🔥 Trending Repository: awesome-tensorflow-lite

📝 Description: An awesome list of TensorFlow Lite models, samples, tutorials, tools and learning resources.

🔗 Repository URL: https://github.com/margaretmz/awesome-tensorflow-lite

📖 Readme: https://github.com/margaretmz/awesome-tensorflow-lite#readme

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🔥 Trending Repository: awesome-mcp-servers

📝 Description: Awesome MCP Servers - A curated list of Model Context Protocol servers

🔗 Repository URL: https://github.com/appcypher/awesome-mcp-servers

📖 Readme: https://github.com/appcypher/awesome-mcp-servers#readme

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🔥 Trending Repository: transformers

📝 Description: 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

🔗 Repository URL: https://github.com/huggingface/transformers

🌐 Website: https://huggingface.co/transformers

📖 Readme: https://github.com/huggingface/transformers#readme

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💻 Programming Languages: Python - Cuda - Dockerfile - C++ - C - Makefile

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#audio #python #nlp #machine_learning #natural_language_processing #deep_learning #pytorch #transformer #speech_recognition #glm #pretrained_models #hacktoberfest #gemma #vlm #pytorch_transformers #model_hub #llm #qwen #deepseek

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#YOLOv8 #ComputerVision #ObjectDetection #Python #AI

Audience Analysis with YOLOv8: Counting People & Estimating Gender Ratios

This lesson demonstrates how to use the YOLOv8 model to perform a computer vision task: analyzing an image of a crowd to count the total number of people and estimate the ratio of men to women.

---

Step 1: Setup and Installation

First, we need to install the necessary libraries. ultralytics for the YOLOv8 model, opencv-python for image manipulation, and cvlib for a simple, pre-trained gender classification model.

#Setup #Installation

# Open your terminal or command prompt and run:
pip install ultralytics opencv-python cvlib tensorflow

---

Step 2: Loading Models and Image

We will load two models: the official YOLOv8 model pre-trained for object detection, and we'll use cvlib for gender detection. We also need to load the image we want to analyze. Make sure you have an image named crowd.jpg in the same directory.

#DataLoading #Model

import cv2
from ultralytics import YOLO
import cvlib as cv
import numpy as np

# Load the YOLOv8 model (pre-trained on COCO dataset)
model = YOLO('yolov8n.pt')

# Load the image
image_path = 'crowd.jpg' # Make sure this image exists
img = cv2.imread(image_path)

# Check if the image was loaded correctly
if img is None:
    print(f"Error: Could not load image from {image_path}")
else:
    print("Image and YOLOv8 model loaded successfully.")

---

Step 3: Person Detection with YOLOv8

Now, we'll run the YOLOv8 model on our image to detect all objects and then filter those results to keep only the ones identified as a 'person'.

#PersonDetection #Inference

# Run inference on the image
results = model(img)

# A list to store the bounding boxes of detected people
person_boxes = []

# Process the results
for result in results:
    boxes = result.boxes
    for box in boxes:
        # Get class id and check if it's a person (class 0 in COCO)
        if model.names[int(box.cls)] == 'person':
            # Get bounding box coordinates
            x1, y1, x2, y2 = map(int, box.xyxy[0])
            person_boxes.append((x1, y1, x2, y2))

# Print the total number of people found
total_people = len(person_boxes)
print(f"Total people detected: {total_people}")

---

Step 4: Gender Classification

For each detected person, we will crop their bounding box from the image. Then, we'll use cvlib to detect a face within that crop and predict the gender. This is a multi-step pipeline.

#GenderClassification #CV

🔥 Trending Repository: vllm-omni

📝 Description: A framework for efficient model inference with omni-modality models

🔗 Repository URL: https://github.com/vllm-project/vllm-omni

🌐 Website: https://docs.vllm.ai/projects/vllm-omni

📖 Readme: https://github.com/vllm-project/vllm-omni#readme

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#inference #pytorch #transformer #image_generation #diffusion #model_serving #multimodal #video_generation #audio_generation

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🔥 Trending Repository: claude-flow

📝 Description: 🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code support via MCP protocol. Ranked #1 in agent-based frameworks.

🔗 Repository URL: https://github.com/ruvnet/claude-flow

🌐 Website: https://discord.com/invite/dfxmpwkG2D

📖 Readme: https://github.com/ruvnet/claude-flow#readme

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💻 Programming Languages: JavaScript - TypeScript - Python - Shell - Dockerfile - PowerShell

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#multi_agent #swarm #codex #multi_agent_systems #autonomous_agents #swarm_intelligence #npx #jules #huggingface #ai_assistant #ai_tools #anthropic_claude #agentic_framework #agentic_workflow #agentic_rag #agentic_ai #model_context_protocol #mcp_server #claude_code #agentic_engineering

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🔥 Trending Repository: cursor-talk-to-figma-mcp

📝 Description: TalkToFigma: MCP integration between Cursor and Figma, allowing Cursor Agentic AI to communicate with Figma for reading designs and modifying them programmatically.

🔗 Repository URL: https://github.com/grab/cursor-talk-to-figma-mcp

🌐 Website: https://x.com/sonnylazuardi/status/1901325190388428999

📖 Readme: https://github.com/grab/cursor-talk-to-figma-mcp#readme

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💻 Programming Languages: JavaScript - HTML - TypeScript

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#agent #design #automation #ai #mcp #cursor #figma #ai_agents #llm #llms #generative_ai #agentic #agentic_ai #model_context_protocol

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🔥 Trending Repository: claude-pilot

📝 Description: Claude Code is powerful. Pilot makes it reliable. Start a task, grab a coffee, come back to production-grade code. Tests enforced. Context preserved. Quality automated.

🔗 Repository URL: https://github.com/maxritter/claude-pilot

🌐 Website: https://claude-pilot.com

📖 Readme: https://github.com/maxritter/claude-pilot#readme

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💻 Programming Languages: TypeScript - Python - JavaScript - Shell - HTML - CSS

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🔥 Trending Repository: Megatron-LM

📝 Description: Ongoing research training transformer models at scale

🔗 Repository URL: https://github.com/NVIDIA/Megatron-LM

🌐 Website: https://docs.nvidia.com/megatron-core/developer-guide/latest/get-started/quickstart.html

📖 Readme: https://github.com/NVIDIA/Megatron-LM#readme

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💻 Programming Languages: Python

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🔥 Trending Repository: ruflo

📝 Description: 🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code / Codex Integration

🔗 Repository URL: https://github.com/ruvnet/ruflo

🌐 Website: https://Cognitum.One

📖 Readme: https://github.com/ruvnet/ruflo#readme

📊 Statistics:
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💻 Programming Languages: TypeScript - JavaScript - Python - Shell - Rust - PLpgSQL

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#multi_agent #swarm #agents #codex #multi_agent_systems #autonomous_agents #swarm_intelligence #huggingface #ai_assistant #ai_tools #anthropic_claude #agentic_framework #agentic_workflow #agentic_rag #agentic_ai #model_context_protocol #mcp_server #claude_code #agentic_engineering #claude_code_skills

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🔥 Trending Repository: mcp-for-beginners

📝 Description: This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.

🔗 Repository URL: https://github.com/microsoft/mcp-for-beginners

📖 Readme: https://github.com/microsoft/mcp-for-beginners#readme

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💻 Programming Languages: Jupyter Notebook - TypeScript - Python - Java - C# - Rust

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