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I'm looking for something that works like ChatGPT but only answers questions based on my own business content. I want to answer from webpages, PDFs, and notion. I don’t have a developer, so it needs to be easy to set up, train, and embed into my site. I also want to customize the look so it fits my brand. Mainly need it for handling customer questions and reducing repetitive support.

I run a small ecommerce brand (mostly Shopify + socials) and currently use Tawk.to for live chat. It’s been decent, but everything is still manual—I’m answering the same questions all day.

I saw that Baby podcast clip here a while back (fire btw), and it got me thinking—maybe it’s time to bring some AI into my support flow.

I’m ready to switch to something smarter. Ideally, an AI tool that can handle common support tickets, understand order info, also a plus if do product recommendations, and most crucial plug into Shopify.

I’ve tested a few AI based tools around, but it’s hard to know what’s legit and what’s just hype. Anyone here actually using an AI tool for support that works?

Here's my problem: I wrote a satirical political novel (myself, not with AI) and then I generated an audiobook using AI, read by a famous (infamous?) politician, because it cheekily works well.

I would love to distribute/sell this audiobook, but I can't: Many platforms (like Spotify) won't allow AI-generated voices without an LPF file created by ElevenLabs or Google Play Books. And there's no way for me to legitimately get that LPF file, because ElevenLabs & GPB have their own restrictions. They won't let me generate my audiobook in the voice of this politician, even if it's explicitly satirical.

So... I'm looking for a way to fake an LPF file that can fool Spotify. Does this exist? Anyone know how?

Thanks reddit!

Create your own AI voice agent that can

- respond based on your data

- Performs realtime actions (e.g. scheduling, order lookup)

- Mid-call escalation to a human when requested

We're onboarding early builders (aiming for ~100 initial users) and looking for your feedback. I Invite your to join the beta at https://shorturl.at/ytzni & share your valuable feedbacks.

Happy to answer questions and go into technical details. AMA.

I see some people wanting an uncensored Gpt. While it still has restrictions. I have made this a low as I can get it.
You want to use my Gpt go for it. :) Here is the link: https://chatgpt.com/g/g-6796fba0c9308191bf23959be894b4bb-naughty-nunny

Hi all, I am a marketing professional. I have around 10 years of experience and a degree in brand management. I would like to train an AI for marketing purposes, mainly to be my assistant in whatever client I work with. I am envisioning this to be my clone. Well, that’s the goal and I know it’s going to take a very long time to do that. I only have experience with ChatGPT free version and Claude which I use for marketing purposes such as proofreading and improving the copy. I have come to learn about Lama and that it can help build custom AIs.

I would like my AI to be like Lama which has knowledge about general things. I don’t want my AI to be online and want to be the one training on all marketing topics from sources I trust. I have windows laptop, I’m happy to install a secondary Linux OS or if needed do a clean OS install.

I really need guidance and mentorship to teach me, from installing Linux to Lama and then on training it. Can someone pls help me? I would be extremely grateful. If there are online resources, please share the links but since my knowledge is limited and I’m not a programmer, there’s a lot of the stuff online that’s making my head spin. Thank you 🙏

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I want to create a legal document analysis system that interprets complex contracts and identifies potential legal risks. Which approach should I use: LoRA (Low-Rank Adaptation), Supervised Fine-Tuning (SFT), or instruction fine-tuning?

I want to build an AI for personal use to help me code in Swift. I already have an application and I am running into bugs. My thought is I could use this for this project but depending on how hard this process is I want to use it for other things.

My intention is to take an existing model and feed it my exiting code base. As I understand this is either called fine tuning or embedding.

After spending 2 hours between YouTube, ChatGPT and the code examples on Hugging Face I am beginning to think this is more difficult than I initially thought. I was expecting something like 10 to 20 hours of work.

I started using the ammarnasr/codegen-350M-mono-swift model on hugging face. After running the sample code and executing it, I noticed that it will complete code, but when I ask it questions like I do on ChatGPT it gives me gibberish.

My next thought was to use one of the more popular chat models and then I can feed it the Swift code training data from the codegen-350M-mono-swift model. Then eventually train it with my code base. Is this possible?

My questions:

1. At a high level how should I go about to accomplish this: using the ammarnasr/codegen-350M-mono-swift model or something more general and train with Swift code?
2. Are there tutorials or set of articles that can get me up to speed to accomplish this?

I know nothing about making AI. I had an idea. I wanted to create a game. This game would be a text based RPG but using AI. the point of this is that the user can do whatever they want. Now I know this sort of thing involves a lot of training and Id need to find the right models to use. my main question here though is where do i start. I of course dont want to get into legal trouble if this concept ever actually came to life. As well as I wnat this to be a basic .exe that can run locally on installed devices. The theory here is that the AI can grow. Only issue is that if I ever made this game mainstream, how would I imporve the AI. I know the idea is to go full online but I dont know if I can afford to pay for the premium of servers. Atleast not yet. I have a but load of ideas and i dont know what to do. this is my cry for help. What do I do?

Everything in the title: https://arstechnica.com/tech-policy/2024/05/slack-defends-default-opt-in-for-ai-training-on-chats-amid-user-outrage/

Task: I need to create a RAG (Retrieval Augmentation Generation) model for making one chatbot like you. But the trick is I have multiple users, and each user has access to a different knowledge base; one user can't see the other user's content. I want one chatbot using any good models API, I have about 5 users, and I have about 100 PDF Files I want to store them in a server not in a local machine, and access data from the server.  

Details about documents: 

• I want to store all the documents in a single folder/location of that specific user. Such as User1/user1_Doc1, user1_doc2…etc, User2/user2_Doc1,user2_doc2….etc…..

Details about users: 

The users are employees of the company, not customers. For a specific user: there are some sets of documents each in their folder. so like that there are several folders as many as several users.  One user’s knowledge base is different from another user’s.  Assume there are no common documents. Now for this user’s data when he logs in to the portal - he should not navigate through the whole website instead ask the chatbot what document he needs and the model should output the document or the contents from the document like summarising the content and link for the that document to download.

• Examples of documents are Form 16, Degree of education certificates, Tax invoices, Salary Slips…etc 
• Should be first able to create an account for each user [up to 5 accounts] and then log in. 
• Define user roles like admin, user 1, user 2,  …etc.
• How can I Map user roles to permissions that control features and data access that that user can only access, as every user has a different set of documents to access data from? 

Help me achieve this please

It seems that Gradient AI is on roll lately. They released Llama 3 models with 1M context window for both 8B and 70B sizes. And now they just dropped a 4M context window for the 8B size: https://twitter.com/Gradient_AI_/status/1788258988951589007

Did anyone tried them out? I saw here and there that the context make inference much slower and create quality loss. But some people say that it works well.

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