9 ms·

It's great to be amazed by this stuff, but it really just shows how poor human intuition is at these things. > Imagine envisioning Airbnb, and having its whole

It's great to be amazed by this stuff, but it really just shows how poor human intuition is at these things. > Imagine envisioning Airbnb, and having its whole frontend and backend done within 1 minute. ...sigh... You're seeing the tip of an iceberg, and you're like, "wow, it's cold"... but you honestly have no idea. Any tool that you spend less an hour using, you have no idea about. That's it. There's nothing more to say. Use it for longer. Try building larger things. Give me a considered opinion when you've formed one, not a reaction video. There was a spate of this kind of article when chat-gpt first came out, and at the time, it was like: why are we seeing these "I spent 10 seconds with a LLM and I made a potato!" and "I spent 20 minutes with an LLM and made a one-page HTML website!" articles, and none of the "I spent a month with an LLM and build a new programming language", or "I spent two weeks with an LLM and I build a raytracer" articles? Oh they said, "It's too soon, give it time... it's only been out a month. 3 months... 6 months...". You still don't see them. ...because they don't exist. No one has done anything impressive with this stuff; it's fundamentally limited in what it can produce, and the massive productivity benefits you see (30x faster!) are for trivial tasks, not difficult tasks. ...and the modest productivity benefits you get from using an actual copilot don't make articles that are nearly as interesting to get as many clicks. Look, AI can seem magical, but when you have something that seems too good to be true, after using it for an hour, or even a day, maybe it's not the right moment to drop a blog post about how gosh darn amazing it is?

Philpax 3y ago
They're pretty clear that that's not possible with the current state of affairs. They're speculating about a future in which that may happen, which seems pretty reasonable to me - we don't know how much gas is left in this tank, after all.
- ChatGTP 3y ago
  Do you know what the point would be of it spitting out an Airbnb clone ? Very little because Airbnb is a huge business which has to solve a lot of problems outside of copying someone’s code ?
  - Philpax 3y ago
    Again, they're using it as an example of something that might be possible, not saying that this is what you should be doing. ...and even if they were, wouldn't it be nice to focus on solving the other problems you mention instead of writing the code?
    - ChatGTP 3y ago
      AirBNB will have 100x people working out new problems I guess…
zufallsheld 3y ago
There are posts where people tried to create a programming language with chatgpt: - https://smaller.fish/posts/l_plus https://smaller.fish/posts/l_plus - https://www.kdnuggets.com/2023/04/datalang-new-programming-language-data-scientists-chatgpt.html https://www.kdnuggets.com/2023/04/datalang-new-programming-l... - https://github.com/forrestchang/gptlang https://github.com/forrestchang/gptlang - https://6502.is-a.dev/posts/aoc-2022/ https://6502.is-a.dev/posts/aoc-2022/
ainiriand 3y ago
I agree with this statement. My personal experience has been creating MongoDB statements, the easy ones are more or less good, with only a few changes necessary. But if I need a complex one it is always incorrect, it is not able to produce it, no matter how I structure my prompt.
djmips 3y ago
Raytracer seems to me to be a trivial task. Maybe try it with CHATGPT.
- dagw 3y ago
  You can literally type write a simple raytracer in python into chatgpt and it will return fully working code of a simple raytracer that can handle spheres and Lambert shading. To be fair you can type the same thing into google and get a link to fully working ray tracer in python back, so maybe not the best example. edit: What would be interesting would be to see how far you can push ChatGPT by asking for more and more advanced features before it stops giving valid answers. Basically what is the most advanced raytracer currently 'stored' in ChatGPT
yellow_lead 3y ago
From my experience, you hit a wall quickly. You get an error, you tell ChatGPT, it tries to fix it, it can't. Now, you must learn everything ChatGPT wrote, in order to fix the error/bug - just as the Romans did.
- swalsh 3y ago
  But using it to write a first draft is FAR quicker than writing it yourself. Most of the complaints I see seem to be that you can't fully delegate your thinking/work to the AI. That you still need to be involved. But pair programming with the AI gives significant productivity boosts on it's own, and that's valuable.
- test6554 3y ago
  Chat GPT saves me the most time when it incorporates the things I didn’t know, I didn’t know into an answer to my problem even when its answer overall is wrong. It pulls in the unknown unknowns.
- theonlybutlet 3y ago
  Your compiler should show you where your errors are and pasting the error and even the documentation back into ChatGPT it can often resolve it. When it fails at this level I feel it's no different to a junior coder being asked to code above their head. You were bound to hit a ceiling at some point.sometimes breaking down your instructions into clear but detailed steps helps (using advanced terminology ideally).
hobs 3y ago
There's no reason this machine would be good at difficult tasks - they are usually somewhat bespoke, require true creativity and understanding of complex requirements, and oftentimes you'll have no example on the internet - normal programming things. However, there's many reasons it would be good at trivial tasks, and arguably most programmers spend most of their time on said trivial tasks, its just the count of them that's overwhelming; switching between multiple different languages, looking up syntax (at least for me!), and deciding on what's a sensical solution for the use case. Nobody cares about the performance characteristics and just wants it to work. So anyway, I don't think a blog post about how you build a new algorithm to come out of any of the LLMs of today, but that doesn't mean I find them underwhelming.
romanovcode 3y ago
It's not about building a product, it's about saving time by making it do small task 30x faster than yourself (just like author did). Not all tasks, but many many menial tasks. At least that's how I use it and I'm super happy with what it gives me.
- taude 3y ago
  Agree with this, not sure why the parent of this thread thinks you'd use it to output a full business. But helping write at the functional level is game changing: I've built entire GitHub Actions to help improve team effeciencies. Built validation rules. A bunch of Python scripts. Parsed a multi-level JSON response that would have taken a few hours, etc... All in minutes. Imagine knocking off like 15 of these tasks before noon.
  - test6554 3y ago
    I am worried that Chat-GPT productivity levels will become the new norm and when it doesn’t give you what you need you will be underperforming at work.
- dmje 3y ago
  That's exactly right. Any product is essentially made up of a gazillion small tasks, whether setting up a framework or writing a database query. If just some of those small tasks can be made quicker / easier using these tools, then why not? What I find most useful in this space is twofold: 1. Specificity: so yeh, I can look at SO or wherever and see responses to a semi-specific query, say about querying a database. And yes, I could post a question and maybe after a few hours if I'm lucky get a response. But with ChatGPT I can throw in the actual table and column names and get a specific response back. Most often it needs honing, sure, but it's most often a great starting point. 2. Being able to back-query. So the fact that history is maintained means I can say "actually, add in a column called X and can you make sure that the query only returns unique IDs" and - again - I'll get an immediate response without having to go all the way back through the questioning journey again. As with any tool, the extreme responses on either end seem obviously flawed. On the one hand: no, it's not going to replace all developer jobs. On the other, to just completely ignore it, not try it, always assume that "the documentation" is better - this seems blinkered and foolish too. Somewhere in the middle is a sweet spot where human brains collide with these tools and we manage to tease out code that is more quickly researched and saves time but also well considered and treated with a degree of healthy scepticism before deployment.
bjornlouser 3y ago
If there was a supplement you could feed your cat that allowed it to speak a few words like a parrot there would also be endless chatter about the use of that product. It’s amazing. ‘Meh. Let me know when your cat can recite T.S. Eliot’
- lolinder 3y ago
  If said supplement caused people to say things like this about the cat, it would definitely warrant a skeptical take like OP's: > Imagine envisioning Airbnb, and having its whole frontend and backend done within 1 minute. We’re not there yet, but we could be there soon. The difference in scale between toy plane spotter and an Airbnb front end and back end is simply enormous, with a combinatorial explosion of possible states and failure modes. Our existing tech will not scale to that level by just adding more parameters and GPUs. We may or may not have a major paradigm shift in the future that enables it, but this talking cat ain't producing Airbnb in 1 minute.
Foobar8568 3y ago
I have tried to do something I thought really simple this weekend: having a C program on a SoC running Linux with a FPGA component e.g. to pass two integers to the FPGA from HPS with mmap, multiply them, get the result within one clock cycle and without doing much (integrating ) on the bus/bridge. It seems I will have to integrate some bus in qsys and complexify the design beyond what I wanted to validate. While its knowledge is impressive, it continues to spout nonsense fairly easily.
- swalsh 3y ago
  It was better when it had the browser plugin. You could give it a link to some documentation.
vasco 3y ago
You seem like the kind of guy that when shown a printing press would go "who cares about reading anyway all the important things to know aren't going to be in books". Speeding up trivial tasks like this is game changing for many things. The amount of "trivial" things I've built with chatgpt that would've just stayed as an idea in my notes is crazy already.
- vitiral 3y ago
  It is not equivalent. The printing press took something people were doing manually and did it at higher quality and far larger scale. This is, at best, a tradeoff of a large range of factors.
  - ben_w 3y ago
    ChatGPT took something people were doing manually and does it at higher quality and far larger scale. The thing? Depending on how charitable I'm feeling, either "internet comments" or "mansplaining".
vector_spaces 3y ago
For the record, I mostly agree with you. I think asking ChatGPT to write apps or large nontrivial programs for the most part is silly and you're going to have a bad time. I hate the weird wide-eyed fanaticism with which people approach this stuff and how people will reach for ChatGPT before they reach for actual documentation. But even if its expertise mainly opens the door to trivial stuff, that alone is a huge boon for accessibility for a lot of folks. > I spent a month with an LLM and build a new programming language For the past few months I've more or less been doing this. I'm self-taught, so my CS knowledge is sketchy and has lots of holes. I never took a compilers or programming languages course. I've done lots of reading but it never fully clicked for me. Having an LLM outline the process for me at a high level, provide pseudocode or even code snippets for less clear bits, and also (try to) answer whatever questions I throw at it related to my external reading has been pretty incredible -- things suddenly clicked. Another way I use it is to ask for important introductory texts or papers when exploring a new area Yes, occasionally it spouts nonsense -- you have to evaluate its responses critically. If you ask it to do anything complicated or involving fast-moving APIs or less well known languages or frameworks, it will straight up make shit up. But we shouldn't throw the baby out with the bathwater here A snippet of a conversation from earlier this summer: Me In Pierce's types and programming languages, he describes the untyped arithmetic expression language. In one of the definitions by inference rules, he defines the language as the minimal set T such that - 0 is in T, true is in T, and false is in T - If t is in T, then succ(t) in T - pred(t) is in T, and iszero(t) is in T. That makes sense if t is numeric but how are these defined if t is Boolean? For example, true is in T, but I don't see how this definition can work if T is to also allow boolean values, like how is succ(true) well-defined? GPT In Pierce's presentation in "Types and Programming Languages", the untyped arithmetic language allows you to form terms like succ(true), even though they don't have a meaningful interpretation in "everyday arithmetic". When we say the term succ(t) is in T, we're referring to the syntactic formation of terms, not their semantic meaning. The language defines the syntax of valid terms, but it doesn't give them any inherent meaning at this stage. This is why we can syntactically form terms that don't make sense semantically. When we actually evaluate these terms (if we were to interpret them), many of them would result in "stuck" states, where we can't proceed with evaluation. For example, while we can form the term succ(true), there's no evaluation rule that tells us what this term evaluates to. The definition you cited doesn't discriminate based on the nature of t. All it says is: if t is a term in T, then succ(t), pred(t), and iszero(t) are also terms in T. Remember, this untyped language serves as a stepping stone. It illustrates what can go wrong without types. The oddities you're pointing out motivate the introduction of more refined, typed systems later in the book, where such nonsensical combinations are explicitly prohibited by the type system.
teemur 3y ago
> and the massive productivity benefits you see (30x faster!) are for trivial tasks, not difficult tasks. Okay, I'll bite. I think quite often solving difficult tasks contain lots of trivial tasks. And speeding the trivial tasks can speed the difficult task up considerably when you can outsource the trivial tasks to an LLM. I do not know how to make a catchy short video on my last side project scratching my itch (VR environment/metaverse to build 4 dimensional objects and move around them in 4d, see https://www.brainpaingames.com https://www.brainpaingames.com if interested). It was multi-year thing to get it where it is now (not pretty yet, I admit...), and it may of course just me being dumb, but yes, I consider it was/is difficult. Especially given I have never done anything in either C# or Unity. The speed-up ChatGPT gave to this project during last half a year was absolutely not trivial. I guess I must have saved years of my limited side-project time to get this far.
Swizec 3y ago
Here's a couple articles from spending a few hours with LLMs. You can write programs in markdown, it's a lot like literate programming that Knuth envisioned. I've been using this technique a lot for side projects lately --> https://swizec.com/blog/programming-in-markdown/ https://swizec.com/blog/programming-in-markdown/ ChatGPT is able to analyze JSON dumps of dependency graphs and identify communities of files that should be modules. This sounds promising and I'm figuring out how to make it more practical for real use --> https://swizec.com/blog/finding-modules-in-a-big-ball-of-mud-with-chatgpt/ https://swizec.com/blog/finding-modules-in-a-big-ball-of-mud... And while I haven't written about it yet, I've been able to get GPT-4 to analyze a blogpost and suggest phrases you should link to your past articles. Needs a little more tweaking with the prompt before prime-time because right now the result, while good, lacks some zhuzh. Whole thing was cobbled together using the markdown programming technique I mentioned in the beginning. I'm at the point now where I'm so tempted to turn markdown programming into an executable script that can save me some copypasting and perhaps where I can say "Inject other markdown here" (basically functions), or "Go look up documentation at <link>" (that's been a painpoint). So yeah there are dozens of us spending time with these things. Dozens!
Towaway69 3y ago
Oracle Oriented Programming, ask the oracle and it shall answer. The whole AI hype reminds me of the ancient Greeks going to the Oracle at Delphi to obtain answers. From the perspective of an outerspace alien, what is the difference between asking a god like figure for answers or an AI is some data center?
- anyonecancode 3y ago
  If your relationship to the subject you're asking about is equivalent to an oracle asking an inscrutable deity, then I don't know that you're going to get much value out of it. OTOH, if you know enough about the subject where the response is something you can evaluate for whether it actually makes sense, and can take and edit or integrate in a useful matter, you'll likely find that the LLM's provide a very helpful speedup that boosts productivity. At least that's been my experience.
  - Towaway69 3y ago
    Stackoverflow is the same: use the code if you understand it. But with AI I have the fear people won't take the time to understand the code - copy, paste and it's done, after all it comes from AI! How maintainable is software that was not fully understood when it was built? When it breaks in 6 months because some api changed. I guess the solution will be to get AI to fix it ... Sigh.
    - anyonecancode 3y ago
      Industrial-scale copy-pasta is definitely a concern I have around LLMs, though in practice I'm not sure it'll actually be _so_ much worse than it is now. Consider, for instance, one team that aims to have good test coverage and productive code reviews, and one that doesn't, and assume that on both teams there are some developers who carelessly copy-paste whatever the LLM spits out. I suspect that the first team will still end up doing pretty well, whereas the latter will get more terrible. So one outcome might be a greater delta between teams these two types of teams, with the takeaway being that in the new AI world, practices around code testing and reviewing are more important than ever.
imacomputertoo 3y ago
I noticed the "make a website in X minutes" video phenomenon too. I wrote a blog post about it where I examine about 10 of these videos and discuss how serious they are. You can see it here. https://joehuntblog.com/posts/build-website-with-chatgpt-no-coding-knowledge https://joehuntblog.com/posts/build-website-with-chatgpt-no-...
yafbum 3y ago
Your take is right, but a bit extreme. One thing I feel people forget is that speed is a feature. Having used a code editor with excellent AI suggestions for more than a month, I can say that I'm very frustrated when it doesn't work and all of a sudden I have to type the boring boilerplate again and make the innumerable, stupid mistakes that any human manually doing it will make. My prediction is that saving the stupid stuff for the LLM will lead to much increased productivity and ability to concentrate. Will it be 30x throughout? No. But with enough 30x moments in your day, you become maybe 10-20% more effective, and a lot more than that when prototyping (as is the case here). I think we'll look back on the time before LLM with the same puzzlement as people looking at punch cards today.
johnnymorgan 3y ago
I'm with you in that except the nothing of value has been created, which I would tweak to nothing new of value has been created. I've spent more than an hour with the tools, ChatGPT and SD being my two big buckets and it's great for productivity within some areas, mostly in fast mockup/art and creative writing, mostly fiction level. I've use it to format and transform my own work, and as a tool it's useful. Like any of these tools, early adopters get an edge for a bit and then the market adoption rolls out and we equalize. I see it mostly filling in the 80% of the easy stuff, with humans stepping in for that last 20%, which has been my approach with good success. However the hype train around it has gone past annoying and into downright scammish/spamish marketing.
PaulRobinson 3y ago
1. Many difficult tasks can be decomposed to trivial tasks. 2. Automating away well-defined trivial tasks frees up more time for figuring out the difficult tasks. 3. If you're running solo, being able to ask for alternative perspectives or to validate ideas - even if that's against a compressed archive of Reddit answers - is still valuable. 4. How is this better or worse than making progress through asking questions on X/Reddit/mailing lists/IRC/Usenet/your local library? I'll tell you: it doesn't irritate other people as much, and it's likely a lot more efficient. I get it, "I spent 20 minutes with an LLM and made a one-page HTML website!" doesn't sound impressive, until you compare it - about a year ago - "I spent two days going through awful ad-laden tutorials and made a one-page HTML website!". The big cognitive gap people might be running into - and I think you might as well - is that people think these LLM tools are there to replace people and to do things that weren't possible before. They specifically aren't. They're tools to do stuff we know how to to do, but a bit faster. Nobody is making explicit claims of magic. Nobody - other than breathless media - thinks this will replace people doing the jobs they do today. It's just going to make it a bit faster and quicker to get some stuff done. They're better screwdrivers, not replacement geniuses. May I ask, have you tried to do something difficult with it? Have you concluded it's impossible, or are you guessing that it isn't due to a lack of signal? I think it might actually be impossible, but I still think they're valuable tools for the reasons above.
- k3liutZu 3y ago
  > 4. How is this better or worse than making progress through asking questions on X/Reddit/mailing lists/IRC/Usenet/your local library? I'll tell you: it doesn't irritate other people as much, and it's likely a lot more efficient. I get it, "I spent 20 minutes with an LLM and made a one-page HTML website!" doesn't sound impressive, until you compare it - about a year ago - "I spent two days going through awful ad-laden tutorials and made a one-page HTML website!". I think this is the money-shot. LLM's (specifically ChatGPT) have helped me debug weird issues, and help get started with new technologies / libraries where searching for the issues on Google did not yield (good) results.
- test6554 3y ago
  Just wait until Chat-GPT is ad laden. Type your question, watch an 8-second ad, see half your answer, watch a 16 second ad see the rest of your answer.
test6554 3y ago
Before Chat-GPT, potatoes took 60-130 days before they were ready to harvest. So 10 seconds would be impressive.
dTal 3y ago
LLMs feel a bit like if someone invented the jet turbine in 1920. People are super impressed, they're bolting them to biplanes and model Ts to see what they can make them do, world records are being broken, there's breathless debate about whether this might mean ultra-fast travel - but until someone invents the monocoque metal monoplane, the revolution progresses slowly. LLMs are incredible. They really are. They can think fuzzily about human concepts, and interface those fuzzy concepts with cold hard computer things like programming languages and databases. But we need a whole bunch more ancillary technology before they'll really start shifting the social needle.
_hzw 3y ago
I spent 86 days with ChatGPT-4 and made a Goodreads clone (kinda) that I always want to have: https://github.com/huangziwei/luvdb https://github.com/huangziwei/luvdb I have near-zero web dev experience before this project. After about a month, I started to rely on it less and less. But I still use it to debug when I hit the wall.
dtnewman 3y ago
This is very pessimistic. The technology became widely available in November. Sure, it’s possible we will hit a wall and not get significant improvements. Or it’s possible we are where internet technology was in 1995. Already, I use ChatGPT almost daily to help me spot bugs. Doesn’t do a perfect job but it is at least 10x smarter than the rubber duck on my desk. I used it to port 7 modules python code into Node/JavaScript. I’m familiar with JS. It did a reasonably good job. And I use it all the time to prototype. Four weeks ago, I knew nothing about chrome extension development. Three weeks ago I released an extension on the chrome store. It’s not that ChatGPT wrote the extension… far from it. But it got me past the initial knowledge gap pretty quickly so that I could focus on the parts of code that I already know how to do.
- wokwokwok 3y ago
  All I’ve said is make an informed opinion instead of relying on your intuition based on the first two seconds of trying to use the tools. > It did a reasonably good job …and you probably had to fix a few things, but over all it saved you time and offered a modest productivity boost. You have clearly actually used these tools. I’m not being negative about LLMs; they’re great tools; modest, helpful, non-world ending tools. That’s been my experience, and I also use them daily. …this “2 minutes and you have an Uber”, unbounded productivity for small tasks = unbounded productivity for large tasks narrative is just people failing to understand what they’re using, because they haven’t used it enough to gain an intuition of how vastly distant where we are now is from that end goal. That’s the point. It’s not pessimism about the future; it’s more like: Pay attention to what we have right now by actually using it, because the shiny examples people give you are, bluntly cherry-picked. Don’t believe the hype. It’s good. It’s not that good.
b33j0r 3y ago
“What? You’re saying it’s gonna take me a whole weekend course!? That’s longer than the sourdough baking class!” And so you see, that’s why you and I don’t have youtube channels, and stock images of us making comically exaggerated gestures. Just so that we have a thumbnail template for: “Is Elon actually right? LLaMama autogpt 5 about to go supernova?” We knew this was a goldrush. At least it’s still pretty funny from a Carlin-esque perspective.
avereveard 3y ago
Yeah. I'm building a LLM game, and it works until it doesn't, then of course you can retry, but the user experience sucks, and costs levitate. Grammar guidance helps a bit, but you still pay for retries unless you host your own model, which is another can of worms. And that's before you get into all the issues of suddenly steering the ai after a long game into providing scoring reliably, sometimes it confuses the scenario from the game proper, or hallucinates actions the user never did. I had the prototype coded in a evening. But the rest has been a month of banging rocks to get it to work reliably start to end (well I've yet to solve scoring, but still, the rest works.)
deleted 3y ago
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walnutclosefarm 3y ago
When I was in junior high, I was given the opportunity to program on a GE Time Share BASIC system, through KSR 33 teletype, and a 100 baud dialup modem. Between January and May, I got maybe 25 minutes a day on the machine. The most impressive thing I could get it to do was play checkers at a level that a four year old could consistently beat. I orchestrated maybe, a couple thousand moving bits of logic to do that. It was all trivial. My reaction might well have been, "you can't do anything valuable with this computer programming thing." After all, I couldn't get it to do anything. My actual reaction was "WOW! Think what we can do with this." Fifty years later I was directing construction of systems of systems that could remove a tumour from a little girl's brain by taking 2D X-Ray images, reconstructing them as 3D density maps, identifying the organs in those maps, identifying the tumour in those maps, computing the best way to align proton beams in order to irradiate just the tumours, and then position the patient and beams, operate the accelerator to generate the protons and zap the tumour. It involved, literally, billions of logic statements - every one of which was trivial in its own context. Spacetime is also flat, everywhere, at the right scale. But we could orchestrate those billions of mathematical and logical trivialities into something very nontrivial because of huge productivity gains in producing them and stringing them together during my lifetime. A 30X productivity gain in producing even trivial (and the example give was not trivial) elements, is part of the path to radical innovation.
janekm 3y ago
I took that comment to refer to the scrappy original AirBnB, not the one we see today (I can't recall exactly how the first implementation was done but I do recall that it was something really simple along the lines of an excel spreadsheet and email). It's in that space of proof of concept implementations that the LLMs can really accelerate (and, potentially, as a "pair programmer" kind of role).
ben_w 3y ago
I try to adjust expectations away from the extremes with these things. Both "it's just a stochastic parrot" (and more recently "artificial stupidity, learns nothing") as a common trope in one group, and also "this is already enough to render $random_profession entirely redundant!" in the opposite. Anyone who tells me the latter: nope, it's as good as a university student in an industrial placement/internship. Anyone who tells me the former: nope, the algorithm definitely had to learn a lot to be able to produce even a half-baked mess, and even 3.5 clearly knows more than I do about almost every subject. So… > it's fundamentally limited in what it can produce, Indeed > and the massive productivity benefits you see (30x faster!) are for trivial tasks, not difficult tasks. For many people, programming at all is difficult. Even for us, switching to a completely different language and API isn't a matter of moments. Don't get me wrong, it absolutely screws up; but at 80% right-first-time (in my experience) going up to 90% just from giving it back the error messages from the compiler or interpreter, that's better than most students and much better than coming from a different domain. The same in reverse means you should never ever rely on it for anything critical, even though it can still save time if you can perform fast reliable tests on the output.
- qsort 3y ago
  I don't disagree with most of your comment, it seems like a fair assessment, all in all. Where I fundamentally differ in my value judgement is this: > 80% right-first-time Even taking that number at face value, that is really bad. Really, really bad. Not because of the number itself, but for its consequences: (a) It's downright harmful as a teacher or tutor. If I'm learning something with little or no prior knowledge, I'm in the worst possible position to question what I'm being taught, and those numbers mean it's going to routinely make mistakes. (b) If these numbers stay in the same ballpark, it's impossible to compose tasks. (80%) ^ 10 ~= 10%. Make that 95%, (95%) ^ 10 ~= 59%. Unless we see a 3 order of magnitude improvement in the error rate, interactive usage constantly supervised by an expert is the only possible way to use this technology. (c) Even if it's something I know very well, is basic enough that the model can reasonably do, and not critical enough that an error will be catastrophic, a 20% error rate means I have to keep my eyes wide open. Unless we see fundamentally new developments, it's a nice to have, but not a game changer. Intellisense level of useful, not git level of useful, let alone Internet level of useful.
javajosh 3y ago
ChatGPT is a junior programmer who knows everything and can do shallow things (like this) instantly. I'm not sure it will ever be a really good programmer, but having such a junior dev at your fingertips feels like a superpower. The ability to decompose problems and recombine the solutions remains in the human sphere.
- test6554 3y ago
  Even when its wrong it can still surface those unknown unknowns that would have cost you lots of time figuring out.
- paganel 3y ago
  > ChatGPT is a junior programmer who knows everything and can do shallow things (like this) instantly. But how do I know that the code doesn't do something malicious? As in, calling a .ru or .cn flight tracker (and thus giving it my geo position, and the geo-positions of everyone using the app I had designed via ChatGPT) instead of a genuine, non-adversarial one? You could say: "it's using opensky-network.org, not a .ru nor a .cn domain, it's right there int he code, it's genuine" which would be correct on the surface, but as someone who has had no connection to this space how do I know who opensky-network.org is without first going on their website and checking who they are? And thus increasing my development time and approaching said development time to a real-life solution. And I only gave one simple adversarial scenario, one that can be easily checked and debunked, but there are other many scenarios more convoluted than that.
  - javajosh 3y ago
    This is precisely what a senior does for a junior: review their code. Not just for it's ability to run, but what resources it uses, which resources can be trusted, and what other resources are available. That's what makes the experience novel: it's a junior that knows all the languages, all the services, all the idioms, but who has zero judgement about whether to use them.
r3trohack3r 3y ago
Personally, I’m not sure that is as far away as you’re saying. You might not crank out an AirBnB competitor, but at the low end of the market I do wonder how far Agents are going to get us. I suspect something interesting is going to happen the price point of tailored individual LLMs running full time drops below the threshold where modeling an existing business unit’s org chart as a collection of interacting LLMs becomes viable. Can you have a Director of Engineering LLM managing a pool of project manager LLMs and platform engineering LLMs and system administrator LLMs and infosec LLMs and software engineering LLMs and frontend engineer LLMs etc. Can all of those LLMs create organizational plans, give each other guidance, self correct each other, give each other feedback, and work towards a solution? For the low end of the market, can you create a software engineering organization with one well compensated Lead Engineer/CTO managing a huge collection of LLMs as a business unit? Or will the error rate compound making the ultimate output of the business unit just noise?
FredPret 3y ago
While you are right… the point of many of these videos is building audience, not documenting a technical reality.

Philpax 3y ago

They're pretty clear that that's not possible with the current state of affairs. They're speculating about a future in which that may happen, which seems pretty reasonable to me - we don't know how much gas is left in this tank, after all.

ChatGTP 3y ago
Do you know what the point would be of it spitting out an Airbnb clone ? Very little because Airbnb is a huge business which has to solve a lot of problems outside of copying someone’s code ?
- Philpax 3y ago
  Again, they're using it as an example of something that might be possible, not saying that this is what you should be doing. ...and even if they were, wouldn't it be nice to focus on solving the other problems you mention instead of writing the code?
  - ChatGTP 3y ago
    AirBNB will have 100x people working out new problems I guess…

zufallsheld 3y ago

There are posts where people tried to create a programming language with chatgpt: - https://smaller.fish/posts/l_plus https://smaller.fish/posts/l_plus - https://www.kdnuggets.com/2023/04/datalang-new-programming-language-data-scientists-chatgpt.html https://www.kdnuggets.com/2023/04/datalang-new-programming-l... - https://github.com/forrestchang/gptlang https://github.com/forrestchang/gptlang - https://6502.is-a.dev/posts/aoc-2022/ https://6502.is-a.dev/posts/aoc-2022/

ainiriand 3y ago

I agree with this statement. My personal experience has been creating MongoDB statements, the easy ones are more or less good, with only a few changes necessary. But if I need a complex one it is always incorrect, it is not able to produce it, no matter how I structure my prompt.

djmips 3y ago

Raytracer seems to me to be a trivial task. Maybe try it with CHATGPT.

dagw 3y ago
You can literally type write a simple raytracer in python into chatgpt and it will return fully working code of a simple raytracer that can handle spheres and Lambert shading. To be fair you can type the same thing into google and get a link to fully working ray tracer in python back, so maybe not the best example. edit: What would be interesting would be to see how far you can push ChatGPT by asking for more and more advanced features before it stops giving valid answers. Basically what is the most advanced raytracer currently 'stored' in ChatGPT

yellow_lead 3y ago

From my experience, you hit a wall quickly. You get an error, you tell ChatGPT, it tries to fix it, it can't. Now, you must learn everything ChatGPT wrote, in order to fix the error/bug - just as the Romans did.

swalsh 3y ago
But using it to write a first draft is FAR quicker than writing it yourself. Most of the complaints I see seem to be that you can't fully delegate your thinking/work to the AI. That you still need to be involved. But pair programming with the AI gives significant productivity boosts on it's own, and that's valuable.
test6554 3y ago
Chat GPT saves me the most time when it incorporates the things I didn’t know, I didn’t know into an answer to my problem even when its answer overall is wrong. It pulls in the unknown unknowns.
theonlybutlet 3y ago
Your compiler should show you where your errors are and pasting the error and even the documentation back into ChatGPT it can often resolve it. When it fails at this level I feel it's no different to a junior coder being asked to code above their head. You were bound to hit a ceiling at some point.sometimes breaking down your instructions into clear but detailed steps helps (using advanced terminology ideally).

hobs 3y ago

There's no reason this machine would be good at difficult tasks - they are usually somewhat bespoke, require true creativity and understanding of complex requirements, and oftentimes you'll have no example on the internet - normal programming things. However, there's many reasons it would be good at trivial tasks, and arguably most programmers spend most of their time on said trivial tasks, its just the count of them that's overwhelming; switching between multiple different languages, looking up syntax (at least for me!), and deciding on what's a sensical solution for the use case. Nobody cares about the performance characteristics and just wants it to work. So anyway, I don't think a blog post about how you build a new algorithm to come out of any of the LLMs of today, but that doesn't mean I find them underwhelming.

romanovcode 3y ago

It's not about building a product, it's about saving time by making it do small task 30x faster than yourself (just like author did). Not all tasks, but many many menial tasks. At least that's how I use it and I'm super happy with what it gives me.

taude 3y ago
Agree with this, not sure why the parent of this thread thinks you'd use it to output a full business. But helping write at the functional level is game changing: I've built entire GitHub Actions to help improve team effeciencies. Built validation rules. A bunch of Python scripts. Parsed a multi-level JSON response that would have taken a few hours, etc... All in minutes. Imagine knocking off like 15 of these tasks before noon.
- test6554 3y ago
  I am worried that Chat-GPT productivity levels will become the new norm and when it doesn’t give you what you need you will be underperforming at work.
dmje 3y ago
That's exactly right. Any product is essentially made up of a gazillion small tasks, whether setting up a framework or writing a database query. If just some of those small tasks can be made quicker / easier using these tools, then why not? What I find most useful in this space is twofold: 1. Specificity: so yeh, I can look at SO or wherever and see responses to a semi-specific query, say about querying a database. And yes, I could post a question and maybe after a few hours if I'm lucky get a response. But with ChatGPT I can throw in the actual table and column names and get a specific response back. Most often it needs honing, sure, but it's most often a great starting point. 2. Being able to back-query. So the fact that history is maintained means I can say "actually, add in a column called X and can you make sure that the query only returns unique IDs" and - again - I'll get an immediate response without having to go all the way back through the questioning journey again. As with any tool, the extreme responses on either end seem obviously flawed. On the one hand: no, it's not going to replace all developer jobs. On the other, to just completely ignore it, not try it, always assume that "the documentation" is better - this seems blinkered and foolish too. Somewhere in the middle is a sweet spot where human brains collide with these tools and we manage to tease out code that is more quickly researched and saves time but also well considered and treated with a degree of healthy scepticism before deployment.

bjornlouser 3y ago

If there was a supplement you could feed your cat that allowed it to speak a few words like a parrot there would also be endless chatter about the use of that product. It’s amazing. ‘Meh. Let me know when your cat can recite T.S. Eliot’

lolinder 3y ago
If said supplement caused people to say things like this about the cat, it would definitely warrant a skeptical take like OP's: > Imagine envisioning Airbnb, and having its whole frontend and backend done within 1 minute. We’re not there yet, but we could be there soon. The difference in scale between toy plane spotter and an Airbnb front end and back end is simply enormous, with a combinatorial explosion of possible states and failure modes. Our existing tech will not scale to that level by just adding more parameters and GPUs. We may or may not have a major paradigm shift in the future that enables it, but this talking cat ain't producing Airbnb in 1 minute.

Foobar8568 3y ago

I have tried to do something I thought really simple this weekend: having a C program on a SoC running Linux with a FPGA component e.g. to pass two integers to the FPGA from HPS with mmap, multiply them, get the result within one clock cycle and without doing much (integrating ) on the bus/bridge. It seems I will have to integrate some bus in qsys and complexify the design beyond what I wanted to validate. While its knowledge is impressive, it continues to spout nonsense fairly easily.

swalsh 3y ago
It was better when it had the browser plugin. You could give it a link to some documentation.

vasco 3y ago

You seem like the kind of guy that when shown a printing press would go "who cares about reading anyway all the important things to know aren't going to be in books". Speeding up trivial tasks like this is game changing for many things. The amount of "trivial" things I've built with chatgpt that would've just stayed as an idea in my notes is crazy already.

vitiral 3y ago
It is not equivalent. The printing press took something people were doing manually and did it at higher quality and far larger scale. This is, at best, a tradeoff of a large range of factors.
- ben_w 3y ago
  ChatGPT took something people were doing manually and does it at higher quality and far larger scale. The thing? Depending on how charitable I'm feeling, either "internet comments" or "mansplaining".

vector_spaces 3y ago

For the record, I mostly agree with you. I think asking ChatGPT to write apps or large nontrivial programs for the most part is silly and you're going to have a bad time. I hate the weird wide-eyed fanaticism with which people approach this stuff and how people will reach for ChatGPT before they reach for actual documentation. But even if its expertise mainly opens the door to trivial stuff, that alone is a huge boon for accessibility for a lot of folks. > I spent a month with an LLM and build a new programming language For the past few months I've more or less been doing this. I'm self-taught, so my CS knowledge is sketchy and has lots of holes. I never took a compilers or programming languages course. I've done lots of reading but it never fully clicked for me. Having an LLM outline the process for me at a high level, provide pseudocode or even code snippets for less clear bits, and also (try to) answer whatever questions I throw at it related to my external reading has been pretty incredible -- things suddenly clicked. Another way I use it is to ask for important introductory texts or papers when exploring a new area Yes, occasionally it spouts nonsense -- you have to evaluate its responses critically. If you ask it to do anything complicated or involving fast-moving APIs or less well known languages or frameworks, it will straight up make shit up. But we shouldn't throw the baby out with the bathwater here A snippet of a conversation from earlier this summer: Me In Pierce's types and programming languages, he describes the untyped arithmetic expression language. In one of the definitions by inference rules, he defines the language as the minimal set T such that - 0 is in T, true is in T, and false is in T - If t is in T, then succ(t) in T - pred(t) is in T, and iszero(t) is in T. That makes sense if t is numeric but how are these defined if t is Boolean? For example, true is in T, but I don't see how this definition can work if T is to also allow boolean values, like how is succ(true) well-defined? GPT In Pierce's presentation in "Types and Programming Languages", the untyped arithmetic language allows you to form terms like succ(true), even though they don't have a meaningful interpretation in "everyday arithmetic". When we say the term succ(t) is in T, we're referring to the syntactic formation of terms, not their semantic meaning. The language defines the syntax of valid terms, but it doesn't give them any inherent meaning at this stage. This is why we can syntactically form terms that don't make sense semantically. When we actually evaluate these terms (if we were to interpret them), many of them would result in "stuck" states, where we can't proceed with evaluation. For example, while we can form the term succ(true), there's no evaluation rule that tells us what this term evaluates to. The definition you cited doesn't discriminate based on the nature of t. All it says is: if t is a term in T, then succ(t), pred(t), and iszero(t) are also terms in T. Remember, this untyped language serves as a stepping stone. It illustrates what can go wrong without types. The oddities you're pointing out motivate the introduction of more refined, typed systems later in the book, where such nonsensical combinations are explicitly prohibited by the type system.

teemur 3y ago

> and the massive productivity benefits you see (30x faster!) are for trivial tasks, not difficult tasks. Okay, I'll bite. I think quite often solving difficult tasks contain lots of trivial tasks. And speeding the trivial tasks can speed the difficult task up considerably when you can outsource the trivial tasks to an LLM. I do not know how to make a catchy short video on my last side project scratching my itch (VR environment/metaverse to build 4 dimensional objects and move around them in 4d, see https://www.brainpaingames.com https://www.brainpaingames.com if interested). It was multi-year thing to get it where it is now (not pretty yet, I admit...), and it may of course just me being dumb, but yes, I consider it was/is difficult. Especially given I have never done anything in either C# or Unity. The speed-up ChatGPT gave to this project during last half a year was absolutely not trivial. I guess I must have saved years of my limited side-project time to get this far.

Swizec 3y ago

Here's a couple articles from spending a few hours with LLMs. You can write programs in markdown, it's a lot like literate programming that Knuth envisioned. I've been using this technique a lot for side projects lately --> https://swizec.com/blog/programming-in-markdown/ https://swizec.com/blog/programming-in-markdown/ ChatGPT is able to analyze JSON dumps of dependency graphs and identify communities of files that should be modules. This sounds promising and I'm figuring out how to make it more practical for real use --> https://swizec.com/blog/finding-modules-in-a-big-ball-of-mud-with-chatgpt/ https://swizec.com/blog/finding-modules-in-a-big-ball-of-mud... And while I haven't written about it yet, I've been able to get GPT-4 to analyze a blogpost and suggest phrases you should link to your past articles. Needs a little more tweaking with the prompt before prime-time because right now the result, while good, lacks some zhuzh. Whole thing was cobbled together using the markdown programming technique I mentioned in the beginning. I'm at the point now where I'm so tempted to turn markdown programming into an executable script that can save me some copypasting and perhaps where I can say "Inject other markdown here" (basically functions), or "Go look up documentation at <link>" (that's been a painpoint). So yeah there are dozens of us spending time with these things. Dozens!

Towaway69 3y ago

Oracle Oriented Programming, ask the oracle and it shall answer. The whole AI hype reminds me of the ancient Greeks going to the Oracle at Delphi to obtain answers. From the perspective of an outerspace alien, what is the difference between asking a god like figure for answers or an AI is some data center?

anyonecancode 3y ago
If your relationship to the subject you're asking about is equivalent to an oracle asking an inscrutable deity, then I don't know that you're going to get much value out of it. OTOH, if you know enough about the subject where the response is something you can evaluate for whether it actually makes sense, and can take and edit or integrate in a useful matter, you'll likely find that the LLM's provide a very helpful speedup that boosts productivity. At least that's been my experience.
- Towaway69 3y ago
  Stackoverflow is the same: use the code if you understand it. But with AI I have the fear people won't take the time to understand the code - copy, paste and it's done, after all it comes from AI! How maintainable is software that was not fully understood when it was built? When it breaks in 6 months because some api changed. I guess the solution will be to get AI to fix it ... Sigh.
  - anyonecancode 3y ago
    Industrial-scale copy-pasta is definitely a concern I have around LLMs, though in practice I'm not sure it'll actually be _so_ much worse than it is now. Consider, for instance, one team that aims to have good test coverage and productive code reviews, and one that doesn't, and assume that on both teams there are some developers who carelessly copy-paste whatever the LLM spits out. I suspect that the first team will still end up doing pretty well, whereas the latter will get more terrible. So one outcome might be a greater delta between teams these two types of teams, with the takeaway being that in the new AI world, practices around code testing and reviewing are more important than ever.

imacomputertoo 3y ago

I noticed the "make a website in X minutes" video phenomenon too. I wrote a blog post about it where I examine about 10 of these videos and discuss how serious they are. You can see it here. https://joehuntblog.com/posts/build-website-with-chatgpt-no-coding-knowledge https://joehuntblog.com/posts/build-website-with-chatgpt-no-...

yafbum 3y ago

Your take is right, but a bit extreme. One thing I feel people forget is that speed is a feature. Having used a code editor with excellent AI suggestions for more than a month, I can say that I'm very frustrated when it doesn't work and all of a sudden I have to type the boring boilerplate again and make the innumerable, stupid mistakes that any human manually doing it will make. My prediction is that saving the stupid stuff for the LLM will lead to much increased productivity and ability to concentrate. Will it be 30x throughout? No. But with enough 30x moments in your day, you become maybe 10-20% more effective, and a lot more than that when prototyping (as is the case here). I think we'll look back on the time before LLM with the same puzzlement as people looking at punch cards today.

johnnymorgan 3y ago

I'm with you in that except the nothing of value has been created, which I would tweak to nothing new of value has been created. I've spent more than an hour with the tools, ChatGPT and SD being my two big buckets and it's great for productivity within some areas, mostly in fast mockup/art and creative writing, mostly fiction level. I've use it to format and transform my own work, and as a tool it's useful. Like any of these tools, early adopters get an edge for a bit and then the market adoption rolls out and we equalize. I see it mostly filling in the 80% of the easy stuff, with humans stepping in for that last 20%, which has been my approach with good success. However the hype train around it has gone past annoying and into downright scammish/spamish marketing.

PaulRobinson 3y ago

1. Many difficult tasks can be decomposed to trivial tasks. 2. Automating away well-defined trivial tasks frees up more time for figuring out the difficult tasks. 3. If you're running solo, being able to ask for alternative perspectives or to validate ideas - even if that's against a compressed archive of Reddit answers - is still valuable. 4. How is this better or worse than making progress through asking questions on X/Reddit/mailing lists/IRC/Usenet/your local library? I'll tell you: it doesn't irritate other people as much, and it's likely a lot more efficient. I get it, "I spent 20 minutes with an LLM and made a one-page HTML website!" doesn't sound impressive, until you compare it - about a year ago - "I spent two days going through awful ad-laden tutorials and made a one-page HTML website!". The big cognitive gap people might be running into - and I think you might as well - is that people think these LLM tools are there to replace people and to do things that weren't possible before. They specifically aren't. They're tools to do stuff we know how to to do, but a bit faster. Nobody is making explicit claims of magic. Nobody - other than breathless media - thinks this will replace people doing the jobs they do today. It's just going to make it a bit faster and quicker to get some stuff done. They're better screwdrivers, not replacement geniuses. May I ask, have you tried to do something difficult with it? Have you concluded it's impossible, or are you guessing that it isn't due to a lack of signal? I think it might actually be impossible, but I still think they're valuable tools for the reasons above.

k3liutZu 3y ago
> 4. How is this better or worse than making progress through asking questions on X/Reddit/mailing lists/IRC/Usenet/your local library? I'll tell you: it doesn't irritate other people as much, and it's likely a lot more efficient. I get it, "I spent 20 minutes with an LLM and made a one-page HTML website!" doesn't sound impressive, until you compare it - about a year ago - "I spent two days going through awful ad-laden tutorials and made a one-page HTML website!". I think this is the money-shot. LLM's (specifically ChatGPT) have helped me debug weird issues, and help get started with new technologies / libraries where searching for the issues on Google did not yield (good) results.
test6554 3y ago
Just wait until Chat-GPT is ad laden. Type your question, watch an 8-second ad, see half your answer, watch a 16 second ad see the rest of your answer.

test6554 3y ago

Before Chat-GPT, potatoes took 60-130 days before they were ready to harvest. So 10 seconds would be impressive.

dTal 3y ago

LLMs feel a bit like if someone invented the jet turbine in 1920. People are super impressed, they're bolting them to biplanes and model Ts to see what they can make them do, world records are being broken, there's breathless debate about whether this might mean ultra-fast travel - but until someone invents the monocoque metal monoplane, the revolution progresses slowly. LLMs are incredible. They really are. They can think fuzzily about human concepts, and interface those fuzzy concepts with cold hard computer things like programming languages and databases. But we need a whole bunch more ancillary technology before they'll really start shifting the social needle.

_hzw 3y ago

I spent 86 days with ChatGPT-4 and made a Goodreads clone (kinda) that I always want to have: https://github.com/huangziwei/luvdb https://github.com/huangziwei/luvdb I have near-zero web dev experience before this project. After about a month, I started to rely on it less and less. But I still use it to debug when I hit the wall.

dtnewman 3y ago

This is very pessimistic. The technology became widely available in November. Sure, it’s possible we will hit a wall and not get significant improvements. Or it’s possible we are where internet technology was in 1995. Already, I use ChatGPT almost daily to help me spot bugs. Doesn’t do a perfect job but it is at least 10x smarter than the rubber duck on my desk. I used it to port 7 modules python code into Node/JavaScript. I’m familiar with JS. It did a reasonably good job. And I use it all the time to prototype. Four weeks ago, I knew nothing about chrome extension development. Three weeks ago I released an extension on the chrome store. It’s not that ChatGPT wrote the extension… far from it. But it got me past the initial knowledge gap pretty quickly so that I could focus on the parts of code that I already know how to do.

wokwokwok 3y ago
All I’ve said is make an informed opinion instead of relying on your intuition based on the first two seconds of trying to use the tools. > It did a reasonably good job …and you probably had to fix a few things, but over all it saved you time and offered a modest productivity boost. You have clearly actually used these tools. I’m not being negative about LLMs; they’re great tools; modest, helpful, non-world ending tools. That’s been my experience, and I also use them daily. …this “2 minutes and you have an Uber”, unbounded productivity for small tasks = unbounded productivity for large tasks narrative is just people failing to understand what they’re using, because they haven’t used it enough to gain an intuition of how vastly distant where we are now is from that end goal. That’s the point. It’s not pessimism about the future; it’s more like: Pay attention to what we have right now by actually using it, because the shiny examples people give you are, bluntly cherry-picked. Don’t believe the hype. It’s good. It’s not that good.

b33j0r 3y ago

“What? You’re saying it’s gonna take me a whole weekend course!? That’s longer than the sourdough baking class!” And so you see, that’s why you and I don’t have youtube channels, and stock images of us making comically exaggerated gestures. Just so that we have a thumbnail template for: “Is Elon actually right? LLaMama autogpt 5 about to go supernova?” We knew this was a goldrush. At least it’s still pretty funny from a Carlin-esque perspective.

avereveard 3y ago

Yeah. I'm building a LLM game, and it works until it doesn't, then of course you can retry, but the user experience sucks, and costs levitate. Grammar guidance helps a bit, but you still pay for retries unless you host your own model, which is another can of worms. And that's before you get into all the issues of suddenly steering the ai after a long game into providing scoring reliably, sometimes it confuses the scenario from the game proper, or hallucinates actions the user never did. I had the prototype coded in a evening. But the rest has been a month of banging rocks to get it to work reliably start to end (well I've yet to solve scoring, but still, the rest works.)

deleted 3y ago

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walnutclosefarm 3y ago

When I was in junior high, I was given the opportunity to program on a GE Time Share BASIC system, through KSR 33 teletype, and a 100 baud dialup modem. Between January and May, I got maybe 25 minutes a day on the machine. The most impressive thing I could get it to do was play checkers at a level that a four year old could consistently beat. I orchestrated maybe, a couple thousand moving bits of logic to do that. It was all trivial. My reaction might well have been, "you can't do anything valuable with this computer programming thing." After all, I couldn't get it to do anything. My actual reaction was "WOW! Think what we can do with this." Fifty years later I was directing construction of systems of systems that could remove a tumour from a little girl's brain by taking 2D X-Ray images, reconstructing them as 3D density maps, identifying the organs in those maps, identifying the tumour in those maps, computing the best way to align proton beams in order to irradiate just the tumours, and then position the patient and beams, operate the accelerator to generate the protons and zap the tumour. It involved, literally, billions of logic statements - every one of which was trivial in its own context. Spacetime is also flat, everywhere, at the right scale. But we could orchestrate those billions of mathematical and logical trivialities into something very nontrivial because of huge productivity gains in producing them and stringing them together during my lifetime. A 30X productivity gain in producing even trivial (and the example give was not trivial) elements, is part of the path to radical innovation.

janekm 3y ago

I took that comment to refer to the scrappy original AirBnB, not the one we see today (I can't recall exactly how the first implementation was done but I do recall that it was something really simple along the lines of an excel spreadsheet and email). It's in that space of proof of concept implementations that the LLMs can really accelerate (and, potentially, as a "pair programmer" kind of role).

ben_w 3y ago

I try to adjust expectations away from the extremes with these things. Both "it's just a stochastic parrot" (and more recently "artificial stupidity, learns nothing") as a common trope in one group, and also "this is already enough to render $random_profession entirely redundant!" in the opposite. Anyone who tells me the latter: nope, it's as good as a university student in an industrial placement/internship. Anyone who tells me the former: nope, the algorithm definitely had to learn a lot to be able to produce even a half-baked mess, and even 3.5 clearly knows more than I do about almost every subject. So… > it's fundamentally limited in what it can produce, Indeed > and the massive productivity benefits you see (30x faster!) are for trivial tasks, not difficult tasks. For many people, programming at all is difficult. Even for us, switching to a completely different language and API isn't a matter of moments. Don't get me wrong, it absolutely screws up; but at 80% right-first-time (in my experience) going up to 90% just from giving it back the error messages from the compiler or interpreter, that's better than most students and much better than coming from a different domain. The same in reverse means you should never ever rely on it for anything critical, even though it can still save time if you can perform fast reliable tests on the output.

qsort 3y ago
I don't disagree with most of your comment, it seems like a fair assessment, all in all. Where I fundamentally differ in my value judgement is this: > 80% right-first-time Even taking that number at face value, that is really bad. Really, really bad. Not because of the number itself, but for its consequences: (a) It's downright harmful as a teacher or tutor. If I'm learning something with little or no prior knowledge, I'm in the worst possible position to question what I'm being taught, and those numbers mean it's going to routinely make mistakes. (b) If these numbers stay in the same ballpark, it's impossible to compose tasks. (80%) ^ 10 ~= 10%. Make that 95%, (95%) ^ 10 ~= 59%. Unless we see a 3 order of magnitude improvement in the error rate, interactive usage constantly supervised by an expert is the only possible way to use this technology. (c) Even if it's something I know very well, is basic enough that the model can reasonably do, and not critical enough that an error will be catastrophic, a 20% error rate means I have to keep my eyes wide open. Unless we see fundamentally new developments, it's a nice to have, but not a game changer. Intellisense level of useful, not git level of useful, let alone Internet level of useful.

javajosh 3y ago

ChatGPT is a junior programmer who knows everything and can do shallow things (like this) instantly. I'm not sure it will ever be a really good programmer, but having such a junior dev at your fingertips feels like a superpower. The ability to decompose problems and recombine the solutions remains in the human sphere.

test6554 3y ago
Even when its wrong it can still surface those unknown unknowns that would have cost you lots of time figuring out.
paganel 3y ago
> ChatGPT is a junior programmer who knows everything and can do shallow things (like this) instantly. But how do I know that the code doesn't do something malicious? As in, calling a .ru or .cn flight tracker (and thus giving it my geo position, and the geo-positions of everyone using the app I had designed via ChatGPT) instead of a genuine, non-adversarial one? You could say: "it's using opensky-network.org, not a .ru nor a .cn domain, it's right there int he code, it's genuine" which would be correct on the surface, but as someone who has had no connection to this space how do I know who opensky-network.org is without first going on their website and checking who they are? And thus increasing my development time and approaching said development time to a real-life solution. And I only gave one simple adversarial scenario, one that can be easily checked and debunked, but there are other many scenarios more convoluted than that.
- javajosh 3y ago
  This is precisely what a senior does for a junior: review their code. Not just for it's ability to run, but what resources it uses, which resources can be trusted, and what other resources are available. That's what makes the experience novel: it's a junior that knows all the languages, all the services, all the idioms, but who has zero judgement about whether to use them.

r3trohack3r 3y ago

Personally, I’m not sure that is as far away as you’re saying. You might not crank out an AirBnB competitor, but at the low end of the market I do wonder how far Agents are going to get us. I suspect something interesting is going to happen the price point of tailored individual LLMs running full time drops below the threshold where modeling an existing business unit’s org chart as a collection of interacting LLMs becomes viable. Can you have a Director of Engineering LLM managing a pool of project manager LLMs and platform engineering LLMs and system administrator LLMs and infosec LLMs and software engineering LLMs and frontend engineer LLMs etc. Can all of those LLMs create organizational plans, give each other guidance, self correct each other, give each other feedback, and work towards a solution? For the low end of the market, can you create a software engineering organization with one well compensated Lead Engineer/CTO managing a huge collection of LLMs as a business unit? Or will the error rate compound making the ultimate output of the business unit just noise?

FredPret 3y ago

While you are right… the point of many of these videos is building audience, not documenting a technical reality.