Ian Simmons launched Kicking the Seat in 2009, one week after seeing Nora Ephron’s Julie & Julia. His wife proposed blogging as a healthier outlet for his anger than red-faced, twenty-minute tirades (Ian is no longer allowed to drive home from the movies).
The Kicking the Seat Podcast followed three years later and, despite its “undiscovered gem” status, Ian thoroughly enjoys hosting film critic discussions, creating themed shows, and interviewing such luminaries as Gaspar Noé, Rachel Brosnahan, Amy Seimetz, and Richard Dreyfuss.
Ian is a member of the Chicago Film Critics Association. He also has a family, a day job, and conflicted feelings about referring to himself in the third person.
For ethical use, only solve GeeTest on sites you own or have permission to test. If you need code-level details for a specific version or a bypass for a particular anti-bot measure, let me know.
1. Introduction: What is GeeTest? GeeTest is a advanced behavioral CAPTCHA system developed by GeeTest (武汉极意网络科技有限公司). Unlike traditional text-based CAPTCHAs or Google’s reCAPTCHA (which mainly relies on risk analysis), GeeTest is famous for its drag-to-fill-the-gap puzzle and click-the-correct-sequence challenges. geetest solving
: Add realistic mouse movement curve (e.g., using bezier or pointerMotion in Puppeteer Extra). 4.2 Approach 2: Reverse Engineering the Payload GeeTest’s server checks not just the final X, but also the encrypted aa containing the entire trajectory. If you just teleport to target_x, you will fail. For ethical use, only solve GeeTest on sites
Then simulate drag from (0,0) to (target_x, 0) with random y-jitter. Introduction: What is GeeTest
It is used by major sites like Steam, Binance, Huawei, and thousands of others. | Version | Characteristics | |---------|------------------| | GeeTest v3 | Classic puzzle slider. Drag a piece into a missing area of an image. | | GeeTest v4 | More complex. Click images in a specific order, or slide puzzle with dynamic backgrounds. | | GeeTest Adaptive | AI-based risk scoring. No challenge if user is trusted. |
import cv2 import numpy as np bg = cv2.imread('bg.jpg') slider = cv2.imread('slider.jpg') result = cv2.matchTemplate(bg, slider, cv2.TM_CCOEFF_NORMED) _, _, _, max_loc = cv2.minMaxLoc(result) target_x = max_loc[0]