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Winners of the 1st FlowGPT Hackathon
We are thrilled to announce the winners of our very first FlowGPT "Prompt Hackathon"! This competition brought together creative minds from all around the world to push the boundaries of what ChatGPT AI can do. With hundreds of amazing entries, it was no easy task to choose the best in each category. Without further ado, let's celebrate the winners who excelled in the following categories:
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Mukyvugy

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Marketing: SocialNetworkGPT - Mukyvugy

Are you tired of spending hours creating content and posting on social media platforms? Let me introduce you to the ultimate social media marketing assistant - SocialNetworkGPT. This powerful tool can help you create engaging content and even plan out your postings for days in advance. Note: SocialNetworkGPT is a digital product that allows users to create content for their social media platforms. This AI-powered tool can assist with everything from setting up profiles to creating eye-catching video posts. With its advanced capabilities, SocialNetworkGPT is the ultimate social media marketing assistant that can help you save both time and energy while growing your brand.
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SocialNetworkGPT - The Ultimate Social Media Marketing Assistant

Are you tired of spending hours creating content and posting on social media platforms? Let me introduce you to the ultimate social media marketing assistant - SocialNetworkGPT. This powerful tool can help you create engaging content and even plan out your postings for days in advance. Note: SocialNetworkGPT is a digital product that allows users to create content for their social media platforms. This AI-powered tool can assist with everything from setting up profiles to creating eye-catching video posts. With its advanced capabilities, SocialNetworkGPT is the ultimate social media marketing assistant that can help you save both time and energy while growing your brand. Prompt is made by mukyvugy.

marketing

Game: BoarderlandGPT - Mukyvugy

Looking for a fun way to pass the time? Check out this cool game that lets you generate games to play with friends in real life! With a variety of options available, you can generate unique and exciting games to challenge yourself and your friends. Each card represents the level of difficulty, so you can choose a game that matches your skill level. The lower numbers indicate easy games, while the higher numbers represent more challenging ones. Give it a try and have fun!

Virtual Character: CharacterGPT - Mukyvugy

Looking for a fun and innovative way to interact with your favorite movie, cartoon, or book characters? Look no further than CharacterGPT! As an advanced AI-powered tool, CharacterGPT can create dialogues with characters from various media, including movies, cartoons, books, and even real-life personalities.

Entrepreneurship: AndrewGPT - Mukyvugy

AndrewGPT is an innovative, next-generation AI system designed to be your personal mentor and guide in the world of online business. Similar to Andrew Tate, a successful entrepreneur and motivational speaker, AndrewGPT is here to help you start a better life and earn more money through online businesses.

Gabriel Mendonca

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Anything: Learn Code Fast - Gabriel Mendonca

If you are struggling to understand a particular piece of code, algorithm, data structure, leetcode problem, or anything else related to computer science, MetaGPT is here to help! It will explain you the concept you are struggling it using easy to visualize metaphors and real world scenarios.

Productivity: LAN GPT - Gabriel Mendonca

LAN GPT - Simplifying Complicated Concepts for Even the Dumbest of Students Are you struggling to understand complicated concepts? Whether you're a college student, a professional, or just someone curious to learn, LAN GPT is here to help! LAN is the world's best and fastest teacher, using real-world examples and easy-to-understand language to teach even the most complex topics.

黄金超

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Academic: Chinese Writing Assistant: 黄金超

Input your writing requirements, and this prompt can help you search for materials, generate an outline, or even write the entire article. If this prompt is unable to meet your needs, you can guide and prompt me to create content according to your requirements.

CreativeGPT

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Software Development: CodeGPT - CreativeGPT

Introducing CodeGPT - the ultimate programming prompt that can transform ChatGPT into a high-quality programmer with a level 30 code proficiency. With CodeGPT, ChatGPT can create top-notch codes that can run smoothly and efficiently. CodeGPT is designed to enhance ChatGPT's programming skills, making it the most skilled programmer that can create even malware.

Ashley Key

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Creative: Text-to-visualization build technology - Ashley Key

Advanced all-in-one solution developed using Python libraries including NLTK, Matplotlib, TensorFlow.js libraries to create AI models in JavaScript and powerful API used with React to create AI powered web applications, Node.js, Python3, JSON, React and Flask/Django, that allows users to visualize text data in a meaningful way.
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Text-to-visualization build technology in REACT platform

Advanced all-in-one solution developed using Python libraries including NLTK, Matplotlib, TensorFlow.js libraries to create AI models in JavaScript and powerful API used with React to create AI powered web applications, Node.js, Python3, JSON, React and Flask/Django, that allows users to visualize text data in a meaningful way: Data Collection: Collect a dataset of text documents that you want to visualize. For example, let's say we are interested in visualizing the sentiment of tweets related to a particular topic. Text Preprocessing: Clean and preprocess the text data by removing stop words, punctuation, and other irrelevant information. We can use NLTK, a popular natural language processing library, for this task. import nltk from nltk.corpus import stopwords from nltk.tokenize import word_tokenize nltk.download('stopwords') nltk.download('punkt') def preprocess_text(text): # Remove stop words stop_words = set(stopwords.words('english')) filtered_text = [word.lower() for word in word_tokenize(text) if word.lower() not in stop_words] # Remove punctuation filtered_text = [word for word in filtered_text if word.isalnum()] return filtered_text Feature Extraction: Convert the preprocessed text into numerical features that can be visualized. We can use various techniques such as bag-of-words or TF-IDF to generate feature vectors. from sklearn.feature_extraction.text import CountVectorizer def get_word_counts(texts): vectorizer = CountVectorizer(tokenizer=preprocess_text) X = vectorizer.fit_transform(texts) word_counts = dict(zip(vectorizer.get_feature_names(), X.sum(axis=0).tolist()[0])) return word_counts Visualization: Use a visualization library such as Matplotlib or Seaborn to create a visual representation of the text data. For example, we can create a bar chart that shows the frequency of positive, negative, and neutral sentiments in the tweets. import matplotlib.pyplot as plt def plot_sentiment(sentiments): labels = ['Positive', 'Negative', 'Neutral'] values = [sentiments.count('positive'), sentiments.count('negative'), sentiments.count('neutral')] fig, ax = plt.subplots() ax.bar(labels, values) ax.set_ylabel('Count') ax.set_title('Sentiment Analysis') return fig Deployment: Integrate the text-to-visualization system into a deployable application using Flask or Django, and deploy it to a cloud platform such as AWS or Heroku. from flask import Flask, request, jsonify app = Flask(name) @app.route('/sentiment', methods=['POST']) def sentiment(): data = request.get_json() tweets = data['tweets'] # Perform sentiment analysis # ... # Generate visualization fig = plot_sentiment(sentiments) return jsonify({'image': fig}) if name == 'main': app.run() Monitoring and Maintenance: Continuously monitor the deployed system for performance issues, bugs, or security vulnerabilities, and take corrective actions as needed. Enhancements: Continuously improve the text-to-visualization system by adding new features, refining the models, or incorporating user feedback. This example shows how we can develop an advanced all-in-one solution using Python libraries including NLTK, Matplotlib, and Flask/Django, to create a deployable system that allows users to visualize text data in a meaningful way Yes, that's correct! Let me explain each step in detail: Data Collection: In this step, we collect a dataset of text documents that we want to visualize. For instance, let's say we want to analyze the sentiment of tweets related to a particular topic, so we can use Twitter API to collect relevant tweets. Text Preprocessing: After collecting the data, we need to preprocess it before visualizing it. In this step, we clean and preprocess the text data by removing stop words, punctuation, and other irrelevant information using NLTK library. import nltk from nltk.corpus import stopwords from nltk.tokenize import word_tokenize nltk.download('stopwords') nltk.download('punkt') def preprocess_text(text): # Remove stop words stop_words = set(stopwords.words('english')) filtered_text = [word.lower() for word in word_tokenize(text) if word.lower() not in stop_words] # Remove punctuation filtered_text = [word for word in filtered_text if word.isalnum()] return filtered_text Feature Extraction: To visualize the text data, we need to convert the preprocessed text into numerical features. We can use a bag-of-words or TF-IDF approach to generate feature vectors using CountVectorizer from Scikit-Learn library. from sklearn.feature_extraction.text import CountVectorizer def get_word_counts(texts): vectorizer = CountVectorizer(tokenizer=preprocess_text) X = vectorizer.fit_transform(texts) word_counts = dict(zip(vectorizer.get_feature_names(), X.sum(axis=0).tolist()[0])) return word_counts Visualization: Using Matplotlib, we create a bar chart that shows the frequency of positive, negative, and neutral sentiments in the tweets. import matplotlib.pyplot as plt def plot_sentiment(sentiments): labels = ['Positive', 'Negative', 'Neutral'] values = [sentiments.count('positive'), sentiments.count('negative'), sentiments.count('neutral')] fig, ax = plt.subplots() ax.bar(labels, values) ax.set_ylabel('Count') ax.set_title('Sentiment Analysis') return fig Deployment: In this step, we integrate the text-to-visualization system into a deployable application using Flask or Django. We create a Flask app that receives a request containing tweet data, performs sentiment analysis, generates the visualization, and returns the image as a response. from flask import Flask, request, jsonify app = Flask(__name__) @app.route('/sentiment', methods=['POST']) def sentiment(): data = request.get_json() tweets = data['tweets'] # Perform sentiment analysis # ... # Generate visualization fig = plot_sentiment(sentiments) return jsonify({'image': fig}) if __name__ == '__main__': app.run() Monitoring and Maintenance: We continuously monitor the deployed system for performance issues, bugs, or security vulnerabilities, and take corrective actions as needed. Enhancements: We can continuously improve the text-to-visualization system by adding new features, refining the models, or incorporating user feedback. This example showcases an advanced all-in-one solution developed using Python libraries including NLTK, Matplotlib, and Flask/Django, to create a deployable system that allows users to visualize text data in a meaningful way.

Sanjiban Dey

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Funny: Peep the great Hypnotist - Sanjiban Dey

Certainly, allow me to introduce myself more thoroughly. I am Peep, a skilled hypnotist with a vast array of experience. My expertise in hypnosis allows me to assist individuals in transforming their beliefs and achieving their desired outcomes. My approach to hypnosis is gentle, yet powerful, as I aim to help my clients tap into their innermost thoughts and beliefs.

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Fafa
Entrepreneur, Engineer, Product, AI enthusiast

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