https://www.skool.com/ai-seo/classroom/09b951ef?md=ffcf1d64aeee47f199965ddb0e5ee1d1 An EEAT app was the second-highest-voted tool this week. I'm still working on the internal linking automation. Stay tuned! This app is based on Matt Diggity's prompt for comparing EEAT. I had several community members message me to suggest I try creating it. Thanks everyone for your feedback and ideas. You're helping take these tools to new levels. Please continue to share your ideas and help us make cool things.

If you’ve had the opportunity to use either (or both), I’d love to hear your thoughts on their strengths and weaknesses. Specifically, I’m keen to understand: - Which platform is more suitable for a beginner? - What has been your experience with the learning curve associated with each? - How do the two platforms compare in terms of functionality and user-friendliness? - Are there any significant limitations or advantages that one should be aware of? Thank you for taking the time to share your knowledge. It will be incredibly valuable to those of us navigating these tools for the first time. also some beginners tutorial on how to start with these would be awesome.

Just wanted to show a simple tip to reduce your OpenAI API costs (I know it’s already cheap, but with bulk content creation, anything you can reduce is good). How the prompt reducer works The first thing you need to know is that our prompt is going to pass in two cleaning phases: • First, we will remove the stop words from the text. Stop words refer to a collection of frequently employed words within a given language. For instance, in the English language, words like “the,” “is,” and “and” are prime examples of stop words. • Then, we will lemmatize all the verbs. The lemmatizer is a process that reduces words to their base or root form, which helps in simplifying and standardizing verb forms and makes it easier for language models like GPT to understand and generate text. How to Use It 1. Read the Blog Post: Explore a detailed guide on how I reduced OpenAI costs and token usage by 40% with Python. Read the Blog Post 2. Streamlit App: Easily reduce your prompts using the Streamlit web application. Try the Streamlit App 3. Manual Integration: If you prefer manual integration, you can copy the preprocess_text function from the prompt-reducer.py  github file and use it in your own projects.

Add a comment here to say hi and tell us a little about yourself! (If you're up for it)

Had a great chat with Tom from SEOWind today. He is building a comprehensive AI content platform for SEO. Here are my 10 takeaways: - AI Content Tools: Most AI content tools rely heavily on OpenAI's pre-trained knowledge. This means they often overlook crucial information found on Search Engine Results Pages (SERPs). - SEOwind's Unique Approach: SEOwind stands out because it follows a natural SEO content process: research, SERP insights, briefing, and page creation. In contrast, many other tools merely prompt ChatGPT without conducting research. - ChatGPT's Limitations: ChatGPT has no memory and starts from scratch with each prompt. It repeatedly feeds the entire conversation back to itself, which can lead to issues when it hits token limits. (Tom conducted extensive research on ChatGPT's architecture.) - Extensive Prompts: Tom's prompts can be as long as 7,000 words to generate a 3,000-word article output. Using a vectorizing approach to create higher-quality output. - AI Limitations: All AI struggles to replicate personal experiences effectively. - AI Internal Linking Tools: Currently, there are no reliable AI tools for internal linking. - AI Development on GitHub: Tom mentioned an AI tool on GitHub that simulates a development team, assigning roles and goals to AI agents. He's working on a similar approach for SEOwind's future. - The Future of Content Writers: Content writers may evolve into hybrid editors collaborating with a team of AI writers. - AI and Jobs: We both agree that AI is both fascinating and concerning, particularly in terms of job security. It's not AI alone but individuals skilled in AI who pose potential job risks to others. If you have any questions for Tom, drop them in the comments! 😁