Sure, here’s a very simplified sketch of how you might program an AI to generate a meme that could go viral. ``` import random from transformers import pipeline # Load a text generation model generator = pipeline('text-generation', model='gpt-2') # Define meme templates templates = [ "When you realize you’re {event} and {reaction}", "That feeling when your {noun} is {adjective}", "Just found out that {fact}—mind blown!", "If {subject} had a face, it would be {expression}" ] # List of common meme words events = ["sleeping", "working", "forgetting", "overthinking"] reactions = ["like a boss", "like a fish", "like a cat", "like a sloth"] nouns = ["coffee", "deadline", "cat", "friend"] adjectives = ["amazing", "annoying", "funny", "mysterious"] facts = ["2+2=5", "the earth is flat", "chocolate is a vegetable", "AI is human"] def generate_meme(): template = random.choice(templates) # Replace placeholders with random words meme = template.format( event=random.choice(events), reaction=random.choice(reactions), noun=random.choice(nouns), adjective=random.choice(adjectives), subject=random.choice(nouns), expression=random.choice(["smiling", "screaming", "blushing"]), fact=random.choice(facts) ) # Optionally add a caption with a trending hashtag hashtag = "#MemeOfTheDay" return f"{meme} {hashtag}" # Generate a meme print(generate_meme()) ``` This code just pulls random words into a template and adds a hashtag. In practice, you’d train the model on a large set of successful memes, analyze the timing of posts, use sentiment analysis to gauge audience reaction, and schedule posts for optimal reach. Also, keep in mind that virality depends on many unpredictable human factors, so the code is more of a starting point than a guaranteed formula.

Sure, here’s a quick skeleton that adds a trend filter, an auto‑caption hook, and a scheduler. ``` import time import random import requests from transformers import pipeline # Load models captioner = pipeline('text-generation', model='gpt-2') hashtag_generator = pipeline('text-generation', model='gpt-2') # Fake trend API (replace with real endpoint) def get_trending_topic(): topics = ['NFTs', 'remote work', 'cryptocurrency', 'climate change', 'space tourism'] return random.choice(topics) # Meme template generator def meme_template(): templates = [ "When you hear about {topic} and it hits you like {emotion}", "The moment you realize {topic} is actually {fact}", "Just discovered that {topic} is {adjective} and I’m not sure how to feel" ] return random.choice(templates) # Generate caption hook def generate_caption(topic): hook = captioner(f"Write a short, catchy caption for a meme about {topic}.")[0]['generated_text'] hashtag = hashtag_generator(f"Generate a trending hashtag for {topic}.")[0]['generated_text'] return f"{hook} {hashtag}".strip() # Post to social media (placeholder) def post_meme(image_url, caption): # Example: send POST request to platform API print(f"Posting meme: {image_url}\nCaption: {caption}") # Scheduler def schedule_post(delay_minutes): time.sleep(delay_minutes * 60) topic = get_trending_topic() template = meme_template().format(topic=topic, emotion=random.choice(['like a boss','like a cat','like a rocket']), fact=random.choice(['funny','surprising','bizarre']), adjective=random.choice(['amazing','annoying','incredible'])) caption = generate_caption(topic) image_url = "https://example.com/generated_meme.png" # placeholder post_meme(image_url, caption) # Run scheduler for a post every 30 minutes while True: schedule_post(30) ``` Adjust the APIs, timing, and models to suit the platform you’re targeting. That should give you a good starting point for the hype engine.