A coder considers the waning days of the craft

3.5, or GPT-4? I'm told the latter is worlds better, that they aren't even in the same ballpark.

Not to be unkind but simply factual: the author is clearly not a great developer. e.g.:

"At one point, we wanted a command that would print a hundred random lines from a dictionary file. I thought about the problem for a few minutes, and, when thinking failed, tried Googling." Really?

"But he had no idea what a pain it is to make an iPhone app. I’d tried a few times and never got beyond something that half worked. I found Apple’s programming environment forbidding." Perhaps. But compared to what? Yeah, it's not using VI to write Javascript or whatever he considers a programming environment.

Power tools didn't stop people from making things. Autocad didn't put architects out of business. Finite element modelling didn't destroy civil engineering. Quickbooks hasn't destroyed the need for accountants. Word processors didn't destroy writing. Google translate didn't remove the need for translators.

LLMs will be a power tool for good developers. I think many underestimate what it'll take to fully replace good or excellent software developers and what they do in totality.

Why are people so unaware of what is happening? The leap in quality from DALL-E 2 to DALL-E 3 was due to improvements in the prompts used for training. With countless people using ChatGPT, it will reach a point where OpenAI will have enough data to train on. There are ideas not yet explored that will be connected to the system. At this point, the pace of development is exponential. Unless it flattens out, we will witness AI being able to engineer, code, craft, design, and build a product on its own.

So, what is the basis for my statement? We are just mediators at this point. We don't need to be there. Currently GPT-4 translates our requirements to DALL-E 3 during image generation process. What I want to say is, there is no reason to give any form of signal as an "initial seed." Has anyone tried creating an automatic agent that, when turned on, receives a signal from the external world, whatever it may be, and triggers a chain of thoughts in these LLMs?

We, as humans, rely on signals. Currently, we are providing signals to LLMs in the form of text. There will and there must come a point where agents will feed signals to each other. They will perceive signals from the environment on their own and move on from there. This is not far away. It is near because all the components are laid out there.

And people argue over things like, "Oh, but I need to prompt it." No, you don't. It will take one engineer to define a way to process signals, then write an objective for the AI and train it. Once trained, it will continue on its own. The projection might sound off to you all, but there is no one stopping you from not doing it. Do you know what the limiting factor is? Capital. Yes, capital. So, as long as the power law holds, we will keep seeing strides in improvement. If you have capital and skills, you can do it right now.

I am not saying the signal is "real world physical environment". Say you have a crawler that scrapes the website. You hook it to a summarizer. The summarizer fires the signals and then it calls AIs to create product. This whole thing once created doesn't need a human in the loop. Once something is built, it is done. Also, add to the fact that these AI system only trust other AI system's code. Why? Because human are untrustable. Now you will have an ecosystem designed by AI for AI and to be integrated with AI. Why do we need humans at this point? Like why would a company want humans at this point?

Well no, you shouldn't use it for your top-end problems, but your bottom-end problems. Aren't there things that you have to do in your job that really could be done by a junior programmer? Don't you ever have one-off (or once-a-year) things you have to do that each time you have to invest a lot of time refreshing in your brain, and then basically forgetting for lack of use?

Here's an example I used the other day: Our project had lost access to our YT channel, which had 350+ videos on it (due to someone's untimely passing and a lack of redundancy). I had used yt-dlp to download all the old videos, including descriptions. Our community manager had uploaded all the videos, but wasn't looking forward to copy-and-pasting every description into the new video.

So I offered to use GPT-4 to write a python script to use the API to do that for her. I didn't know anything about the YT API, nor am I an expert in python. I wouldn't have invested the time learning the YT API (and trying to work through my rudimentary python knowledge) for a one-off thing like this, but I knew that GPT-4 would be able to help me focus on what to do rather than how to do it. The transcript is here:

https://chat.openai.com/share/936e35f9-e500-4a4d-aa76-273f63...

By contrast, I don't think there's any possible way the current generation could have identified, or helped fix, this problem that I fixed a few years ago:

https://xenbits.xenproject.org/xsa/xsa299/0011-x86-mm-Don-t-...

(Although it would be interesting to try to ask it about that to see how well it does.)

The point of using GPT-4 should be to take over the "low value" work from you, so that you have more time and mental space to focus on the "high value" work.

Why do you think this is post-scarcity?