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Escape the Algorithm and Save Time: Build Your Own Feed with AI Automation

I have a problem with my social media feed: there are some people whose content I find really valuable, but on the other hand, there is too much trivial content, so I waste too much time watching low-value videos... BAD DEAL!!! So, let’s solve this issue using AI automation. We can solve this issue by using Telegram as our own social media platform and using automation to make our own feed, far from the algorithms of social media platforms. Choose the people whose content you find really valuable. Use AI automation to monitor them whenever they publish a new video. Once a new video is published, AI sends it to a Telegram bot where you can download it. The person who gave me this idea is Abo Ziad (the Egyptian version of Ali Abdaal), and he is one of my favorite YouTubers. Personally, I think you can add some entertaining channels to make it more balanced. I’ll try this solution for one month, and if you’re interested, I’ll share my experience with you, whether it succeeds or fails. In the end, it’s up to you. Tell us in the comments if you found the general idea useful or not, and whether AI automation is the best solution for this issue.

2026-08-17 原文 →
AI 资讯

Stop Slouching! Build a Real-Time Spine Posture Monitor using MediaPipe and Python

We’ve all been there: hunched over a keyboard at 3 AM, neck craned forward like a turtle, debugging a race condition. "Tech neck" isn't just a meme; it’s a productivity killer. As developers, our spine is our most underrated hardware. In this tutorial, we are going to build a Real-Time Spine Posture Monitor . We will leverage real-time human pose estimation and MediaPipe Python libraries to track your posture via your webcam. By the end of this guide, you'll have a system that detects when you're slouching and sends a system notification to keep your ergonomics in check. This project is perfect for those looking into OpenCV computer vision and developer ergonomics solutions. The Architecture 🏗️ The logic is straightforward: we capture video frames, process them through a pre-trained neural network to find body landmarks, and apply some basic geometry to determine if your posture is healthy. graph TD A[Webcam Feed] --> B[OpenCV Frame Processing] B --> C[MediaPipe Pose Landmark Detection] C --> D{Extract Shoulder & Ear Coordinates} D --> E[Calculate Neck Inclination Angle] E --> F{Angle > Threshold?} F -- Yes --> G[Trigger System Notification] F -- No --> H[Continue Monitoring] G --> B H --> B Prerequisites 🛠️ Before we dive into the code, ensure you have the following installed: Python 3.9+ MediaPipe : Google’s framework for cross-platform ML. OpenCV : For video stream handling. PyObjC : (For macOS) to trigger native system alerts. pip install mediapipe opencv-python pyobjc Step 1: Initialize the Pose Engine MediaPipe makes pose estimation incredibly easy. We’ll use the Pose solution, which provides 33 3D landmarks for the human body. import cv2 import mediapipe as mp import math # Initialize MediaPipe Pose mp_pose = mp . solutions . pose pose = mp_pose . Pose ( static_image_mode = False , model_complexity = 1 , enable_segmentation = False , min_detection_confidence = 0.5 ) mp_drawing = mp . solutions . drawing_utils Step 2: Calculating the "Slouch" Angle 📐 To detect

2026-08-10 原文 →