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The Bitter Prediction

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Re: The Bitter Prediction

#41

Post by sigmoid10 »

cardanome wrote: post #19

A relative known youtuber called the primeagen has recently done a challenge sponsored by Cursor themselves where he and some friends would "vibe code" a game in a week. The results were pretty underwhelming. They would have been much faster not using generative Ai. Compared what you see from game jams where sometimes solo devs create whole games in just a few days it was pretty trash. It also tracks with my own expe…

I feel most people drastically underestimate game dev. The programming aspect is only one tiny part of it and even there it goes so wide (from in-game logic to rendering to physics) that it's near impossible for people who are not really deep into it to have a clue what is happening. And even if you manage to vibe-code your way through it, your game will still suck unless you have good assets - which means textures, models, animations, sounds, FX... you get it. Developing a high quality game is sort of the ultimate test for AI and if it achieves it on a scale beyond game jams we might as well accept that we have reached artificial superintelligence.

Re: The Bitter Prediction

#42

Post by coolThingsFirst »

Still think amazement of ai tools as harsh as it sounds signals incompetence of the user. They are useful don’t get me wrong but just today Claude wrote code that literally wouldnt run.

Thought it’s ok to use new for object literal in JS.

Re: The Bitter Prediction

#43

Post by skerit »

hsuduebc2 wrote: post #34
M4v3R wrote: post #14

To me it’s the exact opposite. I was writing code for the past 20+ years and I recently realized it’s not the act of writing code I love, but the act of creating something from nothing. Over the past few months I wrote two non-trivial utility apps that otherwise I would most probably not write because I didn’t have enough time to do that, but Cursor + Claude gave me the 5x productivity boost that enabled me to do so,…

Same here. I do not usually enjoy programming as an craft but the act of building something is what is loveable experience.

The challenge I often face is having an entire _mental model_ of what I want to build already crystallized in my head, but then the realization that it will take hours of coding to actually convert that to code... That can be incredibly demotivating.

Re: The Bitter Prediction

#44

Post by zwnow »

falcor84 wrote: post #23
zwnow wrote: post #16

Earlier quoted context omitted.

Incredible take considering using AI robs new learners of off real learning. There is a reason lots of experienced devs are dropping it from their editors. Using AI will not make you a better dev, it simply accelerates you building a failing product faster, because ultimately you wont understand your own product. Most devs that use AI blindly trust it instead of questioning what it produces.

> Most devs that use AI blindly trust it instead of questioning what it produces. Without the punctuation, I first read it tautologically as "Most devs that use AI blindly, trust it instead of questioning what it produces". But even assuming you meant "Most devs that use AI, blindly trust it instead of questioning what it produces", there's still a negative feedback loop. We're still at the early experimentation phas…

A dystopia in which ill look for a new career. Using AI to generate code sucks the joy out of the job.

Re: The Bitter Prediction

#45

Post by MattJ100 »

mnky9800n wrote: post #33
mjburgess wrote: post #11

All articles of this class, whether positive or negative, begin "I was working on a hobby project" or some variation thereof. The purpose of hobbies is to be a hobby, archetypical tech projects are about self-mastery. You cannot improve your mastery with a "tool" that robs you of most of the minor and major creative and technical decisions of the task. Building IKEA furniture will not make you a better carpenter. Why…

I think it also misses the way you can automate non-trivial tasks. For example, I am working on a project where there is tens of thousands of different data sets each with their own meta data and structure but the underlying data is mostly the same. But because the meta data and structure are all different, it’s really impossible to combine all this data into one big data set without a team of engineers going through…

I agree, but only for situations where the probabilistic nature is acceptable. It would be the same if you had a large team of humans doing the same work. Inevitably misclassifications would occur on an ongoing basis.

Compare this to the situation where you have a team develop schemas for your datasets which can be tested and verified, and fixed in the event of errors. You can't really "fix" an LLM or human agent in that way.

So I feel like traditionally computing excelled at many tasks that humans couldn't do - computers are crazy fast and don't make mistakes, as a rule. LLMs remove this speed and accuracy, becoming something more like scalable humans (their "intelligence" is debateable, but possibly a moving target - I've yet to see an LLM that I would trust more than a very junior developer). LLMs (and ML generally) will always have higher error margins, it's how they can do what they do.

Re: The Bitter Prediction

#47

Post by walleeee »

jwblackwell wrote: post #25

The author is essentially arguing that fewer people will be able to build software in the future. That's the opposite of what's happened over the past year or two. Now many more non-technical people can (and are) building software.

> The author is essentially arguing that fewer people will be able to build software in the future.

Setting aside the fact that the author nowhere says this, it may in fact be plausible.

> That's the opposite of what's happened over the past year or two. Now many more non-technical people can (and are) building software.

Meanwhile half[0] the students supposed to be learning to build software in university will fail to learn something important because they asked Claude instead of thinking about it. (Or all the students using llms will fail to learn something half the time, etc.)

[0]: https://www.anthropic.com/news/anthropic-education-report-ho...

> That said, nearly half (~47%) of student-AI conversations were Direct—that is, seeking answers or content with minimal engagement.

Re: The Bitter Prediction

#48

Post by IshKebab »

joelthelion wrote: post #35
IshKebab wrote: post #26

> Not only that, the generated code was high-quality, efficient, and conformed to my coding guidelines. It routinely "checked its work" by running unit tests to eliminate hallucinations and bugs. This seems completely out of whack with my experience of AI coding. I'm definitely in the "it's extremely useful" camp but there's no way I would describe its code as high quality and efficient. It can do simple tasks but it…

Which model and tool are you using? There's a whole spectrum of AI-assisted coding.

ChatGPT, Claude (both through the website), and Github Copilot (paid if it makes any difference).

Re: The Bitter Prediction

#49

Post by mnky9800n »

MattJ100 wrote: post #45
mnky9800n wrote: post #33

Earlier quoted context omitted.

I think it also misses the way you can automate non-trivial tasks. For example, I am working on a project where there is tens of thousands of different data sets each with their own meta data and structure but the underlying data is mostly the same. But because the meta data and structure are all different, it’s really impossible to combine all this data into one big data set without a team of engineers going through…

I agree, but only for situations where the probabilistic nature is acceptable. It would be the same if you had a large team of humans doing the same work. Inevitably misclassifications would occur on an ongoing basis. Compare this to the situation where you have a team develop schemas for your datasets which can be tested and verified, and fixed in the event of errors. You can't really "fix" an LLM or human agent in…

Yes but i see it as multiple steps. Like perhaps the llm solution has some probabilistic issues that only get you 80% of the way there. But that probably already has given you some ideas how to better solve the problem. And this case the problem is somewhat intractable because of the size and complexity of the way the data is stored. So like in my example the first step is LLMs but the second step would be to use what they do as structure for building a deterministic pipeline. This is because the problem isn’t that there are ten thousand different meta data, but that the structure of those metadata are diffuse. The llm solution will first help identify the main points of what needs to be conformed to the monolithic schema. Then I will build more production ready and deterministic pipelines. At least that is the plan. I’ll write a substack about it eventually if this plan works haha.

Re: The Bitter Prediction

#50

Post by jwblackwell »

wobfan wrote: post #29
jwblackwell wrote: post #25

The author is essentially arguing that fewer people will be able to build software in the future. That's the opposite of what's happened over the past year or two. Now many more non-technical people can (and are) building software.

No, he never states this and is not true. The author tell his experience regarding his joy programming things and figuring stuff out. In the end he says that AI made him lose this joy, and he compares it to cheating in a game. He does not say one word about societal impact and or the amount of engineers in the future, it's what you interpreted yourself.

“ In some countries, more than 90% of the population lives on less than $5 per day. If agentic AI code generation becomes the most effective way to write high-quality code, this will create a massive barrier to entry”
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