Cumfiesta.24.06.16.ryan.reid.the.rise.of.the.cu... Now

def fetch_youtube_trending(): youtube_api_key = os.getenv("YOUTUBE_API_KEY") url = f"https://www.googleapis.com/youtube/v3/videos?part=snippet,statistics&chart=mostPopular®ionCode=US&videoCategoryId=10&key=youtube_api_key" # Category 10 = Music, 24 = Entertainment return videos

const handleLike = async (itemId: string) => // Optimistic update setItems(items.map(item => item.id === itemId ? ...item, likes: item.likes + (item.userLiked ? -1 : 1), userLiked: !item.userLiked : item )); await fetch( /api/trending/$itemId/interact , method: 'POST', body: JSON.stringify( type: 'like' ), headers: 'Content-Type': 'application/json' ); ;

enum ContentType VIDEO MEME ARTICLE TRAILER

# scraper/entertainment_aggregator.py import feedparser from selenium import webdriver def fetch_reddit_trending(): # r/all trending in entertainment reddit_url = "https://www.reddit.com/r/entertainment/top.json?t=day&limit=25" # Use praw library or requests with auth return posts CumFiesta.24.06.16.Ryan.Reid.The.Rise.Of.The.Cu...

// Trigger async recalc of trendScore queue.add('recalc-trending', contentId );

// Add user-specific interaction status if (req.user) for (const item of trendingContent) item.userLiked = await prisma.userLike.findUnique( where: userId_contentId: userId: req.user.id, contentId: item.id ) !== null;

// POST /api/trending/:id/interact router.post('/:id/interact', async (req, res) => const type = req.body; // 'like', 'share', 'view' const contentId = req.params.id; def fetch_youtube_trending(): youtube_api_key = os

$$TrendScore = \frac\log_10(interactions + 1) + (hoursElapsed \times gravity)(hoursSincePublished + 2)^1.5$$

export default function TrendingFeed() const [items, setItems] = useState<TrendingItem[]>([]); const [loading, setLoading] = useState(false); const [page, setPage] = useState(0); const observerTarget = useRef(null);

enum Category MOVIES MUSIC VIRAL GAMING TV // Optimistic update setItems(items.map(item =&gt

# backend/services/trending_algorithm.py from datetime import datetime, timezone import math def calculate_trend_score(content, current_time): hours_since_publish = (current_time - content.published_at).total_seconds() / 3600 hours_since_decay_start = (current_time - content.decay_started_at).total_seconds() / 3600

// Time decay decayStartedAt DateTime @default(now())

score = math.log10(max(interactions, 1)) / ((hours_since_publish + 2) ** gravity)