[allisdust]

I pity comments like this. Instead of trying to up skill to use the newest programming tools, you are setting yourself for failure.

Sonnet 3.5 digests close to 400k bytes of text and produces coherent code that works on the first try. If someone says its not working and they are a professional programmer, get ready to feel like you are hit by ton of bricks next year. The productivity boost is only going to accelerate and those who can't adopt will be left behind.

[threeseed]

a) There is no up-skilling needed to use LLMs. They are very basic to use.

b) Many of us have used them for a while now and can speak from experience that they aren't providing a meaningful productivity boost. Simply because they don't work well enough to provide a positive ROI. And no amount of prompting expertise can change that.

c) For me it is junior developers who love these tools because they think it's a shortcut to becoming experienced. But it's akin to cheating. You're not actually learning why and how things are supposed to work. And that will hurt you in professional environments where you often need to explain why you wrote that code and introduced that bug.

[allisdust]

Your (1) is not matching with (2) because there are anecdotes contrary to yours (the tweet in question and my personal one). I have close to 2 decades of experience in a variety of languages and frameworks and never felt this powerful and liberated with any of the previous tools.In the past year I have developed 2 complex products nearing market launch with just me on a part time basis.

My professional colleagues continue to feel the exact same way you feel and despite my best efforts refuse to even bother using them for anything. Using LLMs might appear to be simple and the prompt length might be similar between an experienced user vs naive one but the way intent is conveyed varies with skill level.

My only complaints about LLMs are: 1) Context is still a limiting factor (so only medium sized projects) 2) I have to still copy paste the code (no IDE truly helps here)

What has improved in the past 6 months: Sonnet happened and I no longer have to worry about the code being wrong or that it contains obvious mistakes. In many cases where I thought it got it wrong turned out to be a clever way to minimize the number of changes needed/clever ways to do more with less. We are approaching the point where humans no longer are intelligent enough to appreciate the LLMs.

[threeseed]

I look forward to the day that I can be "intelligent enough" to truly appreciate LLMs. Maybe I need to buy a course from someone on X.

And not from months of experience using Claude where it over and over again will give me algorithms that are wrong, assure me every time it is right and do so using versions of libraries that are typically a year or more old.

[simonw]

"There is no up-skilling needed to use LLMs. They are very basic to use."

Hard disagree on that. Using LLMs effectively is deceptively deep. Sure, anyone can throw a prompt at a chatbot - but I've been using them on an almost daily basis for over two years at this point and I still feel like I'm finding out new ways to improve my prompting several times a week.

I talked more about how hard they are to use here: https://simonwillison.net/2024/Jun/27/ai-worlds-fair/#slide....

"Many of us have used them for a while now and can speak from experience that they aren't providing a meaningful productivity boost."

I'm getting a meaningful productivity boost, which gets more meaningful the more time I spend learning how best to apply them.

[Kiro]

We are many professionals that share Karpathy's opinion on this and know for a fact that it provides a very meaningful productivity boost. It may not be for everyone but I can absolutely not imagine going back, and can confidently say it's not just junior developers that love these tools.

[foobarqux]

Why isn't there a single screencast (un-edited, un-cherry-picked) of anyone showing off their 10x productivity boost in a full "typical" coding session?

[Smaug123]

Someone recently asked this on Twitter; Simon Willison responded with https://simonwillison.net/2024/Jun/21/search-based-rag/ which I have not yet watched but which he claimed was a good example of this genre.

[simonw]

Having rewatched that myself the other day it's not actually as good an example as I thought - I use Claude 3.5 Sonnet a bit in it (which was released the morning we recorded that video) and then get a bit of benefit out of Val Town's integration with Codeium, which is similar to VS Code Copilot - but not as much of the code in it was LLM-generated as I remembered.

A better (written) description of how I use these tools is this one: https://simonwillison.net/2024/Mar/30/ocr-pdfs-images/ - and this whole series of posts: https://simonwillison.net/tags/ai-assisted-programming/

[foobarqux]

I would point out that the OCR example (and from what I see the series of posts you linked to) aren't "live" coding screen shares and don't convey the nitty gritty of how these things are used and how well they work.

[simonw]

Right, but they’re the best I have - I don’t do much live coding aside from that Val Town one which doesn’t use LLMs very much.

[Smaug123]

I'd love to see this operationalised as concrete predictions, as one might find on a prediction market! Do you have any specific predictions about programming next year?

I ask (for example) because I suspect shitting out CRUD apps is cheaper via LLM than via human now, and I guess probably most programming work is of that nature, but there are programmers out there whose job is not shitting out CRUD apps, and it's not clear from your statement whether you intend the sentiment to cover those programmers too.

[allisdust]

The answer lies in your question. I foresee consolidation in programming languages and frameworks with compact and well known ones edging out esoteric and niche ones. In a couple of years of time, I predict that there will be new languages specifically targeting LLMs that aren't as human readable but extremely compact similar to byte code (compactness is preferred due to context size limitation not fully going away).

So in a nutshell I feel like most things will be LLM generated with human focus mostly around systems boundary stitching with focus on extreme cases like quant and medical domains where human oversight might be needed.

[tom_]

Let's cross that bridge when we come to it, shall we. Meanwhile, you should be glad we are refusing to use it. If it works as well as you claim, this situation is to your advantage.

[jimsimmons]

I've been waiting since 2021 when I saw demos of GitHub copilot

[specialist]

I'd like to see that. Link(s)?

I've been on the sidelines, waiting for the dust to settle. Kind of like waiting a few months before applying the latest major OS updates.

[neta1337]

Ah yes, just like stocks can only go up. No one will feel like hit by a ton of bricks.