🤖📈 How TweetHunter Used NLP to Build a Successful Startup 🚀

#ai #chatgpt #generativeai #twitter

Are you looking to reduce costs and increase customization on AI models? Well, imagine being able to do that for anything, even coffee ☕️. That's the power of LLMs, which offer customization at six times the cost of their foundational models on platforms like OpenAI.

TweetHunter was able to optimize costs as much as possible by combining Generative AI and Customization to dynamically train models each time data or context changed. This resulted in a powerful model with three capabilities: thread generation, hook generation, and tweet writing.

But how did they do it? It all starts with real data from your users. TweetHunter had access to good data for every niche, and their models were created to take that into consideration. The key is to use NLP and Generative AI, with core libraries like Python NLTK | nltk.TweetTokenizer() and TextBlob.

One of the core libraries used by TweetHunter was Flair. Flair has robust feature engineering capabilities that help it learn effective text representations. It can be fine-tuned to target specific tasks such as named entity recognition, and it has strong support for multi-lingual NLP tasks.

But Flair is just one piece of the puzzle. With Flair, you can classify data, but classification is another beast. To truly build a successful AI-powered tool like TweetHunterIO, you need to go beyond classification and use sentient analysis.

Are you ready to learn more about TweetHunter's architecture? Stay tuned for the next sequence with a working POC! Thanks to makers for the inspiring builders on his 8-figure EXIT.