think a good number of devs would, yes.
rurp 2y ago
I recently had to do a one-off task using SQL in a way that I wasn't too familiar with. Since I could explain conceptually what I needed but didn't know all the right syntax this seemed like a perfect use case to loop in Claude. The first couple back and forths went ok but it quickly gave me some SQL that was invalid. I sent back the exact error and line number and it responded by changing all of the aliases but repeated the same logical error. I tried again and this time it rewrote more of the code, but still used the exact same invalid operation. At that point I just went ahead and read some docs and other resources and solved things the traditional way. Given all of the hype around LLMs I'm honestly surprised to see top models still failing in such basic and straightforward ways. I keep trying to use LLMs in my regular work so that I'm not missing out on something potentially great but I still haven't hit a point where they're all that useful.
- deegles 2y ago
  I'm not convinced LLMs will evolve into general AI. The promises that it's just around the corner feels increasingly like a big scam.
  - petesergeant 2y ago
    Depends what you mean by evolve. I don't think we'll get general AI by simply scaling LLMs, but I think general AI, if it arrives, will be able to trace its lineage very much back to LLMs. Journeys through the embedding space very much feel like the way forward to me, and that's what LLMs are.
    - giardini 2y ago
      Embedding spaces are one thing, LLMs are quite another. I believe the former are understandable and likely a part of true AGI but the latter a series of hacks, at worst a red herring leading us off the proper track into a deadend.
    - boppo1 2y ago
      Is there a resource I can use to understand the difference between embedding space and latent space?
anandnair 2y ago
Coding, especially the type mentioned in the article (building an app based on a specification)—is a highly complex task. It cannot be completed with a single prompt and an immediate, flawless result. This is why even most software projects (built by humans) go through multiple iterations before they work perfectly. We should consider a few things before asking, "Can AI code like humans?": - How did AI learn to code? What structured curriculum was used? - Did AI receive mentoring from an experienced senior who has solved real-life issues that the AI hasn't encountered yet? - Did the AI learn through hands-on coding or just by reading Stack Overflow? If we want to model AI as being on par with (or even superior to) human intelligence, don’t we at least need to consider how humans learn these complex skills? Right now, it's akin to giving a human thousands of coding books to "read" and "understand," but offering no opportunity to test their programs on a computer. That’s essentially what's happening! Without doing that, I don't think we'll ever be able to determine whether the limitation of current AI is due to its "low intelligence" or because it hasn’t been given a proper opportunity to learn.
- DarkmSparks 2y ago
  >How did AI learn to code? It didn't, it's just very good at copying already existing code and tweeking it a bit. >Did AI receive mentoring from an experienced senior It doesnt even comprehend what an experienced senior is, all it cares about is how frequently certain patterns occurred in certain circumstances. >Did the AI learn through hands-on coding or just by reading Stack Overflow? it "learnt" by collecting a large database of existing code, most of which is very low quality open source proofs of concept, then spits out the bits that are probably related to a question.
  - anandnair 2y ago
    I think we're drastically oversimplifying what "pattern matching" means. It is also one of the fundamental mechanisms by which the human brain operates. I believe we are consciously (or perhaps subconsciously) conditioned to think that human "logic" and "reasoning" are several degrees more advanced than pattern matching. However, I don't think this is true. The fundamental difference lies in how patterns are formed in each case. For LLMs, all they know are the patterns they observe in "words" - that is the only "sense" they possess. But for humans, pattern recognition involves continuously ingesting and identifying patterns across our five primary senses—not just separately, but simultaneously. For example, when an LLM describes something as "ball-shaped," it cannot feel the shape of a ball because it lacks another sense to associate with the word "ball-shaped." In contrast, humans have the sense of touch, allowing them to associate the word or sound pattern "ball" with the physical sensation of holding a ball.
DarkmSparks 2y ago
LLMs will never solve this problem, they are basically just glorified copy & paste engines, solving real code problems requires invention, even for most basic tasks. The best they will manage in their current direct is reason they don't have the capability or capacity to actually solve the problem rather than just getting it wrong the vast majority of the time.
- sumedh 2y ago
  > LLMs will never solve this problem We should revisit this comment in 5 years.
internet101010 2y ago
I believe it. I couldn't even get o1 or claude 3.5 to write a tampermonkey script that would turn off auto-scroll to bottom in LibreChat, even when uploading the html and javascript as context. Apparently it has to do with overflow anchor or something in React? Idk. I gave up.
- throwaway290 2y ago
  Unless it works by literally scrolling with JS, I bet some strategic bit of CSS should do it...
simonw 2y ago
I find the framing of this story quite frustrating. The purpose of new benchmarks is to gather tasks that today's LLMs can't solve comprehensively. It an AI lab built a benchmark that their models scored 100% on they would have been wasting everyone's time! Writing a story that effectively says "ha ha ha, look at OpenAI's models failing to beat the new benchemark they created!" is a complete misunderstanding of the research.
- mckngbrd 2y ago
  Shhh ... you're spoiling everybody's confirmation bias against LLMs. They are obviously terrible at coding, just as we have known all along, and everybody should laugh at them. Nothing to see here!
  - johnnyanmac 2y ago
    As long as these companies keep pretending AI is ready to replace humans, I will be biased against lies, thank you.
  - player1234 2y ago
    Since you are one of the cool kids in the know, can you share the road map to profitability and even better the expected/hyped ROI? Without extrpolations into science fiction, please.
siliconc0w 2y ago
Interesting that Claude wins despite the other models being more expensive and doing much better in the traditional benchmarks.
ChrisArchitect 2y ago
Previously on source: https://news.ycombinator.com/item?id=43086347 https://news.ycombinator.com/item?id=43086347
ginvok 2y ago
AI don't "solve" problems, best it can do is remember them. Ask them to solve anything new that's challenging and it starts to hallucinate. At least currently.
- ilrwbwrkhv 2y ago
  And I'm ashamed that OpenAI and Sam altman are walking around talking about AGI. And I'm so... disillusioned by the entire tech community that they have fallen for it or they at least pretend to believe it. It's like LinkedIn Where everybody pretends to be cringe, positivity, people. Even though they know it's cringe and nobody believes it.
  - leosanchez 2y ago
    Whenever AI hallucinates a little complex SQL or some tool / language it doesn't have much training data on, I think of AGI hype and Sam Altman's words on how AI can be used to cure cancer in near future.
    - ilrwbwrkhv 2y ago
      Instead if they rightly just said it is an useful tool to be used by researchers to help them like a smart calculator for big data, it would be so much more honest and correct.
emorning3 2y ago
I dont understand why anyone would even think that the current crop of AI is capable of planning or reason on its own. Transformers with memory would be different story. But, no memory, no capability to reason. End of story, right?
numba888 2y ago
Not sure what they found. Either model is unable, or they were unable to solve the tasks using models. Loos like they used strait questions and not Chain of Thoughts. The result for the same model depends on how you ask. The tasks probably required more thinking under the hood than model is allowed to do in one request. More interesting would be if model is capable of solving given enough time. Using multiple requests orchestrated by some framework automatically.
rsynnott 2y ago
> OpenAI researchers have admitted that even the most advanced AI models still are no match for human coders — even though CEO Sam Altman insists they will be able to beat "low-level" software engineers by the end of this year This is the “self-driving cars next year, definitely” of the 20s, at this point.
floppiplopp 2y ago
LLMs are still just text generators. These are statistical models that cannot think or solve logical problems. They might fool people, as Weizenbaum's "Eliza" did in the late 60s, by generating code that sort of runs sometimes, but identifying and solving a logic problem is something I reliably see these things fail at.
- tomduncalf 2y ago
  Have you tried the latest models, using them with Cursor etc? They might not be truly intelligent but I’d be surprised if an SWE can’t see that they are already offering a lot of value. They probably can’t solve totally novel problems but they are good at transposing existing solutions to new domains. I’ve built some pretty crazy stuff with just prompts - granted I can prompt with detailed technical instructions when needed as I’m a SWE, similar to instructing a junior. I’ve built prototypes which would take days in hours which to me is hugely exciting. The code quality of pure AI generated code isn’t great but my approach right now is to use that to prototype things mostly with prompts (it takes as much time to build a prototype as it would to create a mock up or document explaining the idea previously) then once we are committed to it, I’ll rebuild it mostly by hand but using Cursor to help.
imperial_note 2y ago
The article concludes that LLMs are "not skilled enough at software engineering to replace real-life people quite yet". Yet Claude 3.5 sonnet "earned" $403.325,00 according to the paper referenced. That is $403k worth of labour potentially replaced.
kjrfghslkdjfl 2y ago
[dead]
casey2 2y ago
"Here's the choice you have when you are faced with something new. You can take this technological advance and decide "This is a better way of doing the stuff I'm doing now and I can use this to continue on the path that I'm going", so that's staying in the pink plane , or you can say "This is not a better old thing, this is almost a new thing and I wonder what that new thing is trying to be" and if you do that there's a chance of actually perhaps gaining some incredible leverage over simply optimizing something that can't be optimized very much. - Kay Current LLMs will change the world, but it won't be by completing pull requests quickly. Although a "stargate level" LLM could accelerate pink plane traversal so much that you don't even need to find the correct usecase. LLM scaling will be the computer graphics scaling of this generation. In terms of intelligence gpt4 based o3 is but a postage stamp. As LLMs scale a picture of intelligence will emerge.
prompt_overflow 2y ago
This shows how unrealistic, inaccurate and pointless coding benchmarks are. I also include similar code interview platforms like leetcode, hackerrank and so on.
AbstractH24 2y ago
Increasingly I think that the impact of generative AI is going to more of an incremental form of disruption than revolutionary. More like spreadsheets than the printing press. Spreadsheets becoming mainstream made it easy to do computing that once took a lot of manual human labor quite quickly. And it made plenty of jobs and people who do them obsolete. But they didn’t upend society fundamentally or the need for intelligence and they didn’t get rolled out overnight.
halis 2y ago
The biggest scam in AI is Salesforce. They’re going to take some crappy model that they made or simply switch to a better open source model. Then they’re going to make a large deal with a cloud provider to spin up all these models for all their customers and then re-sell it as AI to their customers for 100x what OpenAI gets per month. And the quality will be lower.
ryandvm 2y ago
LLMs at this point are just a great replacement for Stack Overflow. If what you're doing has been heavily documented and you just need a primer or some skeletal sample code, the LLMs are great. They are not creative at all, but 99% of my job is not creative either.
ineedasername 2y ago
The models weren't allowed to access the internet, meaning they couldn't just crib similar answers that'd been posted online So… not the same basic tools that a human has when coding?
- spartanatreyu 2y ago
  Well if the tasks are based on tasks that were already asked on freelancing sites, the LLMs could just google the task and copy-paste the solution.
darepublic 2y ago
To me o1 is pretty good. I dunno how it would digest an entire codebase and solve a bug in it. Those details weren't obvious to me from the article above. But o1 has certainly been very valuable to me in coding in new languages on the fly.
scotty79 2y ago
> SWE-Lancer, built on more than 1,400 software engineering tasks from the freelancer site Upwork What's not mentioned here (I think) is that tasks in this benchmark are priced and they sum up to million dollars. And current AIs were able to earn nearly half of that. So while technically they can't solve most problems (yet) they are already perfectly capable of taking about 40% of the food off your plate.
contractorwolf 2y ago
To all those devs saying "i tried it and it wasnt perfect the first time, so I gave up", I am reminded of something my father used to say: "A bad carpenter blames his tools" So AI not going to answer your question right on its first attempt in many cases. It is forced to make a lot of assumptions based on the limited info you gave it, some of those may not match your individual case. Learn to prompt better and it will work better for you. It is a skill, just like everything else in life. Imagine going into a job today and saying "i tried google but it didnt give me what I was looking for as the first result, so I dont use google anymore". I just wouldnt hire a dev that couldnt learn to use AI as a tool to get there job done 10x faster. If that is your attitude, 2026 might really be a wake-up call for your new life.
- gar1t 2y ago
  One of the reasons SO works is that the correct or best answer tends to move toward the top of the list. AI struggles to do this reliably - and so its closer to SO where the answers are randomly selected and you have to try a few to get the one that's correct. Also, I bet your father imagined the carpenter's toolbox full of well accepted useful tools. For many of us, non-bad carpenters, AI hasn't made the cut yet.
  - contractorwolf 2y ago
    All I am saying is that if you are expecting it to fail for you, that is absolutely what it will do. You weren't good at typing on your first day either. I went from a decent engineer with 20 years experience to a 10x engineer able to take on any problem, in less than a year. All because I learned how to use the tool effectively. Just dont give up, get back on that bike and keep peddling. I promise it will amaze you if you give it a chance. This is a tool, just like all the other ones you have learned, but it will make you a far better engineer. It can fill all those gaps in your understanding of code. You can ask it all those questions you are unwilling to ask your colleagues, because you think you will sound dumb for not knowing. You can ask it to explain everything again if you still dont get it. It is powerful if you know how to use it.
tiberriver256 2y ago
... And then they tried sonnet 3.7

151 points by colinprince 2y ago

_def 2y ago

> even though CEO Sam Altman insists they will be able to beat "low-level" software engineers by the end of this year. "low/high level" starts to lose its meaning to me because it gets used in opposite ways

mrayycombi 2y ago
Where are the low level CEOs vs high level CEOs? I'll bet AI could do their jobs right now. Can SOMEONE please write AI software to replace these people?
- ITB 2y ago
  Are you bothered by the fact that software engineers might be easier to automate?
  - rpmisms 2y ago
    Considering that there are chickens who outperform stockbrokers, no.
  - throwaway290 2y ago
    It's the opposite. An LLM is better at CEO stuff than working code. A good developer + LLM instead of CEO can succeed. A good CEO + LLM instead of developer cannot succeed. (For a tech company)
  - rsynnott 2y ago
    Is that a fact? I mean, see the linked article; even the company whose whole business model lies in convincing people that that _is_ a fact is kinda saying “yeah, perhaps not”, with vague promises of jam tomorrow.
- richardw 2y ago
  Management consultants. Main attributes are confidence and ability to generate content. No need to stick around to see it through.
realitysballs 2y ago
Yeah low-level language conflated with low-level coders , means the opposite in some sense
anonymoushn 2y ago
Even better, if you click through to the linked source he doesn't say "low-level" at all, or make any claim that is at all like the claim he is cited as making!

chasing0entropy 2y ago

The models were restricted from accessing the internet and forced to develop their own solutions internally. I think researchers will find that human coders are unable to solve most coding problems without access to the internet.

njovin 2y ago
If you're requiring human coders to write valid, compilable code, maybe. But if you're doing that, you're doing coding interviews wrong. Any interviews I've run or been a part of have required the interviewee to demonstrate their problem-solving skills using pseudo-code.
croes 2y ago
And you created the internet then?
mrayycombi 2y ago
Damn. How did they write code before 1992?
- xarope 2y ago
  We did have usenet in the 80s and gopher in the 90s. But yes, in those days it was that mythical "paper" stuff (or were we still using papyrus? I forget)
Demiurge 2y ago
What kinds of problems are you talking about? There are problems that require you to learn new libraries and services constantly, accessing documentation, and there are actual software problems when you have to reflect on your own large code base. I work on both kinds of problems, and in the first case, the models are actually well versed in say, all of CloudFormation syntax, that I would have to look up. On the opposite end, I have written many features on trips, unable to be distracted by the internet, just me and the code, and being able to read library source code. The fact is, programming requires abstract modeling that language models aren’t demonstrating the capability of fully replicating. At least, not that we can see, yet.

aszantu 2y ago

it's so much easier to learn from examples than from documentation in my opinion, documentation is, what I use when I want to know additional parameters or downsides of a functionality. I'm no coder though.

xarope 2y ago
what I'd like LLMs to do is present examples using acceptable design standards, e.g. whats the pythonic way to do this, and what are exceptions that might yield better performance/optimization (at what does it cost), or what is the best go(lang) JSON parser (since the built-in isn't very good). But instead, I get average to below-average examples (surprise surprise, this is what happens when you train on a high noise-to-signal set of data), which are either subtly or wildly incorrect. I can't see this improving, with reddit and other forums trying to introduce AI bot written posts. Surely these companies are aware of how LLM output degenerates when fed its own input within a few (not even dozen) generations?!?

nostrebored 2y ago

This mirrors what I've seen. I've found that LLMs are most helpful in places where I have the most experience. Maybe this is because of explicitness in prompt and preempting edge cases. Maybe it's because I know exactly what should be done. In these cases, I will still sometimes be surprised by a more complete answer then I was envisioning, a few edge cases that weren't front of mind. But if I have _no_ idea things go wildly off course. I was doing some tricky frontend work with dynamically placed reactflow nodes and bezier curve edges. It took me easily 6 hours of bashing my head against the problem, and it was hard to stop using the assistant because of sunk cost. But I probably would have gotten more out of it and been faster if I'd just sat down and really broken down the problem for a few hours and then moved to implement. The most tempting part of LLMs is letting them figure out design when you're in a time crunch. And the way it solves things when you understand the domain and the bottoms-up view of the work is deceptive in terms of capability. And in this case, it's hoping that people on upwork understand their problems deeply. If they did, they probably wouldn't be posting on upwork. That's what they're trying to pay for.

georgemcbay 2y ago
> But if I have _no_ idea things go wildly off course. This is the key to getting some amount of productivity from LLMs in my experience, the ability to spot very quickly when they veer off course into fantasyland and nip it in the bud. Then you point out the issue to them, they agree that they made a dumb mistake and fix it, then you ask them to build on what you just agreed to and they go and reintroduce the same issue they just agreed with you was an obvious problem... because ultimately they are more fancy auto complete machines than they are actual thinking machines. I have found them to be a time saver on the whole even when working with new languages but I think this may in large part be helped by the fact that I have literally decades of coding experience that sets off my spidey senses as soon as they start going rampant. I can't begin to imagine how comical it must be when someone who doesn't have a strong programming foundation just blindly trusts these things to produce useful code until the runtime or compile time bugs become unavoidably obvious.
ipython 2y ago
I just had this conversation with a customer. And it’s hard to avoid anthropomorphizing ai. Once you equate the ai system with a human - a human who creates perfectly pep8 formatted python is probably a decent python programmer, whereas someone who bangs out some barely readable code with mixed spacing and variable naming styles is most likely a novice. We use these signals to indicate how much we should trust the code - same with written text. Poorly constructed sentences? Gaps or pauses? Maybe that person isn’t as knowledgeable. These shortcuts fail miserably on a system that generates perfect grammar, so when you bring your stereotypes gleaned from dealing with humans into the ai world, you’re in for an unpleasant surprise when you unpack the info and find it’s only about 75% correct, despite the impeccable grammar.

WiSaGaN 2y ago

So this is an in-house benchmarks after their undisclosed partnership with a previous benchmark company. Really hope they do not have their next model to vastly outperform on this benchmark in the coming weeks.

pton_xd 2y ago

Well, OpenAI does currently have 288 job openings, including plenty of software engineers, so that says something.

jr-ai-interview 2y ago
Lol, that place is causing so many worldwide horrors misrepresenting AI I would't give sama an answer to an email at this point.

mrayycombi 2y ago

Despite the lack luster coding performance, AI has PROVEN its able to provide a rationale for profit taking job cuts, layoffs, reduced stock grants, and increased executive bonuses. So it's not ALL bad news.

marban 2y ago

I prompted up a very basic Flask scaffold via Windsurf and once it reached a certain code size, it just started to remove or weirdly rewrite old parts to handle the context. ("You're right let's move that back in"). Didn't end well.

blindriver 2y ago

They should feed it bootcamp study materials and Cracking the Coding Interview book in order to improve its ability to code.

Ozzie_osman 2y ago
If it can master Binary Search Trees, it can master anything.
- blindriver 2y ago
  "If you need to improve speed, add Hash Tables."

axelfontaine 2y ago

... so far

jasonthorsness 2y ago

Half of the work is specification and iteration. I think there’s a focus on full SWE replacement because it’s sensational, but we’ll more end up with SWE able to focus on the less patterned or ambiguous work and made way more productive with the LLM handling subtasks more efficiently. I don’t see how full SWE replacement can happen unless non-SWE people using LLMs become technical enough to get what they need out of them, in which case they probably have just become SWE anyway.

petesergeant 2y ago
> in which case they probably have just become SWE anyway or learn to use something like Bubble
tkgally 2y ago
> unless non-SWE people using LLMs become technical enough to get what they need out of them Non-SWE person here. In the past year I've been able to use LLMs to do several tasks for which I previously would have paid a freelancer on Fiverr. The most complex one, done last spring, involved writing a Python program that I ran on Google Colab to grab the OCR transcriptions of dozens of 19th-century books off the Internet Archive, send the transcriptions to Gemini 1.5, and collect Gemini's five-paragraph summary of each book. If I had posted the job to Fiverr, I would have been willing to pay several hundred dollars for it. Instead, I was able to do it all myself with no knowledge of Python or previous experience with Google Colab. All it cost was my subscription to ChatGPT Plus (which I would have had anyway) and a few dollars of API usage. I didn't put any full-time SWEs out of work, but I did take one job away from a Fiverr freelancer.
- ripped_britches 2y ago
  This is a good anecdote but most software engineering is not scripting. It’s getting waist (or neck) deep in a large codebase and many intricacies. That being said I’m very bullish on AI being able to handle more and more of this very soon. Cursor definitely does a great job giving us a taste of cross codebase understanding.

realitysballs 2y ago

I believe the outcome of this type of article is actually positive. The ‘SWE-Lancer’ benchmark provides visibility into a more pragmatic assessment of LLM capabilities. Ironically it actually refutes Altman’s claims mentioned in the same article . Hard to replace engineers when you create a benchmark you can’t score decently on.

throw234234234 2y ago
Or it could be a case of: Never prepare a benchmark/prep a comparison which you think you won't succeed at. This is especially true when you are funded by mostly private/VC investors. Time will tell. I think they are trying to frame the narrative; then succeed at it. Let's see. This helps justify OpenAPI's validation and efforts to investors/VC's. After all; IMO without coding as a use case for LLM's AI wouldn't nearly have the same hype/buzz as it does now. Greed (profit) and fear (losing jobs) are a great motivator to keep investment hype and funds coming in.

rvz 2y ago

The benchmark for AI models to assess their 'coding' ability should be on actual real world production-grade repositories and fixing bugs in them such as the Linux kernel, Firefox, sqlite or other large scale well known repositories. Not these Hackerrank, Leetcode or previous IOI and IMO problems which we already have the solutions to them and reproducing the most optimal solution copied from someone else. If it can't manage most unseen coding problems with no previous solutions to them, what hope does it have against explaining and fixing bugs correctly on very complex repositories with over 1M-10M+ lines of code?

ashu1461 2y ago
The new Lancer benchmark is on actual problems and that is where it is failing by a huge margin.
cjblomqvist 2y ago
For anyone who don't know what IOI and IMO refers to; IOI refers to the International Olympiad in Informatics, a prestigious annual computer science competition for high school students, while IMO refers to the International Mathematical Olympiad, which is a world-renowned mathematics competition for pre-college students. (Ironically, provided by ChatGPT)

m3kw9 2y ago

It solved a lot of mines

spartanatreyu 2y ago

Link to the original paper: https://arxiv.org/pdf/2502.12115 https://arxiv.org/pdf/2502.12115 TL;DR: They tested with programming tasks and manager's tasks. The vast majority of tasks given require bugfixes. Claude 3.5 Sonnet (the best performing LLM) passed 21.1% of programmer tasks and 47.0% of manager tasks. The LLMs have a higher probability of passing the tests when they are given more attempts, but there's not a lot of data showing where the improvement tails off. (probably due to how expensive it is to run the tests) Personally, I have other concerns: - A human being asked to review repeated LLM attempts to resolve a problem is going to lead that human to review things less thoroughly after a few attempts and over time is going to let false positives slip through - An LLM being asked to review repeated LLM attempts to resolve a problem is going to lead to the LLM convincing itself that it is correct with no regard for the reality of the situation. - LLM use increases code churn in a code base - Increased code churn is known to be bad the health of projects

jr-ai-interview 2y ago

This has been obvious for a couple years to anyone in the industry that has been faced with an onslaught of PRs to review from AI enabled coders who sometimes can't even explain the changes being made at all. Great job calling it AI.

ldjkfkdsjnv 2y ago

I’ve got 15 years of coding experience at some of the biggest tech companies. My personal opinion is that most people have no clue how good these AI coding systems already are. If you use something like RepoPrompt, where you selectively choose which files to include in the prompt, and then also provide a clear description of what changes you want to make—along with a significant portion of the source code—a model like O1Pro will nail the solution the first time. The real issue is that people are not providing proper context to the models. Take any random coding library you’re interfacing with, like a Postgres database connection client. The LLM isn’t going to inherently know all of the different configurations and nuances of that client. However, if you pass in the source code for the client along with the relevant portions of your own codebase, you’re equipping the model with the exact information it needs. Every time you do this, including a large prompt size—maybe 50,000 to 100,000 tokens—you dramatically improve the model’s ability to generate an accurate and useful response. With a strong model like O1Pro, the results can be exceptional. The key isn’t that these models are incapable; it’s that users aren’t feeding them the right data.

Dban1 2y ago
the limiting factor is no longer the answers but the questions
tsimionescu 2y ago
Are you suggesting that OpenAI published a paper assessing their own models on real-world problems, but failed to properly use their own models? And/or that you know better than OpenAI scientists how to use OpenAI models most effectively?
- ldjkfkdsjnv 2y ago
  thou shall not question the high priests
  - tsimionescu 2y ago
    That's not what I'm saying. But telling us that the designers of a product are stupid and don't know how to use their own product when they're disclosing its limitations should really come with more than a "trust me bro" as evidence.

yieldcrv 2y ago

I saw this still, chain of thought is great for LeetCode 75 Since interviewers “want to see how you think” (and get the right answer in less time than other candidates on average) I can now see how you’re supposed to think (and get the right answer in less time than other candidates on average, for now)

mohsen1 2y ago

I wonder how many of the solutions that passes SWE-lancer evals would not be accepted by the poster due to low quality I’ve been trying so many things to automate solving bugs and adding features 100% by AI and I have to admit it’s been a failure. Without someone that can read the code and fully understand the AI generated code and suggests improvements (SWE in the loop) AI code is mostly not good.

pzo 2y ago

> The models weren't allowed to access the internet How many software developers could solve most even simple programming problems (except 'Hello world') with zero shot style (you write in notepad then can compile only once and execute once) without access to internet (stackoverflow, google search, documentation), tools (terminal, debugger, linter, cli)? I think then it's not the best comparison to make any judgement. Future benchmark should test agents where they allowed to solve the problem in 5-10 minutes, allow give access to internet, documentation, linter, terminal with MCP servers.

rurp 2y ago
It depends a lot on the type of problem. If we're talking about fixing a bug or adding a new feature to a large existing code base, which probably describes a huge portion or professional software engineering work, I would say most engineers could do most of those tasks without the internet. Especially if the goal is to simply pass a benchmark test of getting it working without future considerations.
inykt 2y ago
These models are held at higher standards than humans. They should be above to solve any coding problem with just the documentation.
- Ekaros 2y ago
  Isn't point of the training that they already have all the information they could have. So they do not need the Internet as on Internet there would only be information they already "know"...
kristopolous 2y ago
I used to be able to. The web's made me lazy. I was better before it and I'm better when I don't use it. Really, the stuff you think helps you is often just holding you back.
achierius 2y ago
Isn't this how interviews tend to work? So I think a good number of devs would, yes.

rurp 2y ago

I recently had to do a one-off task using SQL in a way that I wasn't too familiar with. Since I could explain conceptually what I needed but didn't know all the right syntax this seemed like a perfect use case to loop in Claude. The first couple back and forths went ok but it quickly gave me some SQL that was invalid. I sent back the exact error and line number and it responded by changing all of the aliases but repeated the same logical error. I tried again and this time it rewrote more of the code, but still used the exact same invalid operation. At that point I just went ahead and read some docs and other resources and solved things the traditional way. Given all of the hype around LLMs I'm honestly surprised to see top models still failing in such basic and straightforward ways. I keep trying to use LLMs in my regular work so that I'm not missing out on something potentially great but I still haven't hit a point where they're all that useful.

deegles 2y ago
I'm not convinced LLMs will evolve into general AI. The promises that it's just around the corner feels increasingly like a big scam.
- petesergeant 2y ago
  Depends what you mean by evolve. I don't think we'll get general AI by simply scaling LLMs, but I think general AI, if it arrives, will be able to trace its lineage very much back to LLMs. Journeys through the embedding space very much feel like the way forward to me, and that's what LLMs are.
  - giardini 2y ago
    Embedding spaces are one thing, LLMs are quite another. I believe the former are understandable and likely a part of true AGI but the latter a series of hacks, at worst a red herring leading us off the proper track into a deadend.
  - boppo1 2y ago
    Is there a resource I can use to understand the difference between embedding space and latent space?

anandnair 2y ago

Coding, especially the type mentioned in the article (building an app based on a specification)—is a highly complex task. It cannot be completed with a single prompt and an immediate, flawless result. This is why even most software projects (built by humans) go through multiple iterations before they work perfectly. We should consider a few things before asking, "Can AI code like humans?": - How did AI learn to code? What structured curriculum was used? - Did AI receive mentoring from an experienced senior who has solved real-life issues that the AI hasn't encountered yet? - Did the AI learn through hands-on coding or just by reading Stack Overflow? If we want to model AI as being on par with (or even superior to) human intelligence, don’t we at least need to consider how humans learn these complex skills? Right now, it's akin to giving a human thousands of coding books to "read" and "understand," but offering no opportunity to test their programs on a computer. That’s essentially what's happening! Without doing that, I don't think we'll ever be able to determine whether the limitation of current AI is due to its "low intelligence" or because it hasn’t been given a proper opportunity to learn.

DarkmSparks 2y ago
>How did AI learn to code? It didn't, it's just very good at copying already existing code and tweeking it a bit. >Did AI receive mentoring from an experienced senior It doesnt even comprehend what an experienced senior is, all it cares about is how frequently certain patterns occurred in certain circumstances. >Did the AI learn through hands-on coding or just by reading Stack Overflow? it "learnt" by collecting a large database of existing code, most of which is very low quality open source proofs of concept, then spits out the bits that are probably related to a question.
- anandnair 2y ago
  I think we're drastically oversimplifying what "pattern matching" means. It is also one of the fundamental mechanisms by which the human brain operates. I believe we are consciously (or perhaps subconsciously) conditioned to think that human "logic" and "reasoning" are several degrees more advanced than pattern matching. However, I don't think this is true. The fundamental difference lies in how patterns are formed in each case. For LLMs, all they know are the patterns they observe in "words" - that is the only "sense" they possess. But for humans, pattern recognition involves continuously ingesting and identifying patterns across our five primary senses—not just separately, but simultaneously. For example, when an LLM describes something as "ball-shaped," it cannot feel the shape of a ball because it lacks another sense to associate with the word "ball-shaped." In contrast, humans have the sense of touch, allowing them to associate the word or sound pattern "ball" with the physical sensation of holding a ball.

DarkmSparks 2y ago

LLMs will never solve this problem, they are basically just glorified copy & paste engines, solving real code problems requires invention, even for most basic tasks. The best they will manage in their current direct is reason they don't have the capability or capacity to actually solve the problem rather than just getting it wrong the vast majority of the time.

sumedh 2y ago
> LLMs will never solve this problem We should revisit this comment in 5 years.

internet101010 2y ago

I believe it. I couldn't even get o1 or claude 3.5 to write a tampermonkey script that would turn off auto-scroll to bottom in LibreChat, even when uploading the html and javascript as context. Apparently it has to do with overflow anchor or something in React? Idk. I gave up.

throwaway290 2y ago
Unless it works by literally scrolling with JS, I bet some strategic bit of CSS should do it...

simonw 2y ago

I find the framing of this story quite frustrating. The purpose of new benchmarks is to gather tasks that today's LLMs can't solve comprehensively. It an AI lab built a benchmark that their models scored 100% on they would have been wasting everyone's time! Writing a story that effectively says "ha ha ha, look at OpenAI's models failing to beat the new benchemark they created!" is a complete misunderstanding of the research.

mckngbrd 2y ago
Shhh ... you're spoiling everybody's confirmation bias against LLMs. They are obviously terrible at coding, just as we have known all along, and everybody should laugh at them. Nothing to see here!
- johnnyanmac 2y ago
  As long as these companies keep pretending AI is ready to replace humans, I will be biased against lies, thank you.
- player1234 2y ago
  Since you are one of the cool kids in the know, can you share the road map to profitability and even better the expected/hyped ROI? Without extrpolations into science fiction, please.

siliconc0w 2y ago

Interesting that Claude wins despite the other models being more expensive and doing much better in the traditional benchmarks.

ChrisArchitect 2y ago

Previously on source: https://news.ycombinator.com/item?id=43086347 https://news.ycombinator.com/item?id=43086347

ginvok 2y ago

AI don't "solve" problems, best it can do is remember them. Ask them to solve anything new that's challenging and it starts to hallucinate. At least currently.

ilrwbwrkhv 2y ago
And I'm ashamed that OpenAI and Sam altman are walking around talking about AGI. And I'm so... disillusioned by the entire tech community that they have fallen for it or they at least pretend to believe it. It's like LinkedIn Where everybody pretends to be cringe, positivity, people. Even though they know it's cringe and nobody believes it.
- leosanchez 2y ago
  Whenever AI hallucinates a little complex SQL or some tool / language it doesn't have much training data on, I think of AGI hype and Sam Altman's words on how AI can be used to cure cancer in near future.
  - ilrwbwrkhv 2y ago
    Instead if they rightly just said it is an useful tool to be used by researchers to help them like a smart calculator for big data, it would be so much more honest and correct.

emorning3 2y ago

I dont understand why anyone would even think that the current crop of AI is capable of planning or reason on its own. Transformers with memory would be different story. But, no memory, no capability to reason. End of story, right?

numba888 2y ago

Not sure what they found. Either model is unable, or they were unable to solve the tasks using models. Loos like they used strait questions and not Chain of Thoughts. The result for the same model depends on how you ask. The tasks probably required more thinking under the hood than model is allowed to do in one request. More interesting would be if model is capable of solving given enough time. Using multiple requests orchestrated by some framework automatically.

rsynnott 2y ago

> OpenAI researchers have admitted that even the most advanced AI models still are no match for human coders — even though CEO Sam Altman insists they will be able to beat "low-level" software engineers by the end of this year This is the “self-driving cars next year, definitely” of the 20s, at this point.

floppiplopp 2y ago

LLMs are still just text generators. These are statistical models that cannot think or solve logical problems. They might fool people, as Weizenbaum's "Eliza" did in the late 60s, by generating code that sort of runs sometimes, but identifying and solving a logic problem is something I reliably see these things fail at.

tomduncalf 2y ago
Have you tried the latest models, using them with Cursor etc? They might not be truly intelligent but I’d be surprised if an SWE can’t see that they are already offering a lot of value. They probably can’t solve totally novel problems but they are good at transposing existing solutions to new domains. I’ve built some pretty crazy stuff with just prompts - granted I can prompt with detailed technical instructions when needed as I’m a SWE, similar to instructing a junior. I’ve built prototypes which would take days in hours which to me is hugely exciting. The code quality of pure AI generated code isn’t great but my approach right now is to use that to prototype things mostly with prompts (it takes as much time to build a prototype as it would to create a mock up or document explaining the idea previously) then once we are committed to it, I’ll rebuild it mostly by hand but using Cursor to help.

imperial_note 2y ago

The article concludes that LLMs are "not skilled enough at software engineering to replace real-life people quite yet". Yet Claude 3.5 sonnet "earned" $403.325,00 according to the paper referenced. That is $403k worth of labour potentially replaced.

kjrfghslkdjfl 2y ago

[dead]

casey2 2y ago

"Here's the choice you have when you are faced with something new. You can take this technological advance and decide "This is a better way of doing the stuff I'm doing now and I can use this to continue on the path that I'm going", so that's staying in the pink plane , or you can say "This is not a better old thing, this is almost a new thing and I wonder what that new thing is trying to be" and if you do that there's a chance of actually perhaps gaining some incredible leverage over simply optimizing something that can't be optimized very much. - Kay Current LLMs will change the world, but it won't be by completing pull requests quickly. Although a "stargate level" LLM could accelerate pink plane traversal so much that you don't even need to find the correct usecase. LLM scaling will be the computer graphics scaling of this generation. In terms of intelligence gpt4 based o3 is but a postage stamp. As LLMs scale a picture of intelligence will emerge.

prompt_overflow 2y ago

This shows how unrealistic, inaccurate and pointless coding benchmarks are. I also include similar code interview platforms like leetcode, hackerrank and so on.

AbstractH24 2y ago

Increasingly I think that the impact of generative AI is going to more of an incremental form of disruption than revolutionary. More like spreadsheets than the printing press. Spreadsheets becoming mainstream made it easy to do computing that once took a lot of manual human labor quite quickly. And it made plenty of jobs and people who do them obsolete. But they didn’t upend society fundamentally or the need for intelligence and they didn’t get rolled out overnight.

halis 2y ago

The biggest scam in AI is Salesforce. They’re going to take some crappy model that they made or simply switch to a better open source model. Then they’re going to make a large deal with a cloud provider to spin up all these models for all their customers and then re-sell it as AI to their customers for 100x what OpenAI gets per month. And the quality will be lower.

ryandvm 2y ago

LLMs at this point are just a great replacement for Stack Overflow. If what you're doing has been heavily documented and you just need a primer or some skeletal sample code, the LLMs are great. They are not creative at all, but 99% of my job is not creative either.

ineedasername 2y ago

The models weren't allowed to access the internet, meaning they couldn't just crib similar answers that'd been posted online So… not the same basic tools that a human has when coding?

spartanatreyu 2y ago
Well if the tasks are based on tasks that were already asked on freelancing sites, the LLMs could just google the task and copy-paste the solution.

darepublic 2y ago

To me o1 is pretty good. I dunno how it would digest an entire codebase and solve a bug in it. Those details weren't obvious to me from the article above. But o1 has certainly been very valuable to me in coding in new languages on the fly.

scotty79 2y ago

> SWE-Lancer, built on more than 1,400 software engineering tasks from the freelancer site Upwork What's not mentioned here (I think) is that tasks in this benchmark are priced and they sum up to million dollars. And current AIs were able to earn nearly half of that. So while technically they can't solve most problems (yet) they are already perfectly capable of taking about 40% of the food off your plate.

contractorwolf 2y ago

To all those devs saying "i tried it and it wasnt perfect the first time, so I gave up", I am reminded of something my father used to say: "A bad carpenter blames his tools" So AI not going to answer your question right on its first attempt in many cases. It is forced to make a lot of assumptions based on the limited info you gave it, some of those may not match your individual case. Learn to prompt better and it will work better for you. It is a skill, just like everything else in life. Imagine going into a job today and saying "i tried google but it didnt give me what I was looking for as the first result, so I dont use google anymore". I just wouldnt hire a dev that couldnt learn to use AI as a tool to get there job done 10x faster. If that is your attitude, 2026 might really be a wake-up call for your new life.

gar1t 2y ago
One of the reasons SO works is that the correct or best answer tends to move toward the top of the list. AI struggles to do this reliably - and so its closer to SO where the answers are randomly selected and you have to try a few to get the one that's correct. Also, I bet your father imagined the carpenter's toolbox full of well accepted useful tools. For many of us, non-bad carpenters, AI hasn't made the cut yet.
- contractorwolf 2y ago
  All I am saying is that if you are expecting it to fail for you, that is absolutely what it will do. You weren't good at typing on your first day either. I went from a decent engineer with 20 years experience to a 10x engineer able to take on any problem, in less than a year. All because I learned how to use the tool effectively. Just dont give up, get back on that bike and keep peddling. I promise it will amaze you if you give it a chance. This is a tool, just like all the other ones you have learned, but it will make you a far better engineer. It can fill all those gaps in your understanding of code. You can ask it all those questions you are unwilling to ask your colleagues, because you think you will sound dumb for not knowing. You can ask it to explain everything again if you still dont get it. It is powerful if you know how to use it.

tiberriver256 2y ago

... And then they tried sonnet 3.7