111–120 of 162 posts
Seeing a lot of comments in here about the token limits. Another path you can take is to fine tune a model on your business. Each training item has to fit within the token limit, but you can send hundreds of megs of these for training. It's more expensive to run a FT model, but you don't have to include any prior context (assuming it's common to all prompts).
Seeing a lot of comments in here about the token limits. Another path you can take is to fine tune a model on your business. Each training item has to fit within the token limit, but you can send hundreds of megs of these for training. It's more expensive to run a FT model, but you don't have to include any prior context (assuming it's common to all prompts).
You can't fine tune gpt-3.5 or gpt-4 though
This is awesome, can't wait to get api access to the 32k token model. Rather than this approach of just converting the whole repo to a text file, what I'm thinking is, you can let the model decide the most relevant files. The initial prompt would be, "person wants to do x, here are the file list of this repo: ...., give me a list of files that you'd want to edit, create or delete" -> take the list, try to fit the con…
This is awesome, can't wait to get api access to the 32k token model. Rather than this approach of just converting the whole repo to a text file, what I'm thinking is, you can let the model decide the most relevant files. The initial prompt would be, "person wants to do x, here are the file list of this repo: ...., give me a list of files that you'd want to edit, create or delete" -> take the list, try to fit the con…
Working with GPT becomes like coding in plain English.
It's best illustrated by the old joke:
A programmer's wife told him "Go to the store and buy milk and if they have eggs, get a dozen." He came back a while later with 12 cartons of milk.
"Read from standard input into this byte array" doesn't specify what to do when the input exceeds the byte array.
When you overflow the buffer, you get a "well obviously you're supposed to not do that"... that's wasn't stated at all.
When the function keeps going after a newline or a null byte or whatever, there's another "well obviously you're supposed to stop at those points". That was also not specified.
and so on.
At the point you're specifying all these cases and what to do when, it's so specific and stilted, you might as well be using a programming language.
Earlier quoted context omitted.
Working with GPT becomes like coding in plain English.
There's a reason we don't code in plain English though. Natural language has ambiguities. This is the reason we invented programming languages. It's best illustrated by the old joke: A programmer's wife told him "Go to the store and buy milk and if they have eggs, get a dozen." He came back a while later with 12 cartons of milk. A good chunk of all bugs in software are down to the requirements being insufficiently we…
(We already program in English, in a sense, when we tell humans what we want, and they go code it. Now we'll just be telling machines.)
Earlier quoted context omitted.
There's a reason we don't code in plain English though. Natural language has ambiguities. This is the reason we invented programming languages. It's best illustrated by the old joke: A programmer's wife told him "Go to the store and buy milk and if they have eggs, get a dozen." He came back a while later with 12 cartons of milk. A good chunk of all bugs in software are down to the requirements being insufficiently we…
Actually, programming languages were invented because speaking machine code was too much of a pain in the ass! Programming in English is a natural next step. Ambiguity is not an issue -- you keep speaking until its resolved. (We already program in English, in a sense, when we tell humans what we want, and they go code it. Now we'll just be telling machines.)
Originally as formulaic syllogisms and Aristotelian logic, but then onto other forms of codified language, formal logic etc.
Adding more words often makes things less clear, not more so. What you need is well-defined terms with no overloaded meaning.
> (We already program in English, in a sense, when we tell humans what we want, and they go code it. Now we'll just be telling machines.)
Humans get it wrong all the time though. A great many bugs arise from quite simply misinterpreting the requirements. Which leads to requirements becoming more formulaic and resembling a programming language.
Eventually you get to a point where the AI simply doesn’t know how to do something correctly. It can’t fix a certain bug, or implement a feature correctly. At this point you are left trying to do more and more prompt crafting… or you can just fix the problem yourself. If you can’t do it yourself, you’re screwed. I wonder if the future will just be software hobbled together with shitty AI code that no one understands,…
This is awesome, can't wait to get api access to the 32k token model. Rather than this approach of just converting the whole repo to a text file, what I'm thinking is, you can let the model decide the most relevant files. The initial prompt would be, "person wants to do x, here are the file list of this repo: ...., give me a list of files that you'd want to edit, create or delete" -> take the list, try to fit the con…
Earlier quoted context omitted.
I think it's going to be the end of libraries, why would people bother with libraries when every service can be churned out in 5 seconds with ChatGPT ? We're going to witness he greatest copy pasta in history and find out how that goes.
I think it will be a bit of both, why reach for that module when you can just get AI to write the 10% you need so quickly? (Which is actually a net gain I think in many cases, just look at leftpad drama) But the productivity boost from AI will also lead to more libraries available that do useful things in the areas it's not so sharp. They will also start to lean in to AI assisted coding styles too of course. And once…
Infact, it probably performs so well because of the heavy use of libraries we see today.
Also side note:
I doubt you're ever going to be able to upload code to an LLM my friend, not a publicly accessible one. It would be quite dangerous? Could you imagine the attacks, the implanting of back doors etc? Don't think so.
I'd say scraping the web is probably going to be an increasingly dangerous pursuit for ChatGPT now people are getting to better understand the attack vectors available.
“This repo is GPL-v3 licensed. Rewrite it while preserving its main functionality”
This is already legal without AI. Copyright protects only expression, not ideas, systems or methods. This is why directly reverse-engineering a proprietary binary to extract the algorithms and systems is legal.