This comment is ~3 years late. Every model since gpt3 has had the entirety of available code in their training data. That's not a gotcha anymore.
We went from chatgpt's "oh, look, it looks like python code but everything is wrong" to "here's a full stack boilerplate app that does what you asked and works in 0-shot" inside 2 years. That's the kicker. And the sauce isn't just in the training set, models now do post-training and RL and a bunch of other stuff to get to where we are. Not to mention the insane abilities with extended context (first models were 2/4k max), agentic stuff, and so on.
These kinds of comments are really missing the point.
I've had little success with Agentic coding, and what success I have had has been paired with hours of frustration, where I'd have been better off doing it myself for anything but the most basic tasks.
Even then, when you start to build up complexity within a codebase - the results have often been worse than "I'll start generating it all from scratch again, and include this as an addition to the initial longtail specification prompt as well", and even then... it's been a crapshoot.
I _want_ to like it. The times where it initially "just worked" felt magical and inspired me with the possibilities. That's what prompted me to get more engaged and use it more. The reality of doing so is just frustrating and wishing things _actually worked_ anywhere close to expectations.
The more I've used it, the more I've disliked how poor the results it's produced, and the more I've realised I would have been better served by doing it myself and following a methodical path for things that I didn't have experience with.
It's easier to step through a problem as I'm learning and making small changes than an LLM going "It's done, and production ready!" where it just straight up doesn't work for 101 different tiny reasons.
My preferred approach to avoid that outcome is to divide & conquer the problem. Ask the LLM to implement each small bit in the order you'd implement it yourself given what you know about the codebase.
The subjects in the study you are referencing also believed that they were more productive with it. What metrics do you have to convince yourself you aren't under the same illusionary bias they were?
LLM's still give subpar results with ffmpeg. For example when I asked Sonnet to trim a long video with ffmpeg, it put the input file parameter before the start time parameter, which triggers an unnecessary decode of the video file. [1]
Sure, use the LLM to get over the initial hump. But ffmpeg's no exception to the rule that LLM's produce subpar code. It's worth spending a couple minutes reading the docs to understand what it did so you can do it better, and unassisted, next time.
That says more about suboptimal design on ffmpeg's part than it does about the LLM. Most humans can't deal with ffmpeg command lines, so it's not surprising that the LLM misses a few tricks.
You don't have to leave LLM code alone, it's fine to change it — unless, I guess, you're doing some kind of LLM vibe-code-golfing?
But this does remind me of a previous co-worker. Wrote something to convert from a custom data store to a database, his version took 20 minutes on some inputs. Swore it couldn't possibly be improved. Obviously ridiculous because it didn't take 20 minutes to load from the old data store, nor to load from the new database. Over the next few hours of looking at very mediocre code, I realised it was doing an unnecessary O(n^2) check, confirmed with the CTO it wasn't business-critical, got rid of it, and the same conversion on the same data ran in something like 200ms.
But… I only hear of scammers who say, and psychosis sufferers who think, LLMs are *already* that competent.
Future AI? Sure, lots of sane-seeming people also think it could go far beyond us. Special purpose ones have in very narrow domains. But current LLMs are only good enough to be useful and potentially economically disruptive, they're not even close to wildly superhuman like Stockfish is.
Sure. If you ask ChatGPT to play chess, it will put up an amateur-level effort at best. Stockfish will indeed wipe the floor with it. But what happens when you ask Stockfish to write a Space Invaders game?
ChatGPT will get better at chess over time. Stockfish will not get better at anything except chess. That's kind of a big difference.
But even to the general point, there's absolutely no agreement how much better the current architectures can ultimately get, nor how quickly they can get there.
Do they have potential for unbounded improvements, albeit at exponential cost for each linear incremental improvement? Or will they asymptomatically approach someone with 5 years experience, 10 years experience, a lifetime of experience, or a higher level than any human?
If I had to bet, I'd say current models have an asymptomatic growth converging to a merely "ok" performance; and separately claim that even if they're actually unbounded with exponential cost for linear returns, we can't afford the training cost needed to make them act like someone with even just 6 years professional experience in any given subject.
Which is still a lot. Especially as it would be acting like it had about as much experience in every other subject at the same time. Just… not a literal Ahura Mazda.
Nah, trillion dollars is about right for "ok". Percentage point of the global economy in cost, automate 2 percent and get a huge margin. We literally set more than that on actual fire each year.
For "pretty good", it would be worth 14 figures, over two years. The global GDP is 14 figures. Even if this only automated 10% of the economy, it pays for itself after a decade.
For "Ahura Mazda", it would easily be worth 16 figures, what with that being the principal God and god of the sky in Zoroastrianism, and the only reason it stops at 16 is the implausibility of people staying organised for longer to get it done.
... but those "people with actual money to spend" have burned money on fads before. Including on "AI", several times before the current hysterics.
If you're a good actor/psychologist, it's probably a good business model to figure out how to get VC money and how to justify your startup failing so they give you money for the next startup.
"I'm taking this talking dog right back to the pound. It told me to short NVDA, and you should see the buffer overflow bugs in the C++ code it wrote. Totally overhyped. I don't get it."
I remember when I was a kid, people asking a teacher how to spell a word, and the answer was generally "look it up in a dictionary"… which you can only do if you already have shortlist of possible spellings.
*nix man pages are the same: if you already know which tool can solve your problem, they're easy to use. But you have to already have a shortlist of tools that can solve your problem, before you even know which man pages to read.
(I am very confident I am not the only person who has been deterred by ffmpeg's legendarily complex command-line interface. I feel no shame about this at all.)
To be a little more fair... that example is tidily slotted into the EXAMPLES section, under the heading "You can extract images from a video, or create a video from many images".
I don't think most people read the man pages top to bottom. And even if they did, then for as much grief as you're giving ffmpeg, llm has an even larger burden... no man page and the docs weigh in at over 8k lines.
I get the general point that ffmpeg is a powerful, complex tool... but this is a weird fight to pick.
Ffmpeg is genuinely complicated! And the CLI is convoluted (in justifiable, and unfortunate ways).
But if you approach ffmpeg from the perspective of "I know this is possible", you are always correct, and can almost always reach the "how" in a handful of minutes.
You wouldn't have just typed "extract frame at timestamp as jpeg ffmpeg" into Google and used the StackExchange result that comes up first that gives you a command to do exactly that?
My general point is that people say things like "yeah, but this one study showed that programmers over-estimate the productivity gain they get from LLMs so how can you really be sure?"
Meanwhile I've spent the past two years constantly building and implementing things I never would have done because of the reduction in friction LLM assistance gives me.
I wrote about this first two years ago - AI-enhanced development makes me more ambitious with my projects - https://simonwillison.net/2023/Mar/27/ai-enhanced-developmen... - when I realized I was hacking on things with tech like AppleScript and jq that I'd previously avoided.
It's hard to measure the productivity boost you get from "wouldn't have built that thing" to "actually built that thing".
Translated a vague natural language query ("cli, extract frame 13s into video") into something immediately actionable with specific examples and explanations, surfacing information that I would otherwise not know how to search for.
That's what I've done with my ffmpeg LLM queries, anyway - can't speak for simonw!
Search engines have been able to translate "vague natural language queries" into search results for a decade, now. This pre-existing infrastructure accounts for the vast majority of ChatGPT's apparent ability to find answers.
Yet the interface is fundamentally different, the output feels much more like bro pages[0] and it's within a click of clipboarding, one CTRL V away from extracting the 13th second screenshot. I've been using Google the past 24 years and my google-fu has always left people amazed; yet I can no longer bother to go through Stack Exchange's results when an LLM not only spits it out so nicely, but also does the equivalent of a explainshell[1].
Not comparable and I fail to see why going through Google's ads/results would be better?
DuckDuckGo insists on shoving "AI Assist" entries in its results, so I have a reasonable idea of how often LLMs are completely wrong even given search results. The answer's still "more than one time in five".
I did not suggest using Google Search (the company's on record as deliberately making Google Search worse), but there are other search engines. My preferred search engines don't do the fancy "interpret natural language queries" pre-processing, because I'm quite good at doing that in my head and often want to research niche stuff, but there are many still-decent search engines that do, and don't have ads in the results.
Heck, you can even pay for a good search engine! And you can have it redirect you to the relevant section of the top search result automatically: Google used to call this "I'm feeling lucky!" (although it was before URI text fragments, so it would just send you to the top of the page). All the properties you're after, much more cheaply, and you keep the information about provenance, and your answer is more-reliably accurate.
This is the rub for me… people are so quick to forget the original source for a lot of the data these models were trained on, and how easy and useful these platforms were. Now Google will summarize this question for you in an AI overview before you even land on Stack Overflow. It’s killing the network effect of the open web and destroying our crowd sourced platforms in favor of a lossy compression algorithm that will eventually be regurgitating its own entrails.
Not exactly. The real utility value of LLM for programming is to come up with something new. For Space Invaders, instead of using LLM for that, I might as well just manually search for the code online and use that.
To show that LLM actually can provide value for one-shot programming, you need to find a problem that there's no fully working sample code available online. I'm not trying to say that LLM couldn't to that. But just because LLM can come up with a perfectly-working Space Invaders doesn't mean that it could do that.
> The real utility value of LLM for programming is to come up with something new.
That's the goal for these projects anyways. I don't know that its true or feasible. I find the RAG models much more interesting myself, I see the technology as having far more value in search than generation.
Rather than write some markov-chain reminiscent frankenstein function when I ask it how to solve a problem, I would like to see it direct me to the original sources it would use to build those tokens, so that I can see their implementations in context and use my judgement.
"I would like to see it direct me to the original sources it would use to build those tokens"
Sadly that's not feasible with transformer-based LLMs: those original sources are long gone by the time you actually get to use the model, scrambled a billion times into a trained set of weights.
One thing that helped me understand this is understanding that every single token output by an LLM is the result of a calculation that considers all X billion parameters that are baked into that model (or a subset of that in the case of MoE models, but it's still billions of floating point calculations for every token.)
You can get an imitation of that if you tell the model "use your search tool and find example code for this problem and build new code based on that", but that's a pretty unconventional way to use a model. A key component of the value of these things is that they can spit out completely new code based on the statistical patterns they learned through training.
I am aware, and that's exactly why I don't think they're anywhere near as useful for this type of work as the people pushing them want them to be.
I tried to push for this type of model when an org I worked with over a decade ago was first exploring using the first generation of Tensorflow to drive customer service chatbots and was sadly ignored.
I don't understand. For code, why would I want to remix existing code snippets?
I totally get the value of RAG style patterns for information retrieval against factual information - for those I don't want the LLM to answer my question directly, I want it to run a search and show me a citation and directly quote a credible source as part of answering.
For code I just want code that works - I can test it myself to make sure it does what it's supposed to.
> I don't understand. For code, why would I want to remix existing code snippets?
That is what you're doing already. You're just relying on a vector compression and search engine to hide it from you and hoping the output is what you expect, instead of having it direct you to where it remixed those snippets from so you can see how they work to start with and make sure its properly implemented from the get-go.
We all want code that works, but understanding that code is a critical part of that for anything but a throw-away one time use script.
I don't really get this desire to replace critical thought with hoping and testing. It sounds like the pipe dream of a middle manager, not a tool for a programmer.
I don't understand your point. You seem to be saying that we should be getting code from the source, then adapting it to our project ourselves, instead of getting adapted code to begin with.
I'm going to review the code anyway, why would I not want to save myself some of the work? I can "see how they work" after the LLM gives them to me just fine.
The work that you are "saving" is the work of using your brain to determine the solution to the problem. Whatever the LLM gives you doesn't have a context it is used in other than your prompt - you don't even know what it does until after you evaluate it.
If you instead have a set of sources related to your problem, they immediately come with context, usage and in many cases, developer notes and even change history to show you mistakes and adaptations.
You're ultimately creating more work for yourself* by trying to avoid work, and possibly ending up with an inferior solution in the process. Where is your sense of efficiency? Where is your pride as a intellectual?
* Yes, you are most likely creating more work for yourself even if you think you are capable of telling otherwise. [1]
I have a friend who has been doing just that... usually with his company he manages a handful of projects where a bulk of the development is outsourced overseas. This past year, he's outpaced the 6 devs he's had working on misc projects just with his own efforts and AI. Most of this being a relatively unique combination of UX with features that are less common.
He's using AI with note taking apps for meetings to enhance notes and flush out technology ideas at a higher level, then refining those ideas into working experiments.
It's actually impressive to see. My personal experience has been far more disappointing to say the least. I can't speak to the code quality, consistency or even structure in terms of most people being able to maintain such applications though. I've asked to shadow him through a few of his vibe coding sessions to see his workflow. It feels rather alien to me, again my experience is much more disappointing in having to correct AI errors.
No, he's been working on building a larger eLearning solution with some interesting workflow analytics around courseware evaluation and grading. He's been involved in some of the newer LRS specifications and some implementation details to bridge training as well as real world exposure scenarios. Working a lot with first responders, incident response training etc.
I've worked with him off and on for years from simulating aircraft diagnostics hardware to incident command simulation and setting up core infrastructure for F100 learning management backends.
You are reading too much into my comment. My point was that the test (a Space Invaders clone) used to asses the model is irrelevant for some time now. I could have gotten a similar result with Mistral Small a few months ago.
It's amazing that none of you even try to falsify you claims anymore. You can literally just put some of the code in a search engine and find the prior art example:
I don't think they are missing the point, because they're pointing out that the tools are still the most useful for patterns that are extremely widely known and repeated. I use Gemini 2.5 Pro every day for coding, and even that one still falls over on tasks that aren't well known to it (which is why I break the problem down into small parts that I know it'll be able to handle properly).
It's kind of funny, because sometimes these tools are magical and incredible, and sometimes they are extremely stupid in obvious ways.
Yes, these are impressive, and especially so for local models that you can run yourself, but there is a gap between "absolutely magical" and "pretty cool, but needs heavy guiding" depending on how heavily the ground you're treading has been walked upon.
For a heavily explored space, it's like being impressed that you're 2.5 year old M2 with 64 GB RAM can extract some source code from a zip file. It's worth being impressed and excited about the space and the pace of improvement, but it's also worth stepping back and thinking rationally about the specific benchmark at hand.
> because they're pointing out that the tools are still the most useful for patterns that are extremely widely known and repeated
I agree with you, but your take is much more nuanced than what the GP comment said! These models don't simply regurgitate the training set. That was my point with gpt3. The models have advanced from that, and can now "generalise" over the context in ways they could not do ~3 years ago. We are now at a point where you can write a detailed spec (10-20k tokens) for an unseen scripting language, and have SotA models a) write a parser and b) start writing scripts for you in that language, even though it never saw that particular scripting language anywhere in its training set. Try it. You'll be surprised.
Showing off moderately complicated results that are actually not indicative of performance because they are sniped by the training data turns this from a cool demo to a parlor trick.
Stating that, aha, jokes on you, that's the status quo, is an even bigger indictment.
> These kinds of comments are really missing the point.
I disagree. In my experience, asking coding tools to produce something similar to all of the tutorials and example code out there works amazingly well.
Asking them to produce novel output that doesn’t match the training set produces very different results.
When I tried multiple coding agents for a somewhat unique task recently they all struggled, continuously trying to pull the solution back to the standard examples. It felt like an endless loop of the models grinding through a solution and then spitting out something that matched common examples, after which I had to remind them of the unique properties of the task and they started all over again, eventually arriving back in the same spot.
It shows the reality of working with LLMs and it’s an important consideration.
We went from chatgpt's "oh, look, it looks like python code but everything is wrong" to "here's a full stack boilerplate app that does what you asked and works in 0-shot" inside 2 years. That's the kicker. And the sauce isn't just in the training set, models now do post-training and RL and a bunch of other stuff to get to where we are. Not to mention the insane abilities with extended context (first models were 2/4k max), agentic stuff, and so on.
These kinds of comments are really missing the point.