1/13/2025 at 2:03:37 AM
The demo on the homepage for the completion of the findMaxElement function is a good example of what is to come. Or maybe where we are at now?The six lines of Python suggested for that function can also be replaced with a simple “return max(arr)”. The suggested code works but is absolute junior level.
I am terrified of what is to come. Not just horrible code but also how people who blindly “autocomplete” this code are going to stall in their skill level progress.
You may score some story points but did you actually get any better at your craft?
by st3fan
1/13/2025 at 4:03:59 AM
This is self correcting. Code of this quality won't let you ship things. You are forced to understand the last 20%-30% of details the LLM can't help you with to pass all your tests. But, it also turns out, to understand the 20% of details the LLM couldn't handle, you need to understand the 80% the LLM could handle.I'm just not worried about this, LLMs don't ship.
by tippytippytango
1/13/2025 at 10:06:36 AM
In the case where it write functionally "good enough" code that performs terribly, it rewards the LLM vendor...since the LLM vendor is also often your IaC vendor. And now you need to buy more infra.by tyingq
1/13/2025 at 2:56:53 PM
That's one hell of a synergy. Win-win-loseby HPsquared
1/13/2025 at 2:56:35 PM
I sense a new position coming up: slop cleanup engineerby grahamj
1/13/2025 at 3:19:41 PM
So an engineer.by cootsnuck
1/13/2025 at 3:01:02 PM
This needs to be shouted from the rooftops. If you could do it yourself then LLMs can be a great help, speeding things up, offering suggestions and alternatives etc.But if you’re asking for something you don’t know how to do you might end up with junk and not even know it.
by grahamj
1/13/2025 at 3:23:36 PM
But if that junk doesn't work (which it likely won't for any worthwhile problem) then you have to get it working. And to get it working you almost always have to figure out how the junk code works. And in that process I've found is where the real magic happens. You learn by fixing, pruning, optimizing.I think there's a whole meta level of the actual dynamic between human<>LLM interactions that is not being sufficiently talked about. I think there's, potentially, many secondary benefits that can come from using them simply due to the ways you have to react to their outputs (if a person decides to rise to that occasion).
by cootsnuck
1/13/2025 at 7:47:26 PM
If the junk doesn't work right from the beginning, yes. The problem is that sometimes the junk might look like it works at first, and then later you find out that it doesn't, and you ended up having to make urgent fixes on a Friday night.> And in that process I've found is where the real magic happens
It might be good way to learn if there's someone who's supervising the process, so they _know_ that the code is incorrect, and tells you to figure out what's wrong and how to fixes.
If you are shipping this stuff yourself, this sounds like a way of deploying giant foot-guns into production.
I still think it's a better to learn if you try to understand the code from the beginning (in the same way that a person should try to understand code they read from tutorials and stackoverflow), rather than delaying the learning until something doesn't work. This is like trying to make yourself do reinforcement learning on the outputs of an LLM, which sounds really inefficient to me.
by powersnail
1/14/2025 at 3:14:19 PM
I see what you’re saying. Maybe in a novice or learning type situation having the LLM generate code you need to check for errors could be educational. We all learn from debugging afterall. On the flip I suspect for most people course questions might be better for that however. For those already good at the craft I agree there might be some unexplored secondary effects.What I find (being in the latter category) is most LLM code output falls on the spectrum of “small snippets that work but wouldn’t have taken me long to type out anyway” to “large chunk that saves me time to write but that I have to thoroughly check/test/tweak”. In other words, the more time it saves typing the more time I have to spend on it afterwards. Novices probably spend more time on the former part of that spectrum and experienced devs on the latter. I suspect the average productivity increase across the spectrum is fairly level which means the benefits don’t really scale with user ability.
I think this tracks with the main thing people need to understand about LLMs: they are a tool. Like any tool simply having access to it doesn’t automatically make you good at the thing it helps with. It might help you learn and it might help you do the thing better, but it will not do your job for you.
by grahamj
1/14/2025 at 5:41:06 AM
There are real dangers in code that appears to run but contains sneaky problems. I once asked ChatGPT to take a data set and run a separate t-test on each group. The code it generated first took the average of each group and then ran the test on that one value. The results were wrong, but the code ran and handed off results to my downstream analysis.by Breza
1/13/2025 at 4:07:58 PM
Wait till they come with auto review/merge agents, or maybe there already is. gulpby shriek
1/13/2025 at 2:58:25 AM
On the other hand it might become a next level of abstraction.Machine -> Asm -> C -> Python -> LLM (Human language)
It compiles human prompt into some intermediate code (in this case Python). Probably initial version of CPython was not perfect at all, and engineers were also terrified. If we are lucky this new "compiler" will be becoming better and better, more efficient. Never perfect, but people will be paying the same price they are already paying for not dealing directly with ASM.
by shcheklein
1/13/2025 at 3:48:27 AM
> Machine -> Asm -> C -> Python -> LLM (Human language)Something that you neglected to mention is, with every abstraction layer up to Python, everything is predictable and repeatable. With LLMs, we can give the exact same instructions, and not be guaranteed the same code.
by sdesol
1/13/2025 at 5:29:24 AM
I’m not sure why that matters here. Users want code that solves their business need. In general most don’t care about repeatability if someone else tries to solve their problem.The question that matters is: can businesses solve their problems cheaper for the same quality, or at lower quality while beating the previous Pareto-optimal cost/quality frontier.
by theptip
1/13/2025 at 6:11:45 AM
Recognizable repetition can be abstracted, reducing code base and its (running) support cost.The question that matters is: will businesses crumble due to overproduction of same (or lower) quality code sooner or later.
by thesz
1/14/2025 at 5:58:54 AM
Sure. You seem to think that LLMs will be unable to identify abstraction opportunities if the code is not identical; that’s not obvious to me. Indeed there are some good (but not certain) reasons to think LLMs will be better at broad-not-deep stuff like “load codebase into context window and spot conceptual repetition”. Though I think the creative insight of figuring out what kind of abstraction is needed may be the spark that remains human for a while.Also, maybe recognizing the repetition remains the human's job, but refactoring is exponentially easier and so again we get better code as a result.
Seems to me to be pretty early to be making confident predictions about how this is all going to pan out.
by theptip
1/13/2025 at 6:40:24 AM
> The question that matters is: will businesses crumble due to overproduction of same (or lower) quality code sooner or later.but why doesn't that happen today? Cheap code can be had by hiring in cheap locations (outsourced for example).
The reality is that customers are the ultimate arbiters, and if it satisfies them, the business will not collapse. And i have not seen a single customer demonstrate that they care about the quality of the code base behind the product they enjoy paying for.
by chii
1/13/2025 at 2:26:54 PM
> And i have not seen a single customer demonstrate that they care about the quality of the code base behind the product they enjoy paying for.The code quality translates to speed of introduction of changes, fixes of defects and amount of user-facing defects.
While customers may not express any care about code quality directly they can and will express (dis)satisfaction with performance and defects of the product.
by thesz
1/13/2025 at 12:24:29 PM
It happens today. However, companies fail for multiple problems that come together. Bad software quality (from whatever source) is typically not a very visible one among them because when business people take over, they only see (at most) that software development/maintenance cost more money that it could yield.by carschno
1/13/2025 at 9:56:36 AM
It is happening. There is a lot of bad software out there. Terrible to use, but still functional enough that it keeps selling. The question is how much crap you can pile on top of that already bad code before it falls apart.by OvbiousError
1/13/2025 at 7:29:08 AM
> Cheap code can be had by hiring in cheap locations (outsourced for example).If you outsource and like what you get, you would assume the place you outsourced to can help provide continued support. What assurance do you have with LLMs? A working solution doesn't mean it can be easily maintained and/or evolved.
> And i have not seen a single customer demonstrate that they care about the quality of the code base behind the product they enjoy paying for.
That is true, but they will complain if bugs cannot be fixed and features are added. It is true that customers don't care, and they shouldn't, until it does matter, of course.
The challenge with software development isn't necessarily with the first iteration, but rather it is with continued support. Where I think LLMs can really shine is in providing domain experts (those who understand the problem) with a better way to demonstrate their needs.
by sdesol
1/13/2025 at 6:03:22 PM
Recognizable repetition can be abstracted... which is the whole idea behind training, isn't it?
The question that matters is: will businesses crumble due to overproduction of same (or lower) quality code sooner or later.
The problem is really the opposite -- most programmers are employed to create very minor variations on work done either by other programmers elsewhere, by other programmers in the same organization, or by their own younger selves. The resulting inefficiency is massive in human terms, not just in managerial metrics. Smart people are wasting their lives on pointlessly repetitive work.
When it comes to the art of computer programming, there are more painters than there are paintings to create. That's why a genuinely-new paradigm is so important, and so overdue... and it's why I get so frustrated when supposed "hackers" stand in the way.
by CamperBob2
1/13/2025 at 9:21:08 PM
>> Recognizable repetition can be abstracted
> ... which is the whole idea behind training, isn't it?
Also, I bet that LLM's are not trained to abstract. In my experience they lately are trained to engage users in pointless dialogue as long as possible.
by thesz
1/13/2025 at 10:28:56 PM
No, only the spec is important. How the software implements the spec is not important in the least. (To the extent that's not true, fix the spec!)Nor is whether the implementation is the same from one build to the next.
by CamperBob2
1/13/2025 at 1:18:40 PM
LLMs use pseudo-random numbers. You can set the seed and get exactly the same output with the same model and input.by compumetrika
1/13/2025 at 5:01:12 PM
you won't because floating point arithmetic isn't associativeand the GPU scheduler isn't deterministic
by blibble
1/13/2025 at 9:02:02 PM
You can set PyTorch to deterministic mode with a small performance penalty: https://pytorch.org/docs/stable/notes/randomness.html#avoidi...Unfortunately, this is only deterministic on the same hardware, but there is no reason why one couldn't write reasonably efficient LLM kernels. It just has not been a priority.
Nevertheless, I still agree with the main point that it is difficult to get LLMs to produce the same output reliably. A small change in the context might trigger all kinds of changes in the generated code.
by threeducks
1/13/2025 at 8:53:49 AM
> > Machine -> Asm -> C -> Python -> LLM (Human language)> Something that you neglected to mention is, with every abstraction layer up to Python, everything is predictable and repeatable.
As long as you consider C and dragons flying out of your nose predictable.
(Insert similar quip about hardware)
by zurn
1/13/2025 at 4:39:03 PM
There is no reason to assume that say C compiler generates the same machine code for the same source code. AFAIK, a C compiler that chooses randomly between multiple C-semantically equivalent sequences of instructions is a valid C compiler.by zajio1am
1/13/2025 at 7:23:56 AM
With LLMs, we can give the exact same instructions, and not be guaranteed the same code.That's something we'll have to give up and get over.
See also: understanding how the underlying code actually works. You don't need to know assembly to use a high-level programming language (although it certainly doesn't hurt), and you won't need to know a high-level programming language to write the functional specs in English that the code generator model uses.
I say bring it on. 50+ years was long enough to keep doing things the same way.
by CamperBob2
1/13/2025 at 6:34:08 PM
Even compiling code isn't deterministic given different compilers and different items installed on a machine can influence the final resulting code, right? Ideally they shouldn't have any noticeable impact, but in edge cases it might, which is why you compile your code once during a build step and then deploy the same compiled code to different environments instead of compiling it per environment.by SkyBelow
1/13/2025 at 12:57:03 PM
> With LLMs, we can give the exact same instructions, and not be guaranteed the same code.Set temperature appropriately, that problem is then solved, no?
by jsjohnst
1/13/2025 at 5:03:27 PM
No, it is much more involved and not all providers allow the necessary tweakings. This means you will need to use local models (with hardware caveats) which will require us to ask:- Are local models good enough?
- What are we giving up for deterministic behaviour?
For example, will it be much more difficult to write prompts. Will the output be nonsensical and more.
by sdesol
1/13/2025 at 8:21:28 AM
Aren't some models deterministic with temperature set to 0?by omgwtfbyobbq
1/13/2025 at 4:43:17 AM
assuming you have full control over which compiler youre using for each step ;)What's to say LLMs will not have a "compiler" interface in the future that will reign in their variance
by 12345hn6789
1/13/2025 at 5:10:51 AM
> assuming you have full control over which compiler youre using for each step ;)With existing tools, we know if we need to do something, we can. The issue with LLMs, is they are very much black boxes.
> What's to say LLMs will not have a "compiler" interface in the future that will reign in their variance
Honestly, having a compiler interface for LLMs isn't a bad idea...for some use cases. What I don't see us being able to do is use natural language to build complex apps in a deterministic manner. Solving this problem would require turning LLMs into deterministic machines, which I don't believe will be an easy task, given how LLMs work today.
I'm a strong believer in that LLMs will change how we develop and create software development tools. In the past, you would need Google and Microsoft level of funding to integrate natural language into a tool, but with LLMs, we can easily have LLMs parse input and have it map to deterministic functions in days.
by sdesol
1/13/2025 at 4:42:39 AM
It may be a “level of abstraction”, but not a good one, because it is imprecise.When you want to make changes to the code (which is what we spend most of our time on), you’ll have to either (1) modify the prompt and accept the risk of using the new code or (2) modify the original code, which you can’t do unless you know the lower level of abstraction.
Recommended reading: https://ian-cooper.writeas.com/is-ai-a-silver-bullet
by vages
1/13/2025 at 3:16:47 AM
Yup!No goal to become a programmer– But I like to build programs.
Build a rather complex AI-ecosystem simulator with me as the director and GPT-4 now Claude 3.5 as the programmer.
Would never have been able to do this beforehand.
by MVissers
1/13/2025 at 3:18:46 AM
I think there is a big difference between an abstraction layer that can improve -- one where you maybe write "code" in prompts and then have a compiler build through real code, allowing that compiler to get better over time -- and an interactive tool that locks bad decisions autocompleted today into both your codebase and your brain, involving you still working at the lower layer but getting low quality "help" in your editor. I am totally pro- compilers and high-level languages, but I think the idea of writing assembly with the help of a partial compiler where you kind of write stuff and then copy/paste the result into your assembly file with some munging to fix issues is dumb.By all means, though: if someone gets us to the point where the "code" I am checking in is a bunch of English -- for which I will likely need a law degree in addition to an engineering background to not get evil genie with a cursed paw results from it trying to figure out what I must have meant from what I said :/ -- I will think that's pretty cool and will actually be a new layer of abstraction in the same class as compiler... and like, if at that point I don't use it, it will only be because I think it is somehow dangerous to humanity itself (and even then I will admit that it is probably more effective)... but we aren't there yet and "we're on the way there" doesn't count anywhere near as much as people often want it to ;P.
by saurik
1/13/2025 at 5:03:47 AM
The most underrated thing I do on nearly every cursor suggestion is to follow up with “are there any better ways to do this?”.by ripped_britches
1/13/2025 at 6:34:04 AM
A deeper version of the same idea is to ask a second model to check the first model’s answers. aider’s “architect” is an automated version of this approach.https://aider.chat/docs/usage/modes.html#architect-mode-and-...
by smcnally
1/13/2025 at 1:42:47 PM
I always ask it to "analyze approached to achieve X and then make a suggestion, no code" in the chat. Then a refinement step where I give feedback on the generated code. I also always try to give it an "out" between making changes and keeping it to same to stave off the bias of action.by avandekleut
1/13/2025 at 3:30:23 PM
Yea, the "analyze and explain but no code yet" approach works well. Let's me audit its approach beforehand.by cootsnuck
1/13/2025 at 9:59:33 AM
I used to know things. Then they made Google, and I just looked things up. But at least I could still do things. Now we have AI, and I just ask it to do things for me. Now I don't know anything and I can't do anything.by 55555
1/13/2025 at 3:49:54 PM
Programmers (and adjacent positions) of late strike me as remarkably shortsighted and myopic.Cheering for remote work leading to loads of new positions being offered overseas opposed to domestically, and now loudly celebrating LLMs writing "boilerplate" for them.
How folks don't see the consequences of their actions is remarkable to me.
by nyarlathotep_
1/14/2025 at 3:34:34 PM
In both cases, you get what you pay for.by yellowapple
1/13/2025 at 1:52:19 PM
I feel like I've seen this comment so many times but actually genuine. The cult like dedication is kind of baffling.by deltaburnt
1/13/2025 at 6:06:48 PM
I think that example says more about the company that chose to put that code as a demo in their homepage.by shihab
1/13/2025 at 4:08:14 AM
LLMs also love to double down on solutions that don't work.Case in point, I'm working on a game that's essentially a website right now. Since I'm very very bad with web design I'm using an LLM.
It's perfect 75% of the time. The other 25% it just doesn't work. Multiple LLMs will misunderstand basic tasks. Let's add properties and invent functions.
It's like you had hired a college junior who insists their never wrong and keeps pushing non functional code.
The entire mindset is whatever it's close enough, good luck.
God forbid you need to do anything using an uncommon node module or anything like that.
by 999900000999
1/13/2025 at 7:08:20 AM
> LLMs also love to double down on solutions that don't work.“Often wrong but never in doubt” is not proprietary to LLMs. It’s off-putting and we want them to be correct and to have humility when they’re wrong. But we should remember LLMs are trained on work created by people, and many of those people have built successful careers being exceedingly confident in solutions that don’t work.
by smcnally
1/13/2025 at 2:58:17 PM
The issue is LLMs never say:"I don't know how to do this".
When it comes to programming. Tell me you don't know so I can do something else. I ended up just refactoring my UX to work around it. In this case it's a personal prototype so it's not a big deal.
by 999900000999
1/13/2025 at 6:20:03 PM
That is definitely an issue with many LLMs. I've had limited success including instructions like "Don't invent facts" in the system prompt and more success saying "that was not correct. Please answer again and check to ensure your code works before giving it to me" within the context of chats. More success still comes from requesting second opinions from a different model -- e.g. asking Claude's opinion of Qwen's solution.To the other point, not admitting to gaps in knowledge or experience is also something that people do all the time. "I copied & pasted that from the top answer in Stack Overflow so it must be correct!" is a direct analog.
by smcnally
1/13/2025 at 2:08:36 PM
So now you have an overconfident human using an overconfident tool, both of which will end up coding themselves into a corner? Compilers at least, for the most part, offer very definitive feedback that act as guard rails to those overconfident humans.Also, let's not forget LLMs are a product of the internet and anonymity. Human interaction on the internet is significantly different from in person interaction, where typically people are more humble and less overconfident. If someone at my office acted like some overconfident SO/reddit/HN users I would probably avoid them like the plague.
by deltaburnt
1/13/2025 at 6:57:23 PM
A compiler in the mix is very helpful. That and other sanity checks wielded by a skilled engineer doing code reviews can provide valuable feedback to other developers and to LLMs. The knowledgeable human in the loop makes the coding process and final products so much better. Two LLMs with tool usage capabilities reviewing the code isn't as good today but is available today.The LLMs overconfidence is based on it spitting out the most-probable tokens based on its training data and your prompt. When LLMs learn real hubris from actual anonymous internet jackholes, we will have made significant progress toward AGI.
by smcnally
1/13/2025 at 2:43:17 PM
> people who blindly “autocomplete” this code are going to stall in their skill level progressAI is just going to widen the skill level bell curve. Enables some people to get away with far more mediocre work than before, but also enables some people to become far more capable. You can't make someone put in more effort, but the ones who do will really shine.
by generalizations
1/13/2025 at 1:18:21 PM
Anybody care to comment whether the quality of the existing code influences how good the AI's assistance is? In other words, would they suggest sloppy code where the existing code is sloppy and better (?) code when the existing code is good?by dizhn
1/13/2025 at 3:28:26 PM
What do you think? (I don't mean that in a snarky way.) Based on how LLMs work, I can't see how that would not be the case.But in my experience there are nuances to this. It's less about "good" vs "bad"/"sloppy" code and more about discernable. If it's discernably sloppy (i.e. the type of sloppy a beginning programmer might do which is familiar to all of us) I would say that's better than opaque "good" code (good really only meaning functional).
These things predict tokens. So when you use them, help them increase their chances of predicting the thing you want. Good comments on code, good function names, explain what you don't know, etc. etc. The same things you would ideally do if working with another person on a codebase.
by cootsnuck
1/13/2025 at 2:23:21 PM
Reminds me of the 2000s outsourcing hype. I made a lot of money cleaning up that mess. Entire projects late, buggy, unreadable and unmaintainable.Business pay big when they need to recover from that kind of thing and save face to investors.
by sirsinsalot
1/15/2025 at 5:35:23 AM
As a cybersecurity professional (as in, the more cybersecurity problems there are, the less likely I am to ever find myself out of a job), I'm rooting for AI!by aja12
1/13/2025 at 3:35:53 AM
Keep in mind that this is the stupidest the LLM will ever be and we can expect major improvements every few months. On the other hand junior devs will always be junior devs. At some point python and C++ will be like assembly now, something that’s always out there but not something the vast majority of developers will ever need to read or write.by svachalek
1/13/2025 at 4:28:25 AM
My experience observing commercial LLM's since the release of GPT-4 is actually the opposite of this.Sure, they've gotten much cheaper on a per-token basis, but that cost reduction has come with a non-trivial accuracy/reliability cost.
The problem is, tokens that are 10x cheaper are still useless if what they say is straight up wrong.
by llamaLord
1/13/2025 at 5:20:27 AM
> Sure, they've gotten much cheaper on a per-token basis, but that cost reduction has come with a non-trivial accuracy/reliability cost.This only holds for OpenAI.
by maeil
1/13/2025 at 5:18:51 AM
> Keep in mind that this is the stupidest the LLM will ever be and we can expect major improvements every few months.We have seen no noticable improvements (at usable prices) for 7 months, when the original Sonnet 3.5 came out.
Maybe specialized hardware for LLM inference will improve so rapidly that o1 (full) will be quick and cheap enough a year from now, but it seems extremely unlikely. For the end user, the top models hadn't gotten cheaper for kore than a year until the release of Deepseek v3 a few weeks ago. Even that is currently very slow at non-Deepseek providers, and who knows just how subsidized the pricing and speed at Deepseek itself is, given political interests.
by maeil
1/13/2025 at 4:08:33 PM
No major AI advancements for 7 months? Guess everyone's jobs are safe for another year, and after that we're all dead?by Eliezer
1/16/2025 at 5:17:06 AM
> No major AI advancements for 7 months?For my caveat "at usable prices", no, there haven't been any. o1 (full) and now o3 have been advancements, but are hardly available for real-world use given limitations and pricing.
by maeil
1/13/2025 at 3:59:22 AM
> we can expect major improvements every few months.I'm not sure this is grounded in reality. We've already seen articles related to how OpenAI is behind schedule with GPT-5. I do believe things will improve over time, mainly due to advancements in hardware. With better hardware, we can better brute force correct answers.
> junior devs will always be junior devs
Junior developers turn into senior developers over time.
by sdesol
1/13/2025 at 6:46:32 AM
> I'm not sure this is grounded in reality. We've already seen articles related to how OpenAI is behind schedule with GPT-5.Progress by Google, meta, Microsoft, Qwen and Deepseek is unhampered by OpenAI’s schedule. Their latest — including Gemini 2.0, Llama 3.3, Phi 4 — and the coding fine tunes that follow are all pretty good.
by smcnally
1/13/2025 at 7:12:37 AM
> unhampered by OpenAI’s scheduleSure, but if the advancements are to catch up to OpenAI, then major improvements by other vendors are nice and all, but I don't believe that was what the commenter was implying. Right now the leaders in my opinion are OpenAI and Anthropic and unless they are making major improvements every few months, the industry as a whole is not making major improvements.
by sdesol
1/13/2025 at 8:04:19 PM
OpenAI and Anthropic are definitely among the leaders. Playing catch-up to these leaders' mind-share and technology is some of the motivation for others. Calling the progress being made in the space by Google (Gemini), MSFT (Phi), Meta (llama), Alibaba (Qwen) "nice and all" is a position you might be pleasantly surprised to reconsider if this technology interests you. And don't sleep on Apple and AMZ -In the space covered by Tabby, Copilot, aider, Continue and others, capabilities continue to improve considerably month-over-month.
In the segments of the industry I care most about, I agree 100% with what the commenter said w/r/t expecting major improvements every few months. Pay even passing attention to huggingface and github and see work being done by indies as well as corporate behemoths happening at breakneck pace. Some work is pushing the SOTA. Some is making the SOTA more widely available. Lots of it is different approaches to solving similar challenges. Most of it benefits consumers and creators looking use and learn from all of this.
by smcnally
1/13/2025 at 1:08:32 PM
I wish this was true as being a shitty programmer who is old , I would benefit from this as much as anyone here but I think it is delusional.From my experience I wouldn't even say LLMs are stupid. The LLM is a carrier and the intelligence is in the training data. Unfortunately, the training data is not going to get smarter.
If any of this had anything to do with reality then we should already have a programming specific model only trained on CS and math textbooks that is awesome. Of course, that doesn't work because the LLM is not abstracting the concepts how we normally think of in order to be stupid or intelligent.
It hardly shocking that next token prediction on math and CS textbooks is of limited use. You hardly have to think about it to see how flawed the whole idea is.
by harvodex
1/13/2025 at 12:49:04 PM
GitHub Copilot came out in 2021.by n144q
1/13/2025 at 6:54:00 AM
> I am terrified of what is to come.Don't worry. Like everything else in life, you get what you pay for.
by csomar
1/13/2025 at 2:41:55 AM
The silver lining is that the value of your skills is going up.by MaKey
1/13/2025 at 11:18:48 AM
> The suggested code works but is absolute junior levelThis isn't far the current status quo. Good software companies pay for people who write top quality code, and the rest pay juniors to work far above their pay grade or offshore it to the cheapest bidder. Now it will be offloaded to LLM's instead. Same code, different writer, same work for a contractor who knows what they're doing to come and fix later.
And so the cycle continues.
by worble
1/13/2025 at 3:52:15 AM
I mean you can treat it as just a general pseudocode-ish implementation of an O(n) find_max algorithm. Tons of people use Python to illustrate algorithms.(Not to hide your point though -- people please review your LLM-generated code!)
by runeblaze