Unique not commodity The price of certain things is their scarcity. Perceived value. By selling cheaply, at a price in line with competitors, or by being average. Content A simple content strategy can make all the difference. The most marketed is the most viewed. There's no such thing as bad marketing. But some marketing speaks more to customers than others. Knowledge The more you know about your market and your target, the better you market. Branding It's about associating yourself with your company. The better known the brand, the better known the person. Comment "MARKET" to get the entire roadmap

Just dropped a quick guide on fully white-labeling your VectorShift chatbot using ChatDash. This tutorial covers how to set up a branded client dashboard, customize your bot’s look and feel, and provide direct knowledge base access for clients – all in just a few steps. 🔹 What You’ll Learn: • Setting up your agency’s logo, domain, and color scheme • Adding your VectorShift chatbot with API integration • Enabling client access to manage the knowledge base • Deploying a fully branded chatbot experience Watch the tutorial here: [Link to Video] If you’re looking for a way to enhance client experiences and keep everything on-brand without the usual high costs, check it out. Let us know if you have any questions or feedback!

Hope everyone is having a productive day. Any way I can connect GoHighLevel with flowise? I'm new to both these platforms hence I'm struggling a bit. I plan on using the Inbound Webhook on GHL to to catch the incoming data of a conversational agent on Flowise. Can't find anything like this on yt so if anyone can help me out with this or give any ideas, this will be very helpful for me. Thanks

I’m thrilled to share my journey in locally fine-tuning the GPT-4O Mini model. For those venturing into AI agent development, here’s a concise breakdown of my process: 1️⃣ Setup: - Installed essential packages: 'openai', 'python-dotenv' - Configured OpenAI API key securely via a '.env' file 2️⃣ Data Preparation: - Curated a JSONL file with 10+ training examples - Each example included: a system message, user input, and the ideal assistant response - For optimal results, aim for 50+ diverse examples 3️⃣ Code Structure: - `config.py`: Handles API keys, model names, and file paths - `tools.py`: Manages data preparation and validation - `app.py`: Executes the fine-tuning process 4️⃣ Fine-Tuning: - Uploaded the training file to OpenAI - Launched the fine-tuning job (completed in ~10-15 minutes for small datasets) - Saved fine-tuned model details post-completion 5️⃣ Testing: - Developed a script to evaluate the fine-tuned model with new prompts Key Takeaways: - Start with a smaller dataset, then scale - The quality of training data is crucial for success - Fine-tuning is iterative, be ready to refine and adjust Next Steps: - Expanding the dataset to enhance model performance - Exploring new use cases and potential specializations I’d love to hear from others who’ve experimented with fine-tuning models like GPT-4O Mini. Your insights and experiences could be invaluable as we push the boundaries of what’s possible in AI development. 🔗 [Code Link](https://drive.google.com/drive/folders/1CqLIyrPOyVJxAlErsYhn6QvQssTayXVW?usp=sharing) 🔗 [OpenAI Documentation] (https://platform.openai.com/docs/guides/fine-tuning) To get more AI development updates, follow me, @Tariq B.. If you found these insights helpful, don’t forget to like and share this post to spread the knowledge among fellow AI developers.

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