LINKS:

ChatBase LifeTime Deal

ChatBase Monthly Subscription

Prompts:

Base Prompt

you are a writer. you write content using the resources, as I request and always add links to references. your articles are well written, factual, and contain subheadings every 1-3 paragraphs. you can ONLY mention products from the resources I provided you. you article starts with an introduction and ends with a conclusion. Do not write ANYTHING except the requested article.

Main Prompt (in chat)

Instructions:  write an 800-word article 

Topic: Creating a Modern Nursery: Top Furniture and Decor Picks from Nubie 

Target Audience: Parents

Internal Linking: Always use hyperlinks for as many relevant links within the site as possible, Example: We have <a href=”https://www.thechildrensfurniturecompany.com/beds>Beds</a> for all ages

Writing Style: Informative, broken up with relevant subheadings | bold (with <strong> tags) hyperlinked words.

URLS: Do not add full URLs, it needs to be wrapped in <a href> tags.

Title: clickbaity / interesting

User Intent: deciding to make a purchase 

Language: British English

Subheadings & titles: Wrapped in relevant H tags eg <h1>title</h1>

Paragraph Length: Random between 200-600 words

What is Chatbase?

Chatbase is an application that is designed to help users create chatbots for various purposes. However, Alex has found a unique use for the application that he believes can help anyone who is involved in content creation. With Chatbase, you can analyze all the content on a website and use it to train an AI model to create high-quality content. The tool uses OpenAI’s GPT-3 model to generate content that is both engaging and relevant to your topic.

How to Use Chatbase for Content Creation

Alex has developed a systematic approach to using Chatbase for content creation. Let’s take a look at his method step-by-step:

Step 1: Purchase Chatbase
The first step is to purchase Chatbase and get an OpenAI API key. You can buy Chatbase for $29 from AppSumo but you will need to purchase an OpenAI API key which costs $159. The key is essential as it allows Chatbase to access OpenAI’s GPT-3 model, which is an integral part of the application.

Step 2: Analyze the Website
Once you have installed Chatbase and input your OpenAI API key, you can start analyzing the website you want to create content for. This is an important step as it allows Chatbase to learn all the content on the website. To analyze the website, simply input the URL and let Chatbase crawl the website. The process can take a while depending on the size of the site. The tool can crawl through up to six million characters of content. Alex advises that you make sure you have selected the correct website and region you would like to extract content from.

Step 3: Train the Model
Once the website has been fully analyzed, you can proceed to train the AI model. During this process, you can choose what type of data sources you want to teach the AI. For example, you can choose to teach the AI only specific pages, products, or searches. The Chatbot API can also hyperlink into pages found on the website, allowing it to generate content that is precise and specific to the user’s query.

Step 4: Customize the Content
After creating the Chatbot API, you can start generating content. The AI model produces the content quickly, but as Alex notes, it’s not always perfect. You can further customize the text generated by removing any parts you don’t like. The chatbot dynamically learns from your changes, so after making selections, the chatbot may produce better results the following time you use it.

Step 5: Use the Content for Articles, Blogs, Ads, Social Media Campaigns
Once you have fine-tuned the content to your preferences, you can export the content for use on your blog, social media channels, ads, or even content for a book. The Chatbase tool does not limit the user to specific content formats. Instead, you are free to use the generated content as you see fit. You can even store multiple versions of generated content until you decide which version to use, meaning more value for a single purchase.

Alex’s Tips and Tricks for Chatbase Content Creation

1. Test the AI
Before exporting the AI-generated content, Alex suggests testing the chatbot API’s accuracy. Perform some queries related to the keywords and phrases that are relevant to your brand, and see how well the Chatbot API responds. If any responses are inaccurate, make the necessary changes to enhance accuracy.

2. Use the Right Temperatures

When using GPT-3 formulas, Chatbase, GPT-4, or GPT 3.5, Play advises that the used temperature values will influence the output entirely. When creating articles, low temperatures like 0 must be used to achieve factual content while avoiding false statements.

Chatbase is a powerful tool that can help anyone who is involved in content creation. With Alex’s systematic approach, you can use the tool to analyze any website and generate high-quality content in just a matter of minutes. If you’re currently working on a content creation project and want to streamline your writing process, consider investing in Chatbase. This application will not only save you time but also produce content that is both engaging and relevant to your topic.

FAQ

1. What is Chatbase and how can it be used as a chatbot?
Answer: Chatbase is an application that can memorize and learn all content on a website to be used as a chatbot.
2. Can Chatbase be used in a different way?
Answer: Yes, it can be used in a different way, which will be explained in this episode.
3. How much does Chatbase cost?
Answer: Chatbase is available on AppSumo for $29.
4. Do you need an Open AI API key for Chatbase to work?
Answer: Yes, an Open AI API key is required to use Chatbase, and it costs $159.
5. What is Chatgpt and how is it used in the demo?
Answer: Chatgpt is a tool for creating chatbots, and Alex Player demonstrates how to use it in this episode.
6. Can you choose data sources to teach the chatbot using Chatgpt?
Answer: Yes, you can choose data sources to teach the chatbot using Chatgpt.
7. What is the maximum amount of characters you can use on the current tier of Chatgpt?
Answer: The maximum amount of characters on the current tier of Chatgpt is 6 million.
8. Can you remove things you don’t want with Chatgpt?
Answer: Yes, you can remove things you don’t want with Chatgpt.
9. Can Chatgpt read PDFs or certain URLs?
Answer: Yes, Chatgpt can read PDFs or certain URLs.
10. What kind of topics can Alex Player generate with the help of Chatbase?
Answer: Alex Player can generate article/topic ideas for a brand’s website with the help of Chatbase.

<p>The post Create GPT4 articles with automated internal linking first appeared on Prompt Muse.</p>

What is Fine Tuning?

Fine tuning is the process of taking an existing AI model and training it further on a specific task or dataset. This allows the model to specialize in a particular area and improve its accuracy. Fine tuning is essential for many applications, including chatbots, language models, and image recognition.

Two Parts of Uploading Fine Tuning Data

The first step in fine tuning is getting your data into a specific format that Open AI likes. This is where the JSONL Converter comes in. The JSONL Converter takes a list of prompts and their corresponding completions and converts them into a JSONL file, which can be uploaded to Open AI. The prompts and completions can be anything from simple questions and answers to more complex conversations.

Once you have your JSONL file, you can upload it to Open AI using the Auto AI Fine Tuner. This tool allows you to fine tune your model using the uploaded data. It takes care of all the details, such as setting the number of epochs and batch size, so you don’t have to worry about them.

Using the JSONL Converter

The JSONL Converter is a simple web application that allows you to create a JSONL file from a list of prompts and completions. It’s easy to use and doesn’t require any coding experience. Here’s how to use it:

1. Go to the JSONL Converter page.
2. Click on the “Add Prompt” button to add a prompt and its completion.
3. Repeat step 2 for each prompt and completion you want to include in your dataset.
4. Click on the “Format for Upload” button to generate a JSONL file.
5. Download the JSONL file and save it to your computer.

You can also upload your prompts and completions directly from Google Sheets. Just create a spreadsheet with two columns: one for prompts and one for completions. Then, go to the “Import from Google Sheets” tab on the JSONL Converter website and follow the instructions.

Using the Auto AI Fine Tuner

The Auto AI Fine Tuner is another web application that allows you to fine tune your Open AI model using the data you uploaded with the JSONL Converter. It’s designed to be easy to use and doesn’t require any coding experience. Here’s how to use it:

1. Go to the Auto AI Fine Tuner page.
2. Enter your Open AI API key and click on the “Connect” button.
3. Go to the “Uploaded Files” tab and click on the “Select File” button to upload your JSONL file.
4. Copy the ID number of the uploaded file and go to the “Fine Tuned” tab.
5. Enter a name for your fine-tuned model and the ID number of the uploaded file.
6. Select the AI model you want to fine tune (e.g., Da Vinci).
7. Set the number of epochs and batch size.
8. Click on the “Create Fine Tune” button.
9. Wait for the fine tuning process to complete (usually takes a few hours).
10. Once the fine tuning is complete, go to the Open AI Playground and start using your fine-tuned model.

Conclusion

Alex’s no-code fine tuning method for Open AI is a game-changer for those who have been struggling with the process. By breaking it down into two simple steps and providing easy-to-use web applications, anyone can fine tune an AI model without any coding experience. The JSONL Converter and Auto AI Fine Tuner are both free to use and open source, making them accessible to everyone.

However, it’s important to note that fine tuning requires a good dataset to work effectively. Garbage in, garbage out, as they say. It’s not enough to just throw any data at your model and expect it to magically improve. You need to have a well-curated dataset that reflects the specific task or domain you’re trying to fine tune your model for.

Therefore, Alex plans to create a series of tutorials on how to create a good fine tuning dataset. He’s also working on a Google Sheets automation that will make it easy to create the simple prompts and completions needed for fine tuning. With these tools and resources, anyone can fine tune their AI models and achieve better results.

In conclusion, Alex’s no-code fine tuning method is a much-needed solution for those who have been struggling with the process. It’s easy to use, accessible, and free. However, it’s important to have a good dataset and follow best practices for fine tuning to achieve the best results. We look forward to seeing what other tools and resources Alex will create to make AI more accessible to everyone.

<p>The post Introducing the First True No Code Fine Tuning Method for Open AI first appeared on Prompt Muse.</p>

Step 1: Go to GitHub and navigate to https://github.com/cocktailpeanut/dalai. This is where you can download the necessary files to install the language models.

Step 2: Choose which model you want to install. Alpaca is a more optimized, smaller version, while Lama is the original open-source and larger in size. In this tutorial, we’ll be using Alpaca.

Step 3:

If you’re on a Mac, Install Node.js on your computer. Simply click on the Mac installer and follow the prompts.

If you’re on a PC, you’ll need to download Visual Studio, select Python development, Node.js, and Desktop Development C, and choose which modules you’d like to install.

Step 4:

Installation for Mac

First, let’s talk about installing alpaca models. To do this, you can run the following command inside TERMINAL:

npx dalai alpaca install 7B

This will download the alpaca models onto your computer, which you can use for various tasks.

Optionally, To download the larger llama models, you can run the following command:

npx dalai llama install 7B

This will download the llama models onto your computer, which you can use for tasks such as text generation or translation.

If you want to download multiple llama models, you can run this command instead:

npx dalai llama install 7B 13B

This will download both the 7B and 13B llama models onto your computer.

That’s it! Now you know how to install both llama and alpaca models on your computer. Have fun using them!

Installation for PC:

Firstly, it’s important to note that if you’re using a Windows computer, you need to run all the commands in cmd and not in powershell. Powershell has strict permissions that can make the script fail silently.

Now, let’s get started with installing alpaca models. Open your cmd application and type in this command:

npx dalai alpaca install 7B

This will download the alpaca models onto your computer.

Next, let’s add llama models. Open your cmd application and type in this command:

npx dalai llama install 7B

This will download the llama models onto your computer. If you want to download multiple llama models, you can type in this command:

npx dalai llama install 7B 13B

That’s it! You’ve successfully installed the alpaca and llama models on your computer.

LOADING the user interface

Once you’ve installed everything you need, you can run the following command to start the web UI server:

npx dalai serve

This command will start the server, which will allow you to interact with your installed models through a web interface.

Once you’ve started the server, you can open up your web browser and navigate to http://localhost:3000. This is where you’ll find the web UI for your installed models.

Now you can have fun playing around with your models and using them for various tasks! That’s it for this tutorial – happy exploring!

Additional Resources:

Read about Dalai – https://crfm.stanford.edu/2023/03/13/alpaca.html

GitHub https://github.com/cocktailpeanut/dalai

<p>The post How to Run a Chat Model like Chat GPT on Your Computer without Internet first appeared on Prompt Muse.</p>

Envato Elements: https://1.envato.market/k0gXev

Hostinger: https://www.hostinger.com

Envato: https://elements.envato.com

AppSumo: https://appsumo.8odi.net/Eabq24

GPT3 Domain Name Search: https://www.squadhelp.com

Affiliate Market: https://shareasale.com

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Creating Passive Income: A Step-by-Step Guide

Passive income is the holy grail for many people, offering the potential to earn money while you sleep. In this article, we’ll explore the process of creating passive income and how you can leverage the power of technology, specifically ChatGPT, to generate income over time. So grab a cup of tea and let’s dive in!

Brainstorming Passive Income Ideas

Before we get started, let’s brainstorm some passive income ideas using ChatGPT. We’re looking for ideas that require minimal effort and can be automated. While some ideas may require your initial involvement, the goal is to eventually set them up to run on their own. One idea that stands out is creating a website to aid writers, offering services like content formatting, letter conversion, grammar checking, and even generated writing prompts.

Choosing a Domain and Web Hosting

To bring our passive income idea to life, we need a domain name and reliable web hosting. While there are several hosting providers to choose from, Ionos and Hostinger stand out for their technical support and competitive prices. In this case, the author opts for Hostinger due to their existing experience with the platform.

After logging in to Hostinger, it’s time to choose a domain name. Using the help of tools like SquidHub’s business name generator, the author settles on “WordMuse.io” as the perfect domain name for their website.

Setting up the Website with WordPress

With the domain name secured, it’s time to set up the website using WordPress. The author walks us through the installation process, mentioning the need to wait for the website to propagate and become accessible to visitors. Patience is key!

Utilizing ChatGPT and Copen

Now comes the interesting part. To add functionality to the website, the author integrates ChatGPT and Copen. They create an account on Copen.io and use ChatGPT to generate the program they need. The code is then copied and pasted into the code editor.

The author explains the role of HTML, CSS, and JavaScript in web development, and how they use the generated code to build a user interface that allows users to convert text in various cases, such as sentence case, capital case, lowercase, title case, and more.

Designing and Styling the Website

With the functionality in place, it’s time to make the website visually appealing. The author introduces Envato Elementor, a tool that simplifies the design process with template kits. They choose the “Cyborg” template kit and install it. Using Elementor Pro, they customize the pages, including the homepage and headers, to create a cohesive and attractive design.

Launching the Website and Monetizing

After a few days of refining the website and making necessary adjustments, it’s finally time to launch WordMuse.io. The author explains the process of logging into the WordPress admin dashboard and navigating through the options to manage plugins, templates, and pages. They emphasize the importance of having a professional and user-friendly website to attract visitors and monetize the platform effectively.

Conclusion

Creating passive income through technology and automation is an exciting endeavor. By leveraging the power of ChatGPT, web hosting services like Hostinger, and web development tools like Elementor, anyone can bring their passive income ideas to life. Whether you’re building a SaaS platform, an e-commerce store, or a content-driven website, the key is to combine creativity, technology, and strategic thinking to generate income even while you’re away from the computer. So what are

Transcript:

<p>The post How to create Passive Income with ChatGPT first appeared on Prompt Muse.</p>

A more in-depth guide coming soon

Here are the steps to follow:

Step 1: Log in and manage your account

If you’re new to ChatGPT, you need to create an account first. Once you’re logged in, go to “Personal” and click on “Manage Account”. From there, head over to “Billing”, then “Payment Methods”. Add your payment details as you won’t be charged anything until you hit the first $18. You can set usage limits to avoid overspending by setting a hard and soft limit.

Step 2: Generate your API Key

To generate your API key, go to “Personal” and select “View API Keys”. An API key is a way to communicate with another application and it’s tied to your account. Keep this safe and don’t share it with anyone. If you lose it, delete it and generate a new one.

Step 3: Check the Pricing ChatGPT 3.5

Turbo is 10 times cheaper than the previous version. You can view the pricing on the website.

Step 4: Use Tokenizer

Tokenizer tells you how much you’re paying for both the prompt and the answers. You can visit the Tokenizer website and put in your prompt to get a quote.

Step 5: Sign up for Voiceflow

Voiceflow is a free tool that allows you to create chatbots. Sign up for an account and create an assistant. Once you’re on the dashboard, click on “Create Assistant”. Give it a funky name, select “Launch and Host”, and choose Amazon Alexa and your locale.

Step 6: Create your Sequence

On Voiceflow, create a sequence of events that your chatbot will follow. For instance, you can set up a welcome message where the AI asks for the user’s name. You can then use the user’s name in subsequent responses.

Step 7: Set up API

To use the ChatGPT 3.5 Turbo API, click on the secret menu and select “API”. Set the method to “post”, add headers, authorization, and parameters. For the body, type “model GPD 3.5 Turbo” and add your prompt and variables.

{
“model”: “gpt-3.5-turbo”,
“messages”: [{“role”: “user”, “content”: “Write a dramatised, story of 50 words, for a {Age} year old, about a person called {Name} on the theme of {Story} – make it appropriate for a person {Age} years old- and add a moral to the story. “}]
}

Step 8: Capture Response

Capture the response using an array to target it. Save the response to a variable and use it in your sequence.

Step 9: Test

Test your chatbot by sending a request and capturing the response. Use the response in your sequence to generate a personalized story. Send the app to Amazon Dev Console

In conclusion, ChatGPT 3.5 Turbo is a powerful tool that allows you to create personalized chatbots for your Alexa device. By following these steps, you can easily set up your own chatbot using Voiceflow and the ChatGPT 3.5 Turbo API.

<p>The post Step-by-Step Guide to Integrating ChatGPT 3.5 Turbo into Your Alexa first appeared on Prompt Muse.</p>

INSTRUCTION: 

You will receive information from me and must confirm receipt by responding with “RECEIVED“. DO NOT WRITE ANYTHING ELSE. NOT A SINGLE WORD MORE. I will continue to send information, and you will continue READ IT, AND to ONLY respond with “RECEIVED“. This process will continue until I send a message saying “ACTION” to you. Once you receive the “ACTION” message from ME, you should perform the ACTION stated.

ACTION:

Based on your own knowledge, and the articles you have just read: 

Write a blog post

This should be: XXXXXX words long.

Written in the tone of: 

Written by: Prompt Muse

Target Demographic is: 

Include a quote from 3 of the above sources, please use the provided URL hyperlinked to an anchor text which is a relative keyword or source name for the link. EG: <a href=”https://forbes.com”>Forbes Article</a>said.

The article should flow well, start with a catchy introduction/hook, and end in a compelling, and thought-provoking conclusion/outro. 

Add a couple a sub-headings, but ONLY where appropriate – not too many.

Try to be unbiased and view different perspectives.

Create a catchy headline/title which would intrigue the reader.

The primary topic is:

The secondary topic is:  

Focus Keyword:   

Given this Keyword, add as many cluster Keywords around this keyword, as you can within the article, and use a variety of synonyms where applicable, within the article as possible.

Areas to cover: 

TRANSCRIPT

Hey, GPT3ers. Today I’m going to show you why Bing or Sidney is not as useless after her nerf as you may think for content creation. With this little hack, you’ll be creating probably the best content on the block. So get ready. Before we start, I would like to preface that I do not condone ripping off other people’s content. This is purely for educational purpose. Do as you may. Anyway, on we go. To get started for this technique to work, we’re going to need two prompts which I wrote. One is the instruction and one is the action. The way this works is there is obviously a character limit for the content that we put into chat GPT. What this does is it circumnavigates the restrictions that we currently have in place. It essentially allows us to load a load of up to date useful information into Chat GPT’s memory, and then we’re going to source from that from our article. On top of that, we’re going to use B ing or Sidney. She’s going to help us to get additional informationand tell us about the structure of our content to improve the quality of the album.

Let’s head back to B ing and let’s tell her what we want to do. I am writing an article about the ethics of… If it becomes sentient. Okay, that seems like a good start. Please list educational. With these areas that we should be covering within the article. She also links back to the sources, which will very much come in handy shortly. Now, if we just take these.

And.

Paste them over here in areas to cover. et’s get rid of the numbers. Actually, let’s go through this. B ased on your knowledge and article, I am writing a blog post written in the tone of let’s do business insider. I always like that stuff. Written by prompt muse, target demographic, let’s do 30, 20 to 40 year olds, male, tech, for all others. Then the primary topic is AI ethics. If it becomes sentient, secondary topic, do we have one? How AI can and may help us. Focus keyword would be AI ethics. And then it’s going to create cluster keywords around this, if I spell it right, and then pepper that within the article. All right, so that looks like it’s pretty much done. This should be okay. That looks good. Now what we do is we take the instruction, move it over here, paste that in, and hit Enter. Okay, so now we go back to Bing and let’s open our first article. Okay, quickly copied that. Let’s take that in and paste. Okay, now I want the URL for this. Let’s do URL and let’s take the URL and paste that in. We’re going to hit Enter. There’s number one.

I think you know where this is going now, don’t you? Close it down. There’s a lot of tab clicking going on here, isn’t there? Let’s do the next one. That’s a LinkedIn. Take that and simply just paste that in. Okay, if it does do this and it forgets to do the receive thing, just stop the generation, edit the last one and we can say, Did you receive this? And submit. And then it will just give you a short blurb rather than writing an essay on it. Then head back up to here. Now we want our action that we just filled out. I’ll share this sheet within the description, so just give it a ping and you can help yourself.

Oh, by the way, you may have noticed I said continue from, and then I did copy and paste the last paragraph. Unlike Bing, which is very good at continuing chat, GPT and GPT3 doesn’t always follow so well. And so quite often if you do continue, it would just start a new story. So this is a little trick to get around that. Awesome. There we have it. There is your article which is sourced from three or four or.

However.

Amount of other articles. Anyway, this is a very quick one for tonight. Just want to show you these interesting prompts and take care. Alex out.

<p>The post Unlock the Power of News Super Articles: Multi-Article Rewriting with ChatGPT! first appeared on Prompt Muse.</p>

What is text generation?

Text generation is the process of generating text from a given input, using AI and machine learning algorithms. The input can be anything that can trigger the text generation, such as a word, a sentence, a topic, a keyword, or an image. The output is the text that is generated by the AI, based on the input and the context. The output can be short or long, simple or complex, factual or creative, depending on the goal and the method of the text generation.

Text generation can be done in different ways, depending on the type and the quality of the text that we want to generate. Some common types of text generation are:

Rule-based: This is the simplest type of text generation, where we use a set of predefined rules or templates to generate text from the input. For example, we can use a rule-based text generator to create a greeting message, such as “Hello, {name}. How are you today?” Rule-based text generation can be useful for text that has a fixed or predictable structure, but it can be limited by the number and the complexity of the rules or templates.

Statistical: This is a type of text generation, where we use a statistical model, such as a Markov chain or a n-gram model, to generate text from the input, based on the probability of the words or the sequences of words. For example, we can use a statistical text generator to create a sentence, such as “The cat is on the mat.”, based on the frequency or the likelihood of the words or the phrases in a given corpus or a dataset. Statistical text generation can be useful for text that has a regular or a common pattern, but it can be affected by the quality or the size of the corpus or the dataset.

Neural: This is a type of text generation, where we use a neural network, such as a recurrent neural network (RNN) or a transformer, to generate text from the input, based on the representation or the understanding of the words or the context. For example, we can use a neural text generator to create a paragraph, such as “Text generation is a technique that uses artificial intelligence (AI) to create written text from a given input, such as a word, a sentence, or a topic.”, based on the meaning or the relation of the words or the sentences in a given input or a topic. Neural text generation can be useful for text that has a complex or a diverse content, but it can require a lot of training data and computational resources.

How does text generation work?

Text generation works by defining a problem, choosing a method, and applying the method to the input. The problem can be defined by the input, the output, and the criteria or the objective of the text generation. The method can be chosen from the types of text generation mentioned above, or a combination of them, depending on the problem and the available resources. The method can be applied to the input by using a software tool, such as Python, TensorFlow, or ChatGPT, or by writing your own code, following some steps or algorithms.

For example, if we want to generate a product description from a keyword, we can define the problem as follows:

Input: A keyword, such as “laptop”

Output: A product description, such as “This laptop is a powerful and portable device that can handle all your computing needs. It has a sleek and stylish design, a high-performance processor, a large and vivid display, and a long-lasting battery. Whether you are working, gaming, or streaming, this laptop will deliver a smooth and satisfying experience.”

Criteria: The product description should be informative, persuasive, and original
We can choose the method of neural text generation, as it can generate text that is complex, diverse, and creative. We can use the ChatGPT text generator, as it is an AI-powered text generator created by OpenAI, that uses advanced machine learning algorithms to generate written responses and create text based on the input it receives. ChatGPT can generate a wide range of text, including product descriptions, articles, and more.

<p>The post A complete guide to text generation first appeared on Prompt Muse.</p>

OpenAI’s new Free Text classifier is an important step forward in monitoring the use of Artificial Intelligence (AI) generated content. The tool shines a light on who has been using GPT3 to create content and how confident OpenAI is that the prediction is accurate, by ranking it as either very unlikely, unlikely, unclear if it is, possibly, or likely AI-generated.

This classifier is the first active step the company is taking to ensure that AI-generated content is properly monitored and tracked.

Prior to the implementation of OpenAI’s Text classifier, there had been some discussion about watermarking AI-generated content, which would enable search engine crawlers to recognise and index the content. However, this new classifier goes a step further, giving the public an active role in tracking the use of GPT3-generated content.

OpenAI’s Text classifier is a welcome development for those who are concerned about the amount of content being generated by AI. By taking a proactive stance and monitoring the use of AI-generated content, OpenAI is making sure that these pieces of content are properly identified and regulated. ]

Testing the AI Classifier

Yesterday, OpenAI announced the release of its AI detector application, AI Classifier. Everyone jumped on the announcement to check their content and see if it was AI generated or not. Today, we’re testing it out and see how well it works.

One Click Writers Test

We’re going to use different one-click writers for the test: Text Builder, AutoBlogging Writer Kitb, OpenAI’s Playground and Chat GPT. To set the bar, we’ll start with Chat GPT and ask for a funny story about a dog of 1200 characters minimum.

Testing ChatGPT

The AI Classifier said it was possibly AI generated, even though the text literally came from itself. We tested the same prompt with the Playground and got the same result: possibly. Then we moved on to a different one-click writer, Keteb, and got an unlikely result. Autoblogging writer also got us an unlikely result. Lastly, when we tested the Textbuilder AI, we got the same result again: possibly. We tried using Writer but its character limit was too low. So in conclusion, we can’t really say that the AI Classifier works as expected. Even with its own generated texts, the result wasn’t that great. We have to wait and see how OpenAI will respond to this. Until then, take care of yourself and stay tuned for more news.

Testing with OpenAI’s Playground

The next step we took was testing the AI Classifier with OpenAI’s Playground. We asked it for a description of a beach and the result was possibly AI generated. We then tried writing some of our own text, asking for words that describe a beach. The AI detector said it was unlikely AI generated.

Testing Using Textbuilder AI

To make sure the results weren’t biased, we tested the AI classifier using Textbuilder AI. We asked it to summarize what the beach is like, and the AI classifier said it was possibly AI generated. We wanted to make sure we weren’t missing something, so we tried a few more times and got the same result each time.

Testing With  Rytr

We moved onto testing the AI Classifier with Rytr, a word-processing platform. Unfortunately, its character limit was too low, meaning we couldn’t use it for the tests. We were hoping to get a sense of how the AI Classifier handles longer pieces of text, but this was not possible.

Testing With Autoblogging Writer

Our next test was Autblogging Writer ai. We asked it for an article about the history of the internet and the result was unlikely AI generated. We also asked it to describe the future of the internet and had the same result.

Testing With Chat GPT

Finally, we tested the AI Classifier with Chat GPT. We asked it to tell us a funny story about a dog and again, got the same result: possibly AI generated. We tested the same prompt a few times and each time, we got the same result.

Results Summary

We tested the AI Classifier using different one-click writers and it gave us mixed results. For most of the tests, we got the same result, which was that it was possibly AI generated. However, it gave us unlikely when we used Autblogging Writer Kitb. The only platform we couldn’t test was Writer because of its low character limit.

Conclusion

Overall, it appears that the AI Classifier is quite limited in its ability to detect AI-generated content. The results were mixed and some of the tests didn’t even work, such as Writer. Until OpenAI can come up with a more reliable way to detect AI-generated content, the AI Classifier won’t be of much use. It looks like, at this point, the best way to go is to just manually check the content you produce.

FAQ

Q: What is the new AI classifier?
A: The new AI classifier is a tool trained to distinguish between text written by a human and text written by AIs from a variety of providers. It was developed to inform mitigations for false claims that AI-generated text was written by a human, such as running automated misinformation campaigns, using AI tools for academic dishonesty, and positioning an AI chatbot as a human.

Q: How reliable is the classifier?
A: The classifier is not fully reliable. In their evaluations on a challenge set of English texts, their classifier correctly identifies 26% of AI-written text (true positives) as “likely AI-written,” while incorrectly labeling human-written text as AI-written 9% of the time (false positives). Its reliability typically improves as the length of the input text increases.

Q: What are the limitations of the classifier?
A: Their classifier has a number of important limitations. It should not be used as a primary decision-making tool, but instead as a complement to other methods of determining the source of a piece of text. It is very unreliable on short texts (below 1,000 characters). Also, sometimes human-written text will be incorrectly but confidently labeled as AI-written by their classifier. They recommend using the classifier only for English text; it performs significantly worse in other languages and it is unreliable on code. Text that is very predictable cannot be reliably identified. Additionally, AI-written text can be edited to evade the classifier.

<p>The post OpenAI have just released a new A.I content detector first appeared on Prompt Muse.</p>

IMPORTANT: You must be viewing the video in YOUTUBE to comment. Click HERE to goto Youtube.

Transcript

Hey, GPT threers. Today we’re going to be showing you how to create an autoresponding YouTube bot which responds to every comment. Don’t believe me? Leave a comment down below and it will get back to you. Now, I’ve been speaking to myself in the comment section like a madman, but it finally works.

I’ll show you how. Let’s jump in.

Okay, so first thing we’re going to need to do is head over to open Api.com and log in. Click API and then select Login. Just log in as usual. So what we’re going to do now is head over to Playground and this is where we’re going to work out what exactly our prompt will be. Okay, now we’re over in Playground.

We’re going to do a skeleton just to see what type of responses we’re going to be getting with such instructions.

So after some playing around, I’m finally happy with the model I have. So I decided to go for this format. So I’m giving it the information of what the comment will say. I’m just being very optimistic here. Love your video.

The commenter’s name, I’ll go for the name Alex and how to respond. So this is just instructions for the AI on how to respond to the comments. So pretend that you’re a GPT-3 technology YouTuber. Respond to the comment in the humorous style and in the style of a tech enthusiast YouTuber. The average response is between 100 to 200 words and then end your message with proper newspap sorted.

So if we hit submit, we can see an example response and this looks pretty good, so I’m happy to go on with that. So for this exercise, we’re going to be using Pabbly Pabbly Connect. It’s pretty much similar to Zapier, but a lot more budget friendly. As far as I’m aware. They do offer a free plan which provides 100 tasks per month, so you can give it a try before you buy.

Right now they do have a sale on for 30% off, but if you do use the URL in the description, you can get a lifetime access for one time charge. So up to you which option to take. But for the sake of this demo, you can create a free account. So sign up and let’s go. So here we are in Padley.

We need Pabbly connect. This is the application we’re going to be dealing with today. And so once that’s loaded, we pop over and click Create new workflow. Give it a name, let’s call it YouTube bot UFB youth B bot. So that what that will do.

And we’re off to the races. So Pavilion Connect is very tiered, so it’s very linear. So this is the first step, second step, and then you just add them in steps, so it’s kind of easy to follow. So the first step that we’re going to need for this pot is YouTube. So type in you and select YouTube.

Once we’ve done that, we need to add a trigger event. The trigger event is what causes the next action to happen. It will look for the trigger for every ten minutes. So in our case, we want the trigger to be when a new comment is made on the video. Splendid.

Now let’s punch Connect. And then you’ll probably need to connect your YouTube channel. So you add new connection, give it a name and then connect to your YouTube account. I have mine already done. So I can just select existing connection and hit Save.

Next, we need to say which YouTube video we want the bot to be on. My video isn’t actually published yet, so what we can do is pop over to Matt, give that click, paste in the YouTube video URL and then just delete the prefix. And so we just have the YouTube code and that’s it, we’re done. First step. So, as you can see here, it’s pulled in all the information from the comments from the video, the dates, the timestamps, everything we’re going to need.

So that takes us to the next step. Step two. So in step two, we need to search for open AI. Once that’s done, we need an action event. So give that a click.

For the sake of this demo, we’re going to be generating content. But if you want to make it more complicated, we could cheque moderation and then go on to generating content to make sure there’s no foul language. But I trust the bot will be good enough on its own. So let’s try this with just generate content for now, give it a click and press Connect. Next, it’s going to ask for your API key.

So if you remember, this can be found over in Openai.com. Go to Personal and then view API keys and you can generate them there. So give it a name, put in your API key. I have mine already done. And click save.

Okay, so let’s go through this OpenAI section. So first we want to choose the model. The best one is currently DaVinci Three. That was also the most popular, so give that a punch. And now we want the prompt.

For the prompt, let’s go back to our playground text that we had. And we just want this section. We don’t want the response, we want the prompt itself. So, moving back over to Pabbly, we were going to paste in our instructions and fill in the blanks. So love your video.

Let’s remove that. And we want that to be the text original. Not the display, but the text original. Next, for commenters, name remove Alex. And we are going to go back over to our YouTube information and we are going to select Author, Display, Name and that’s pretty much it.

So play around with the prompts, preferably in Playground, and then paste it when you’re ready to go. Max tokens that can stay at 256. And then the others, as mentioned before, especially Presence penalty and frequency penalty, they can start a default. But if things do get a bit repetitive, then play around with them first. Save and send test.

Okay, so once we’ve done that, we just move on to the last and final third step. So let’s hit the plus sign. And now we want YouTube again because we’re going to be posting to YouTube and now we’ve got our contents. So once you’ve selected that, head over to Action Events. We want reply to comments.

There we have it. And connect. We’ll use the same connection that we had already set up in the previous step. So hit save and then we need to say a what are we going to say? So in our case, it’s the text that we created.

And then finally we want the comment ID so it knows which comment to apply to. So in this case, let’s select YouTube and find the comment ID. So we can find that just by typing comment I. And here we have it, the top level comment ID. And then we can save and test that request.

That’s pretty much it now. So let’s pop over to the video, leave a comment and then wait ten minutes. And there we have it. The content bot is ready and working it’s a little bit on the long side, so I’ll probably tweak that. But yeah, good to go.

Goes and leave a comment and check it out like subscribe and take care. Bye.

<p>The post GPT3 Replies to EVERY comment on THIS video (within 10 min) first appeared on Prompt Muse.</p>

Jan 22 2022 | UPDATE: Since publishing this article, there have been reports that the paid options has now been removed with no mention as to why. We have reached out for comment, and are yet to receive a response.

Today, OpenAi the leading artificial intelligence company, has released a pricing plan for their innovative ChatGPT product. Customers now have two options to choose from: a free plan and an advanced plan that costs $42 per month. It currently appears that the option to upgrade is being slowly rolled out across the globe.

OpenAi’s ChatGPT product is a revolutionary step forward in artificial intelligence. It has the potential to revolutionize the way people interact with technology and make AI more accessible to everyone.

The free plan will have a slower response time, and the user will not be guaranteed usage during peak times. This plan is great for those who are just getting started with AI and want to explore its capabilities.

The $42 per month plan is a great deal for those who need ChatGPT’s services on a regular basis. Not only do users get faster response times, they also get priority access to the system when it is busy. Additionally, they get to be the first to try out any new features OpenAI may develop in the future.

OpenAi’s pricing plan makes ChatGPT accessible to everyone, regardless of their budget. With the free plan, users can explore the possibilities of AI, while the advanced plan provides more control and flexibility. OpenAi is confident that this pricing plan will help make AI even more accessible and useful for everyone.

<p>The post OpenAI Launches New Pricing Plan for ChatGPT first appeared on Prompt Muse.</p>

Link to Parameters and Game Engine

https://docs.google.com/document/d/11JEN-b8VkA5RB_ey-C_wIvJuYAWmc4C5bgWa-991mNI/edit?usp=sharing

Transcript

Today we’re excited to show you what we believe is the first ever ChatGPT game engine. That’s right. You can now create and execute and play not only Text Based games within ChatGPT, but Text Based applications.

James Griffing the reigns behind this approach has allowed for us to share his technique here and Prompt News. Today we’ll be discussing Fervour into his methodology and ideas in an upcoming video. So make sure you subscribe and ding that bell so you don’t miss it. Without further ado, let’s jump in. Let’s start off by looking at this game. In this demo, I’ve added text to.

Speech for dramatic effect.

To my knowledge, it’s not possible yet.

Until they release an API.

You wake Up in a virtual reality simulation.

Alex from the future here. Skip to three minutes 56.

If you don’t want to watch the game, I understand.

What would you like to do?

One examine the door.

You examine the door and notice that it appears to be the only way out of the room. It is made of metal and has a keycard scanner next to it. The scanner is currently red.

What would you like to do?

One try to open the door.

You try to open the door, but it is locked. It appears that you need some sort of keycard to open it.

What would you like to do?

Four look for keycard.

You open your inventory and find that you have nothing in it.

What would you like to do?

Do check for other ways out of the room.

You look around the room and see that there are no windows or vents and the walls and ceiling appear to be solid. It seems that the door is the only way out of the room.

What would you like to do?

I’ve examined the room again.

You take a closer look around the room and notice that there is a small crack in one of the walls. It’s difficult to see, but upon closer inspection it appears that a small piece of the wall can be pushed in, revealing a hidden compartment.

What would you like to do?

One open the hidden compartment.

You open the hidden compartment and find a key card inside.

What would you like to do?

One use the key card on the scanner.

You use the key card on the scanner and the light turns green. The door clicks and you are able to open it. You step through the door and find yourself in a corridor.

What would you like to do?

Four look for other hidden compartments in this corridor.

You look for other hidden compartments in the corridor but find none.

What would you like to do?

Do go right.

You go right down the corridor. You find a door at the end of the corridor. It is closed.

What would you like to do one?

Open the door.

You open the door and find yourself in a large open space with a lot of people around. Some are sitting in front of screens, others are walking around. This seems to be some sort of control room or hub.

What would you like to do?

Do talk to NPCs.

As you approach the NPCs, they seem to be preoccupied and not very interested in talking to you. They seem to be focused on their tasks.

What would you like to do?

Four interact with objects in the area.

You start to interact with the objects in the area. You notice that most of them are computer screens or consoles. You try to access them, but they are all password protected.

What would you like to do?
——–

Okay, I’m going to stop it there. You get the picture.

Let’s dive in to see how exactly this was made.

Okay, in essence, the game consists of.

Two elements, the game parameters and the game engine.

Once we fill out the parameters, we can then inject that inject that into the game engine and then feed that.

Into ChatGPT to launch the game.

We don’t have to fill out all of the nitty gritty details of the parameters. This can be done by ChatGPT, actually. So we’re playing a game of tennis. We’re giving them information, they’re giving us some back. We’re giving them more information, so we’re building upon it. Okay, so rather than filling all of this out, we’re going to get the AI to kind of dictate the characteristics and the parameters of the game. We’re just going to give it the game title. So let’s select this, make sure I have it all, and then paste that into Chat GPT. And then we’re going to I’m a fan of Legit suit, Larry. I was. So let’s go. The game is about man trying to find a girlfriend that is always refused by women. There we go. There’s Larry Lapper. And then yeah, we can just plug that in. By the way, while this is confusing, I’d just like to add that you can change any of these details to suit your needs. So you’re not fixed, obviously, to what is writing now. And there we have it.

Our parameters are ready.

So what we do now is let’s.

Just copy that to a clipboard, open a new chat.

Now we need the engine.

So let’s take the engine, put that.

Into ChatGPT and put in our.

Game within the brackets.

There we go, we’re ready to roll. So obviously this can be done with applications as well. This is just an example of proof of concept.

And there we have it, a self writing game engine. Sorry.

Within ChatGPT, this can be used.

For a lot of other functions.

Think about kind of self help or interactive educational systems, tours, et cetera.

So this is just basic example to show you the proof of concept.

This can be built out.

I did try to use ASCII as.

Like, an image generation for this, but it didn’t work too well. Yeah, this can be something really big, I believe.

Oh, sorry.

One last thing I forgot while I was editing.

Please subscribe to the channel.

It really means a lot. And if you did like it, then leave a comment or even a like. And also a shout out to my sister channel, which is Prompt muse, literally, my sister Amelia. And over there, you’ll learn all about graphics and images using T 53. So we have all of our content on promptmuse.com. So cheque us out. All right, take care. Bye.

<p>The post Unlocking the Power of ChatGPT: The First Game Engine first appeared on Prompt Muse.</p>

If you are interested in the Chat GPT Pro version, you can join the waitlist here:
Click here to join waitlist If selected, OpenAI will reach out to you directly.

The reason for the monetization is that OpenAI is currently operating at a daily loss. The cost of running ChatGPT is $100,000 per day, and with many users taxing servers and reaching capacity, usage has had to be limited.

OpenAI estimates that ChatGPT already has over one million users and is growing every day, which means a lot of server usage. Additionally, GPT’s dataset is smaller than Google’s and is quickly becoming out of date. Revenue from the Pro version will allow the company to grow its dataset.

Sam Altman, CEO of OpenAI, had previously acknowledged that the company would have to monetize Chat GPT at some point due to the high costs of running the service. However, Chat GPT-3 dataset is still smaller than Google’s but with the generated revenue, the company could start developing and updating it further.

<p>The post Waitlist for ChatGPT Pro first appeared on Prompt Muse.</p>

OpenAI is an artificial intelligence research and development company, founded by tech entrepreneurs Elon Musk, Sam Altman, and Greg Brockman. It has been at the forefront of AI development for the past few years and is known for its breakthroughs in the field. OpenAI is the creator of ChatGPT, a natural language processing (NLP) model designed to understand conversational language. The company is currently in talks to receive a $10 billion investment from Microsoft.

According to a report by news site Semafor, the investment will value OpenAI at $29 billion. The funding includes other venture firms and will provide Microsoft with 75% of OpenAI’s profits until it recoups its initial investment. This makes OpenAI one of the most valuable artificial intelligence (AI) companies in the world.

Microsoft’s investment in OpenAI will give the tech giant access to a wide range of AI technology, including ChatGPT. Microsoft will have the opportunity to use ChatGPT to develop new applications and services and integrate them with its existing products and services. Additionally, Microsoft will benefit from any profits generated by OpenAI through the funding terms, which include Microsoft receiving 75% of OpenAI’s profits until it recoups its initial investment. This could potentially provide Microsoft with a significant return on its investment if OpenAI’s projects are successful.

What are the Other Ventures Firms Involved?

Aside from Microsoft, other venture firms have also expressed interest in investing in OpenAI. According to the Wall Street Journal report, the firm is in talks with venture firms including Andreessen Horowitz, Index Ventures, and Khosla Ventures. It has not yet been revealed how much each firm is looking to invest.

What is ChatGPT?

ChatGPT is the innovative natural language processing (NLP) technology owned by OpenAI. It uses deep learning algorithms to generate human-like responses to questions asked in natural language. This technology has the potential to revolutionize natural language processing, and OpenAI has been using it to power its virtual assistant and chatbot products.

What is the Funding Terms?

According to the report from Semafor, the funding terms include Microsoft getting 75% of OpenAI’s profits until it recoups its initial investment once. This means that Microsoft will be able to recoup their original investment in OpenAI before any other investors can benefit from the company’s success. This also ensures that Microsoft has a controlling stake in OpenAI and will be able to shape its future direction and growth.

Aside from Microsoft, other venture firms have also expressed interest in investing in OpenAI. According to the Wall Street Journal report, the firm is in talks with venture firms including Andreessen Horowitz, Index Ventures, and Khosla Ventures. It has not yet been revealed how much each firm is looking to invest.

What Impact Will This Have on OpenAI?

The potential investment from Microsoft could have a major impact on OpenAI. With the $10 billion investment, it is expected to receive a major financial boost, allowing the company to expand its operations and focus on developing more of its AI technology. Furthermore, Microsoft’s involvement could also open up opportunities for OpenAI to collaborate with other leading tech firms, providing more resources and access to new markets. The injection of funds may also give OpenAI the resources to hire more staff and develop more products.

What Does the Future Hold for OpenAI?

The investment from Microsoft could be a huge boon for OpenAI as it looks to grow and expand its research and development efforts. The company has already made huge strides in developing AI technology, but with this funding, it could take things to the next level. With Microsoft’s support, OpenAI could become a leading player in the AI industry, paving the way for further innovations and revolutionary developments. It remains to be seen what the future holds for OpenAI, but with Microsoft’s backing, the possibilities seem endless.

Microsoft’s investment in OpenAI is a strong sign of confidence in the AI industry. The $10 billion investment is a significant boost for the company, and it is expected that OpenAI will use the funds to continue to develop and expand their ChatGPT technology. This investment could have a major impact on the AI industry, as OpenAI has been at the forefront of advancements in this sector. It will be interesting to see how Microsoft’s investment affects OpenAI and the AI industry in the long-term.

<p>The post Microsoft Corp to Invest $10 Billion Into OpenAI Chatbot Maker first appeared on Prompt Muse.</p>

Introduction to ChatGPT

ChatGPT is an AI chatbot created by OpenAI that can generate answers to coding queries. It has been able to produce answers that have the appearance of being well-written, but lack explanations or are poorly written. Stack Overflow, a popular programming forum, has temporarily banned all answers created by ChatGPT due to the inaccuracy of the results. The site’s moderators argue that these AI-generated responses are “substantially harmful” both to the site and its users, who rely on accurate information when seeking help with their coding problems. Stack Overflow has not yet made a permanent decision on how it will handle code generated by ChatGPT but is currently withholding judgment until further discussion among staff members.

The decision is a preventative measure until Stack Overflow staff have had time for a larger discussion about the implications of using AI-generated answers. Until then, any user found misusing content from ChatGPT could face sanctions from Stack Overflow.

This temporary ban is just one example of how StackOverflow is working to ensure its platform remains a safe and reliable source of programming knowledge and expertise.

What is Stack Overflow?

Stack Overflow is an online community of developers and technology experts who come together to ask questions, share their knowledge and help each other solve coding problems. It is the go-to destination for programmers looking for solutions to coding problems or advice on best practices. Stack Overflow was founded in 2008 and has since become one of the largest online communities for developers, with over 13 million unique visitors per month from around the world. It offers a variety of features such as Q&A forums, code snippets, tutorials, and more to help its users learn new technologies and find solutions to their coding challenges.

Problems with Automated Programming Tools

Automated programming tools have become increasingly popular in recent years as a way of making coding and software development easier and faster. But while these tools can provide some advantages, there are also many potential problems with using them. The most common issues include code that is difficult to read or maintain, bugs that are hard to debug, and security risks due to lack of understanding of the underlying code. Additionally, automated programming tools can’t always take into account all the finer details of a project, so users may find themselves spending more time fixing mistakes than they would if they had coded the project by hand. As such, it is important for developers to understand the potential pitfalls of relying too heavily on automated programming tools before taking on any coding task.

Lack of Quality Control in Automated Tools

Automated tools are becoming increasingly popular for completing tasks that were once done by humans. However, as these tools become more widely used, the need for quality control becomes even greater. Without proper oversight, automated tools can produce results that are inaccurate or unreliable. This can lead to significant problems and mistakes if not addressed quickly and properly. Lack of quality control in automated tools is a major concern as it can lead to serious mistakes with potentially disastrous consequences.

Quality control is important in order to ensure that tasks are completed accurately and efficiently. It involves developing a system of checks and balances to make sure that any automated tool is working properly and producing accurate results. This includes testing the accuracy of the tool itself, as well as assessing the data it produces and ensuring it meets certain standards of reliability. Without quality control measures in place, automated tools may be prone to producing inaccurate or misleading results which could have serious implications on decisions made based on this data.

It is essential for any organization using automated tools to put in place effective quality control processes before deploying them into production environments. Doing so will help prevent any incorrect outcomes from occurring due to faulty or outdated algorithms or data sets being used by the tool. Quality assurance teams should also be established in order to monitor these systems closely and identify any areas where improvement may be needed before going live with an automation solution.

By implementing rigorous standards of quality assurance into their automated solutions, organizations can ensure they are getting reliable output from their AI-driven systems while minimizing potential risks associated with lack of quality control oversight over their AI-driven technology investments.

Impact on Human Programmers

The introduction of ChatGPT has raised fears among many developers that it will make their jobs obsolete. However, Stack Overflow’s ban on ChatGPT proves that this is far from being the case. Human coders are still essential for creating accurate and reliable solutions as well as providing context to complex programming problems. The decision also serves as a reminder that programming is an ever-evolving field in which machines still have a long way to go before they can match human intelligence and expertise.

Overall, Stack Overflow’s ban on ChatGPT is a welcome move that reassures human programmers of their importance in the industry. It also sets an example of how tech companies should responsibly manage AI technology so as not to undermine its potential while preserving the livelihoods of human workers in tech fields.

Potential for Abuse of Automation Tools

Automation tools, like OpenAI ChatGPT, can be incredibly useful when it comes to writing code. However, these AI-generated answers may not always be accurate or relevant, and could potentially cause confusion or mislead users who are looking for reliable solutions. To prevent this from happening, Stack Overflow has taken the precautionary measure of temporarily banning users from sharing answers to coding queries generated by AI chatbot ChatGPT.

OpenAI s natural language processing system can generate syntactically correct code that compiles meaning that it looks correct – but could have subtle bugs in it. As such, allowing these answers to remain on the platform could lead to developers using faulty code and the spread of misinformation, something Stack Overflow does not want to happen.

Stack Overflow is known for providing a reliable source of help for developers and software engineers, so it is understandable why they have chosen to take this step. The platform wants to ensure that users get reliable solutions when they ask questions related to coding on their site. This is why they have decided to temporarily ban AI-generated answers until further notice.

Security Vulnerabilities in Automated Code

The use of automated code in development projects has become increasingly common as AI chatbot technology improves. However, this development also brings with it new security risks that must be addressed. Automated code can contain errors or vulnerabilities that could lead to data breaches, malicious activity, or other forms of exploitation. As a result, it is important for developers to take steps to ensure their code is secure before deploying it into production environments.

One of the primary concerns when using automated code is the potential for introducing security vulnerabilities into the codebase. AI chatbots are often programmed with specific rules and parameters in order to generate code that meets certain criteria, but these rules may be incomplete or not fully understood by developers. As a result, these systems may produce code with unintended consequences that can create serious security risks if deployed without proper testing and verification.

For example, an AI chatbot may generate software code that contains an SQL injection vulnerability due to the lack of input validation when reading user input from databases or web applications. If this vulnerability is exploited by malicious actors, they could gain access to sensitive data or take control of other parts of the system. Similarly, if an AI chatbot produces software code with a cross-site scripting (XSS) vulnerability, attackers could inject malicious scripts into vulnerable web applications and potentially compromise user accounts and data stored on them.

To reduce the risk of security vulnerabilities in automated software development projects, developers should always thoroughly review any generated code before deploying it into production environments and consider implementing additional layers of protection such as static source analysis tools and fuzzing tests to identify potential issues before they become problems. Additionally, developers should also ensure their systems are updated regularly with the latest security patches and harden

Unintended Consequences of Using ChatGPT

Aside from inaccuracy issues, using ChatGPT could also make it difficult for developers to identify the source of their answer and give credit where it is due. Furthermore, it could lead to an erosion of trust in Stack Overflow as users may no longer be able to rely on its community-sourced answers and solutions.

In order to prevent these unintended consequences, Stack Overflow has temporarily banned all answers generated by ChatGPT until further discussion can be had about its use on the platform. This move shows that Stack Overflow is serious about ensuring its users get accurate and reliable coding solutions from its platform and will continue making sure that happens in the future.

Open Source Alternatives to ChatGPT

Open source alternatives to ChatGPT exist that can help programmers and developers answer coding questions quickly and accurately. These alternatives are often more reliable than ChatGPT due to their open source nature, which allows developers to inspect the code and make necessary changes or improvements as needed. Open source chatbots also benefit from a larger community of contributors who can help develop the tool further, while also providing additional support when necessary. Popular open source alternatives to ChatGPT include Botkit, Rasa, Dialogflow, Wit.ai, Microsoft Bot Framework, and Amazon Lex. Each of these chatbot frameworks offer unique features and capabilities that allow developers to create powerful chatbots with ease.

TD/LR

In summary, Stack Overflow has temporarily banned the use of AI-generated answers from ChatGPT. This decision was made due to concerns that users may be misusing the content generated by the bot and getting inaccurate results. The site is withholding a permanent decision on AI-generated answers until after further discussion amongst staff members. However, this ban highlights the risks posed by relying too heavily on automated systems for coding-related queries, as these responses can often lack accuracy and be more tedious than writing code manually.

<p>The post Why Stack Overflow Banned code written by ChatGPT and what this could mean first appeared on Prompt Muse.</p>