chatGPT-discord-bot
sharegpt
chatGPT-discord-bot | sharegpt | |
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9 | 37 | |
2,573 | 1,683 | |
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8.5 | 6.9 | |
12 days ago | 6 months ago | |
Python | TypeScript | |
GNU General Public License v3.0 only | MIT License |
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chatGPT-discord-bot
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My friend tried making a discord bot with ChatGpt integration
Neat! If you're looking for an open source bot that works and has some cool features, check out this one: https://github.com/Zero6992/chatGPT-discord-bot
- Anon gets a Switch
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Alternative front end?
There are two methods I've found, Zero699's ChatGPT Discord Bot and lencx's ChatGPT Desktop Client (Mac / Windows / Linux). The former seems viable cross-platform, but it's complicated; the latter appears more straightforward but has no mobile option.
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How Should I Be Using “await” for Coroutines
Hi all, so I'm playing with some code that originally was a discord bot For ChatGPT. I'm trying to strip it to just work with phrases via the terminal. everything was going ok until I removed the last chunk of discord code that waited for a user's input. The specific issues are on lines 119 & 147. I believe that this is my final hurdle to achieve my desired code. I have included my whole code minus the json file called "config.json" that contains my OpenAI key
- I made a GPT-3.5 powered Discord bot in python.
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making a lain bot for discord, what would be some cool features?
If you want Lain-bot to be a true consciousness of the Wired, you should make it a ChatGPT Discord bot.
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ChatGPT API model leaked
Was easily integrated into the Chatbot I use (https://github.com/Zero6992/chatGPT-discord-bot)
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ChatGTP tools you may need - Work always in progress)
chatGPT-discord-bot: Integrate ChatGPT into your own discord bot
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Discord Bot for Chat GPT
The new repo you have listed in there (https://github.com/Zero6992/chatGPT-discord-bot) works great, thanks
sharegpt
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How Open is Generative AI? Part 2
Vicuna is another instruction-focused LLM rooted in LLaMA, developed by researchers from UC Berkeley, Carnegie Mellon University, Stanford, and UC San Diego. They adapted Alpaca’s training code and incorporated 70,000 examples from ShareGPT, a platform for sharing ChatGPT interactions.
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create the best coder open-source in the world?
We can say that a 13B model per language is reasonable. Then it means we need to create a democratic way for teaching coding by examples and solutions and algorithms, that we create, curate and use open-source. Much like sharegpt.com but for coding tasks, solutions ways of thinking. We should be wary of 'enforcing' principles rather showing different approaches, as all approaches can have advantages and disadvantages.
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Thank you ChatGPT
You can see the url in the comment, https://sharegpt.com and if you go there it gives you the option for installing the chrome extension, after that it shouldn’t be hard to use it
- The conversation started as what would AI do if it became self aware and humans tried to shut it down. The we got into interdimensional beings. Most profound GPT conversation I have had.
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Übersicht aller nützlichen Links für ChatGPT Prompt Engineering
ShareGPT - Share your prompts and your entire conversations
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(Reverse psychology FTW) Congratulations, you've played yourself.
Or used https://sharegpt.com
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"Prompt engineering" is easy as shit and anybody who tells you otherwise is a fucking clown.
you can gets lots of ideas here > https://sharegpt.com/ (180,000+ prompts)
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I built a ChatGPT Mac app in just 20 minutes with no coding experience - thanks ChatGPT!
I would love to read the whole conversation: Check out this cool little GPT sharing extension: https://sharegpt.com - that way the code snippets can be copied easily
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Teaching ChatGPT to Speak My Son’s Invented Language
> Cool, that’s really the only point I’m making.
To be clear, I'm saying that I don't know if they are, not that we know that it's not the same.
It's not at all clear that humans do much more than "that basic token sequence prediction" for our reasoning itself. There are glaringly obvious auxiliary differences, such as memory, but we just don't know how human reasoning works, so writing off a predictive mechanism like this is just as unjustified as assuming it's the same. It's highly likely there are differences, but whether they are significant remains to be seen.
> Not necessarily scaling limitations fundamental to the architecture as such, but limitations in our ability to develop sufficiently well developed training texts and strategies across so many problem domains.
I think there are several big issues with that thinking. One is that this constraint is an issue now in large part because GPT doesn't have "memory" or an ability to continue learning. Those two need to be overcome to let it truly scale, but once they are, the game fundamentally changes.
The second is that we're already at a stage where using LLMs to generate and validate training data works well for a whole lot of domains, and that will accelerate, especially when coupled with "plugins" and the ability to capture interactions with real-life users [1]
E.g. a large part of human ability to do maths with any kind of efficiency comes down to rote repetition and generating large sets of simple quizzes for such areas is near trivial if you combine an LLM at tools for it to validate its answers. And unlike with humans where we have to do this effort for billions of humans, once you have an ability to let these models continue learning you make this investment in training once (or once per major LLM effort).
A third is that GPT hasn't even scratched the surface in what is available in digital collections alone. E.g. GPT3 was trained on "only" about 200 million Norwegian words (I don't have data for GPT4). Norwegian is a tiny language - this was 0.1% of GPT3's total corpus. But the Norwegian National Library has 8.5m items, which includes something like 10-20 billion words in books alone, and many tens of billions more in newspapers, magazines and other data. That's one tiny language. We're many generations of LLM's away from even approaching exhausting the already available digital collections alone, and that's before we look at having the models trained on that data generate and judge training data.
[1] https://sharegpt.com/
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Humans in Humans Out: GPT Converging Toward Common Sense in Both Success/Failure
of that conversation. Perhaps something like shareGPT[1] can help?
[1] https://sharegpt.com
What are some alternatives?
BetterChatGPT - An amazing UI for OpenAI's ChatGPT (Website + Windows + MacOS + Linux)
ChatGPT - Lightweight package for interacting with ChatGPT's API by OpenAI. Uses reverse engineered official API.
Ghidra-ChatGPT
llm-workflow-engine - Power CLI and Workflow manager for LLMs (core package)
ChatGPT-Next-Web - A cross-platform ChatGPT/Gemini UI (Web / PWA / Linux / Win / MacOS). 一键拥有你自己的跨平台 ChatGPT/Gemini 应用。
unofficial-chatgpt-api - This repo is unofficial ChatGPT api. It is based on Daniel Gross's WhatsApp GPT
chatgpt-twitter-bot - Twitter bot powered by OpenAI's ChatGPT API. It's aliveeeee 🤖
openai-python - The official Python library for the OpenAI API
delegate - Universal interface for large language models
chatgpt-conversation - Have a conversation with ChatGPT using your voice, and have it talk back.
chatgpt-web - 用 Express 和 Vue3 搭建的 ChatGPT 演示网页
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]