EasyClick AI Programming in Practice: Simulating Human-Like Swipe Trajectories with Bézier Curves

Introduction: When AI Meets Automation Script Development

In the field of automation script development, making programmatic actions look like real human behavior has always been a core concern for developers. Mechanical straight-line swipes and fixed-interval taps are easily flagged by detection systems as non-human operations. Bézier curves are the perfect solution to this pain point — they can generate natural, smooth, and randomized motion trajectories that precisely simulate the feel of a real finger swipe.

This article is based on a practical EasyClick AI fully-automated programming tutorial shared by a Bilibili creator. It provides a detailed breakdown of how to use AI-powered conversations to generate Bézier curve parameters and achieve high-quality human-like swipe simulation.

EasyClick Hot Update System: From Open Source to Custom Development

The Original System's Complete Architecture

The original EasyClick hot update system features a Chinese ink-wash art design style with a dual-mode, three-module closed-loop architecture:

• Upload & Publish: Supports remote hot updates for script code
• Log Monitoring: Real-time tracking of script execution status
• Version Management: Full-lifecycle version control coverage

Hot Update technology is particularly valuable in the mobile automation scripting space. Its technical implementation typically involves three core components: delta updates (only delivering changed portions), version verification (MD5/SHA hash comparison), and rollback mechanisms (automatic recovery on update failure). EasyClick, as an automation framework based on Android Accessibility Services, features a hot update system that allows developers to host JavaScript scripts in the cloud, with devices periodically pulling the latest version for execution — achieving a "deploy once, iterate continuously" operational model. This means developers can remotely fix bugs and adjust strategy parameters without requiring any manual action from users.

The original system also invested in interaction details, such as a flowing light effect on mouse hover, reflecting product-grade interaction quality. While this three-page design is functionally complete, the page-switching overhead is somewhat high for efficiency-focused developers.

A Student's Custom Fork

The video showcases a student's custom development based on the open-source system — simplifying the original three-page architecture into a single-page dark-themed interface. The design philosophy is clear: open it and use it immediately, simple and direct, eliminating unnecessary interaction steps.

This case itself demonstrates the value of open-source ecosystems: when the core framework is robust enough, community developers can quickly customize differentiated product forms based on their own needs.

Core Technology: AI-Generated Bézier Curve Parameters for Human-Like Swipe Simulation

The Mathematical Essence of Bézier Curves

Bézier curves were proposed by French engineer Pierre Bézier in 1962 while working at Renault, originally for automotive body surface design. Their mathematical essence is parametric curves based on Bernstein Polynomials, with curve shapes defined by a set of control points. Taking the most commonly used cubic Bézier curve as an example, the formula is:

B(t) = (1-t)³P₀ + 3(1-t)²tP₁ + 3(1-t)t²P₂ + t³P₃

Where t∈[0,1] is the parameter, and P₀ through P₃ are the four control points. The curve always passes through the start point P₀ and end point P₃, while P₁ and P₂ act as "magnets" pulling the curve's bending direction. The cubic-bezier() function in CSS animations, Adobe Illustrator's Pen tool, and TrueType outlines in font rendering are all typical applications of Bézier curves in different domains.

Why Choose Bézier Curves for Swipe Trajectory Simulation?

Bézier curves have natural advantages in simulating human finger swipes:

1. Non-linear motion: Real finger swipes are not constant-speed straight lines — they involve acceleration and deceleration
2. Trajectory randomness: Human swipe paths are never perfectly straight; there are always subtle deviations
3. Controllable parameters: By adjusting control points, you can precisely control the degree of curvature and motion rhythm
4. Mature mathematical foundation: Bézier curves have well-established mathematical formulas with high computational efficiency

The Complete AI-Assisted Parameter Generation Workflow

Traditionally, developers needed to manually tune Bézier curve control point coordinates — a process that was both tedious and experience-dependent. With AI-powered conversations, the entire workflow is dramatically simplified:

1. Describe the requirement: Tell the AI the swipe's start point, end point, and desired motion characteristics (e.g., fast start with slow stop, S-shaped trajectory, etc.)
2. AI generates parameters: The AI automatically calculates appropriate Bézier curve control points based on the description
3. Real-time debugging: Plug the parameters into the script, run it, and observe the actual trajectory
4. Iterative optimization: Continue the AI conversation to fine-tune parameters based on results

This human-AI collaborative development model allows even zero-experience developers to get started quickly — producing high-quality swipe trajectories without needing to deeply understand the mathematics behind Bézier curves.

Practical Application Scenarios for Bézier Curve Swipes

UI Motion Design

Bézier curves aren't just for simulating swipe operations — they're equally powerful in UI animation:

• Page transition animations: Elements can enter and exit along Bézier curve paths for more natural visuals
• Hover light effects: Like the mouse glow effect in EasyClick's original version, which is essentially a curve-based motion path
• Elastic bounce-back: The bounce effect when scrolling to list boundaries can be achieved by adjusting curve parameters

Anti-Detection Strategies for Automation Scripts

In automated testing and RPA (Robotic Process Automation) scenarios, the core value of Bézier curve swipes lies in:

• Each swipe trajectory is slightly different, increasing randomness
• Speed curves conform to ergonomic characteristics
• Can simulate finger pressure variations (combined with pressure parameters)

The RPA market is projected to exceed $13 billion by 2025. However, as automation tools proliferate, platform anti-automation detection technologies are also rapidly evolving. Mainstream detection methods include: trajectory analysis (checking whether swipe paths are perfect straight lines or fixed curves), timing analysis (whether operation intervals show mechanical equal spacing), device fingerprinting (detecting virtual environments), and behavioral modeling (using machine learning to establish normal user behavior baselines). Among these, trajectory analysis is the most fundamental and effective detection dimension, because human fingers — constrained by muscle control physiology — inevitably produce non-linear, non-constant-speed trajectories with micro-tremors. This is precisely the core simulation target of Bézier curves.

Key Technical Points for Bézier Curve Swipes

For developers looking to implement this technology, the following points are worth noting:

• Number of control points: Cubic Bézier curves (4 control points) are typically sufficient for most swipe scenarios
• Time distribution: Uniformly sampling points on the curve leads to uneven speed — easing functions are needed
• Noise injection: Overlaying small random offsets on the curve trajectory further enhances realism
• AI prompt design: The more specific the description, the more accurate the AI-generated parameters

The Critical Role of Easing Functions

Easing functions map linear time to non-linear progress, determining whether an animation "feels" natural. Robert Penner's classic easing function set (ease-in, ease-out, ease-in-out, etc.), proposed in 2001, remains the industry standard. In Bézier curve swipes, if you simply sample at equal intervals of parameter t, the unequal arc lengths across curve segments cause slow speed in dense areas and fast speed in sparse areas. There are two solutions: one is Arc-length Parameterization, which remaps t to uniform arc length through numerical integration; the other is overlaying easing functions — for example, using ease-out to create a "fast start, slow stop" feel that matches the physiological process of a hand going from exertion to relaxation. Understanding and correctly applying easing functions is the key leap from "functional" to "convincing."

Conclusion

AI-assisted programming is transforming how developers work. From manual parameter tuning to AI-conversation-based generation, the barrier to applying Bézier curves has been dramatically lowered. Whether you're building automation scripts, UI animations, or interaction designs, mastering this technology combination can significantly boost development efficiency and output quality. EasyClick's open-source ecosystem also proves a point: good tools combined with AI empowerment enable more people to achieve higher-quality technical output at lower cost.