Comment by ThalesX | Hacker News Reader

Comment by ThalesX | Hacker News Reader

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> You can program GPT in English.

> Let me repeat that: You can program GPT in English. ENGLISH!

How?

Let me repeat that: How?

I had a little script that from time to time parses a list of jobs from a specific board, extracts some categories, inserts them into an SQLite and have a frontend that displays them to me in a way I want.

The board has since changed some things which would mean maybe 2 hours of commitment from me to update the script.

How do I program GPT in English. ENGLISH! To do that for me? What are the steps involved? I've been using ChatGPT and GPT-4 for awhile and I can't imagine what the steps are to make this happen without a lot of back and forth. I can't imagine how to program the infrastructure. I can't imagine how the API endpoint is more than a fancy autocomplete. I need help understanding what it means that I can program it in ENGLISH! (I can also program it in my country's language for what it's worth).

> That doesn't mean the world hasn't changed, forever.

I sort of agree with this.

> make this happen without a lot of back and forth

Perhaps this is the part you're missing. When I've watched people program with ChatGPT it _is_ a lot of back and forth because an enormous amount of context is able to be stored and back referenced. I.e. one wouldn't say "make me a Flappy Bird clone for iOS", they'd start with:

"Give me the code for a starter SpriteKit project". Then

"Now draw a sprite from bird.png and place it in the center of the screen".

"Now make it so the bird sprite will fall as if it's affected by gravity"

I won't bore anyone with how might one go from that all the way to a simple game, but I'm sure you see the idea. There are obviously _huge_ limitations to this approach and professionals will get hit them fast, but the proof is in the pudding: people who can barely code are producing real software through this approach. It's happening.

> Perhaps this is the part you're missing. When I've watched people program with ChatGPT it _is_ a lot of back and forth because an enormous amount of context is able to be stored and back referenced.

I've tried to build a lot of fun stuff with it so far. Haven't been able to properly 'program it in English' for anything non-trivial. Back and forth ended up in loops of not what I wanted. I'm just utterly confused at the difference in experiences I've had with it vs. what some people are preaching.

> There are obviously _huge_ limitations to this approach and professionals will get hit them fast, but the proof is in the pudding: people who can barely code are producing real software through this approach. It's happening.

I've had 4 product people I know try to create products using ChatGPT. All 4 of them basically got stuck on the first steps of whatever they were trying to do. "Where do I have to put this code?", "How do I put it online?", "How do I store user data?", "Where do I get a database from?". Basic questions to any professional, but to them it was impossible to overcome the obstacles from code to deployment.

I don't doubt that it's happening and it will become better in the future; I'm just having a hard time trying to grasp where some people are coming from when my experience as a professional, using it, has been mixed.

i've observed this schism between people who can get LLMs to produce useful output and people who are baffled, I think it's a mixture of two things:

expectations: using to the LLM to break problems into steps, suggest alternatives, using the LLM to help them think through the problem. I think this is the people using it to write emails - myself included, having a loop to dial in the letter allows me to write the letter without the activation energy needed to stare at a blank page

empathy: people who've spent enough time interacting with an LLM get to know how to boss it around. I think some people are able to put themselves in the LLMs shoes and imagine how to steer the attention into a particular semantic subspace where the model has enough context to say something useful.

GPT4 writes boilerplate python and javascript servers for me in one shot because I ask for precisely what I want and tell it what tools to use - I think because I have dialed in my expectation for what it's capable of and I learned how to ask in precise language, I get to be productive with GPT4's code output. Here's a transcript: https://poe.com/lookaroundyou/1512927999932108

Interesting point about empathy. Sorry I'm abusing the comment system to get back to your comment in the future.

Let me give you a simple example. I had to deal with a desynced subtitle file recently. I described the exact nature of the desync (in terms like "at point X1 the offset is Y1, and at X2 it is Y2") to GPT-4 and asked it to write me a Python script to fix this. It did require a couple tweaks to run, but when it did, it "just worked".

"Automatic Language-Agnostic Subtitle Synchronization"

Link: https://github.com/kaegi/alass

It's basically magic.

Honestly don't think it will be long before gpt can read this comment, then politely ask you for the urls of the job board and your git repo and 2 seconds later you will have a pull request to review

You might find this interesting - https://github.com/Torantulino/Auto-GPT

> Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, autonomously develops and manages businesses to increase net worth. As one of the first examples of GPT-4 running fully autonomously, Auto-GPT pushes the boundaries of what is possible with AI.

Perhaps, but I'm talking about right now. I would love to be able to do this as I have 1000 ideas and no time to try them out.

I have some scrapers built in Scrapy, and from my experimentation with GPT4, I bet you could paste in your scraper code, the html source from the website in question (at least the relevant part), and tell GPT4 to update your scraper and you'd get something that's at least 95% correct within 30 seconds.

The same way people used to write code in the early days: trial and error, and a boatload of cursing!

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