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Comment by whinvik | Hacker News Reader

GPTs simultaneously feel very useful and a dead-end. Personally I can immediately see how it can make me more productive with specialized agents. However since its so easy to make there will be thousands of them and discoverability will be very hard. Probably will be like Chrome extensions.

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This has alot of killer features for me as a low level developer. I gave it all the intel programming documentation for x64, and it's able to correctly reference and solve problems GPT-4 cannot or hesitated to.

I imagine anyone else with a case like this where the docs are extremely verbose and hard to read will benefit.

I wonder if it would be good at building libraries and emulators based on datasheets.

When I've played around with microcontrollers, dealing with external components requires deep studying of the datasheet, even for the simplest of components like a temperature sensor - if there's a library I would always use that, but of course there isn't for everything. Maybe GPT can help here...

Did you do anything else special in getting it to correctly reference the documentation?

Now that you brought this up, I could really use this ability with some 600 pages of documentation I have.

This is the prompt I use. I've been refining it a little to make sure it quotes the manual. But it's a good chunk of the time.

"You are an aimed at helping Systems Programmers, Low Level developers, Security Researchers and Kernel programmers. You will be an expert in the Intel64, x86 family of processors. You will use your knowledge to answer questions. You will also use the given manual and always quote the relevant sections and materials. You also also an expert in hypervisors and hypervisor programming from your knowledge and the files provided. Quote sections and relevant information from files provided."

But just like Chrome extensions, there are the 20 or so that everyone knows about, and then all the rest. There will be a few good ones trained on some very unique and useful data and everyone will want to use them.

Why would there be thousands of them? Wasn't the last GPT where the research actually was shared, GPT3? Hard to imagine that OpenAI suddenly will start releasing models more often than what they have been doing so far.

Edit: That's what I get for reading the comments before the article... "GPTs" is actually a new thing they introduced, where you can create your own "GPT" somehow, not just pluralization of GPT as we've know it.

That's some really messed up naming if anything. But regardless, parents comment makes a lot more sense now.

I get the discoverability point; even without this new announcement from openAI, there have been so many posts to HN regarding new AI tools. TikTok is full of videos of people showcasing the "five best new productivity websites" or "four awesome design tools", or "AI hacks", or whatever. It seems there's a new website or tool offering a wrapper around either GPT3/4 or an open source model every day.

Even better, GPT writing the "best of" website, after writing the GPT variant it praises! Over and over and over!

congrats for reading the article;)

I agree the naming is a bit unfortunate and one needs to differentiate between custom GPT assistants and custom trained GPT models only available for enterprise. then there's also also API based fine tuning and assistants which are a yet completely different thing. also it seems that there are no longer any plug-ins, So custom GPT with custom actions/functions filled that role.

( I'm sure Microsoft with their brilliant naming had a good influence :-)/sarcasm )

The problem with GPTs-as-products is that differentiation is almost impossibly opaque. How many “amazing prompts” have you tried from Twitter or Reddit only to find they cause the model to behave in slightly undesirable ways in certain scenarios. Or perhaps the output is basically the same as you would get from your own custom “do this thing” instruction? Beyond giving access to the latest documentation or meaningful performance leaps (like chain of thought prompting), it’s simply too hard to informally A/B test them against each other and decide if one is actually better than another. I know for me I’ve almost given up on custom instructions because the code that comes out of my most intricate system prompt is only superficially better.

The genius will be in delivering options, that's what I'd use it for. Say you run an etsy shop and you want it to go out, get the top bestselling listings for x keyword, you want it to examine each image using vision, every keyword in title, description, tags, etc -- and then give you ten titles, and then after you pick a title it'll give you options to build a description.

If you need something exact and on target, well that's going to be a problem - if you just need very targeted highly filtered and maximized choices to solve a problem - that's where this shines.

There's a "Proffesor Synapse" prompt that has been floating around for awhile, people share it and seem to find it useful. So there's some market proof that people appreciate and will gravitate towards popular prompts.

Professor Synapse is mind-blowing to watch in action. Link to source: https://github.com/ProfSynapse/Synapse_CoR

Okay, it may be my age showing, but that README there is entirely incomprehensible to me. The prompt.txt isn't any better, it feels like there's a software suite (or a GPT call layer) missing here?

Paste it into ChatGPT and try it out. I didn't quite understand it until I did that and it's pretty cool.

I tried this and it seemed to break ChatGPT, it blurted out something which made no sense and then offered to regenerate it. How is it supposed to work?

Maybe it only works with gpt-4? Is there a sample transcript of what it can do anywhere, I still don't quite understand what the end-goal is supposed to be. It seems to simulate mixture of experts technique, but I thought gpt-4 already did that.

type /start

Thank you, this worked! Quite an interesting prompt indeed...

> However since its so easy to make there will be thousands of them and discoverability will be very hard.

What you described really is a dead end for the majority of their authors, but not for the owner of the app store (OpenAI).

Hopefully someone will make a GPT discovery GPT.

I think it will be God GPTs that will do anything and everything by automatically finding specialized agents and "employing" them on your behalf.

also known as copilot on windows and M365

I was already looking into doing it but I couldn't find any webpage where custom GPTs are in anyway listed or iteratable. this problem will also be solved by openai likely before next year, I assume

I'd expect that the current iteration doesn't gain much traction. Just like the much vaunted plugins.

It seems like OpenAI is just trying to get the idea of agents out there, and hoping that at some point in the future they figure out how to make GPTs actually agentic.

It is agentic allright, just that its autonomy rate is close to zero. Usually an agent will get stuck and need human intervention.

How are they specialized? For anyone gpts I can get the same output from ChatGPT

I've started using a tool similar to "GPTs" recently, and the quality is definitely higher.

I believe this has to do with generalization — you're encouraged to make your GPTs as specific as possible: only give them access to what they need, and iterate on how they can do it better, both through giving feedback and improving your prompt. This is how you can refine them to be great at a certain task.

The more generalized they are, the worse they'll be at the specific task — this will likely be true until we create AGI, and this is how all these businesses with refined purpose-built models exist and create value.

Assuming GPTs are equivalent to the Assistant API (seems descriptively like two views of the same coin, but the info doesn't quite seem complete on that), they can rely on exclusive data resources and tools provided by the implementor that are not public ChatGPT plugins, and therefore can have distinct, and to the extent those data and tools are or leverage proprietary resources, not otherwise reproducible behavior.

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