Showing posts with label #Playwright. Show all posts
Showing posts with label #Playwright. Show all posts

Tuesday, 9 June 2026

Critical Authentication Vulnerabilities Found: Playwright Security Testing Demonstration




























Critical Authentication Vulnerabilities Found: Playwright Security Testing Demonstration





๐Ÿ” The above video demonstrates a security assessment performed using Playwright automation to validate multiple authentication and session-management vulnerabilities.


๐Ÿ”ด Authentication Bypass Testing
๐ŸŸ  Session Fixation Analysis
๐Ÿ”ต Cookie Security Validation
๐ŸŸฃ Open Redirect Assessment
⚫ CAPTCHA Protection Evaluation
๐ŸŸค Automated Security Validation using Playwright


The demonstration highlights how multiple security weaknesses can potentially be chained together to create unauthorized account access scenarios. The objective is to help security teams identify, validate, and remediate critical authentication risks before they can be exploited.



Saturday, 6 June 2026

How to ARCHITECT AN ECONOMICAL ERP PLATFORM SYSTEM

How to Architect an Economical ERP Platform System
⚡ AI • ERP • QUALITY ENGINEERING

How to Architect an Economical ERP Platform System

Building an economical ERP platform system from scratch — and proving it works end-to-end. Explore a complete enterprise order-to-cash and procure-to-pay lifecycle powered by modern cloud-native architecture.

☕ Java 21 ๐Ÿš€ Spring Boot ๐Ÿ“จ Kafka ๐Ÿงช Playwright ๐Ÿ” Keycloak ☁️ Cloud Native
▶️ Watch the Complete ERP Demo
๐ŸŽฏ The goal: Demonstrate that an enterprise ERP workflow can be architected economically while still maintaining scalability, security, automation, and testability.

The entire business lifecycle is automated with zero manual clicks — from procurement through finance, manufacturing, logistics, and project closure.
๐Ÿ—️

The Architecture

Cloud-native, modular and built for scale

01

Microservices

8 cloud-native Java 21 / Spring Boot microservices covering Finance, SCM, Manufacturing, Logistics, HRM, CRM and Analytics.

02

API & Security

Traefik, API Gateway and Keycloak OAuth2/OIDC provide secure access with a multi-tenant architecture by design.

03

Event Backbone

Kafka provides asynchronous event communication between business domains and services.

04

Data Isolation

PostgreSQL-per-service keeps domain data isolated, maintainable and independently scalable.

05

Performance

Redis caching reduces unnecessary database access and improves application responsiveness.

06

Multi-Tenant Ready

The platform is designed around tenant-aware services and enterprise-grade security boundaries.

Java 21 Spring Boot Kafka PostgreSQL Redis Traefik API Gateway Keycloak OAuth2 / OIDC Microservices Multi-Tenant
๐Ÿงช

The Testing Pyramid

Automation from unit level to complete business flow

~36
JUnit unit tests across services
16
Playwright E2E specifications
14
Reusable Page Objects

๐Ÿš€ One Mega-Spec. One Complete Business.

The complete ERP business lifecycle can be executed as one automated 15-step journey, validating the system from user authentication through procurement, finance, manufacturing, inventory and project closure.

๐Ÿ”

What the Business Life Cycle Does

End-to-end enterprise workflow validation

01

๐Ÿ” Multi-Role Authentication

Procurement, finance, manufacturing and other enterprise roles can securely sign in and sign out.

02

๐Ÿ“‹ Purchase Order Workflow

Purchase orders move through a notification-driven approval workflow.

03

๐Ÿ“ฆ Inventory Validation

Stock-on-hand deltas are validated across the complete business cycle.

04

๐Ÿ’ฐ Financial Validation

The workflow validates business transactions while maintaining a P&L-neutral project closure.

05

✅ End-to-End Completion

The entire enterprise workflow completes automatically without manual intervention.

๐Ÿ’ก The Bigger Idea

An economical ERP does not have to mean a compromise in architecture or engineering quality. With the right combination of modular microservices, event-driven communication, automated testing, security and infrastructure choices, it is possible to build a platform that is both cost-conscious and enterprise-ready.

Tuesday, 14 April 2026

End-to-End AI Credit Risk Systems: Turning Raw Data into Instant Lending Intelligence

๐ŸŽง Video Transcript: Please click the Closed Captions (CC) icon in the video to read the transcript.

๐Ÿค– AI-Powered Credit Risk & Real-Time Lending

Banks are increasingly leveraging AI-powered credit risk platforms to evaluate loan applications and calculate credit scores within seconds, transforming traditional lending into a real-time decision-making process.

๐ŸŸข What’s New?

AI-powered credit risk platforms can analyze applicant data and support rapid credit scoring, helping financial institutions move toward faster and more intelligent lending decisions.

๐Ÿ”ต How It Works

A modern Data Lakehouse architecture can combine streaming data with technologies such as Kafka, Databricks, and Spark. Machine learning models then process this information to evaluate credit risk and support near real-time decisions.

๐ŸŸฃ Key Innovation

Multi-layered data refinement transforms raw information into decision-ready insights:

๐Ÿฅ‰ Bronze ๐Ÿฅˆ Silver ๐Ÿฅ‡ Gold

This layered approach improves data quality and provides reliable inputs for credit scoring and risk analysis.

๐ŸŸก Risk Intelligence in Action

Advanced analytics can calculate important credit-risk metrics that help financial institutions understand potential losses and exposures.

PD
Probability of Default
LGD
Loss Given Default
EAD
Exposure at Default

๐Ÿ”ด Business Impact

Visualization platforms such as Power BI can provide real-time dashboards, automated decision insights, and improved customer experiences.


Faster Credit Decisions
Moving lending decisions from lengthy manual processes toward near real-time evaluation.

๐ŸŸ  The Big Picture

AI + real-time data pipelines are reshaping credit-risk management by moving organizations from manual approvals toward intelligent, end-to-end decision platforms designed for speed, scalability, and precision.

๐ŸŒŸ

Follow the Complete Engineering Journey

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Tuesday, 17 February 2026

MODERN END TO END IBRD CREDIT SCORE AI PREDICTOR FULL STACK WITH CHAT ASSISTANT APPLICATION DEVELOPMENT, TESTING, AND CI/CD

Modern End-to-End IBRD Credit Score AI Predictor

Full-Stack Application Development, Comprehensive Quality Assurance, and Automated CI/CD Lifecycle

๐ŸŽฅ

YouTube Playlist & Project Walkthroughs

Explore the complete development, testing, CI/CD, and application walkthroughs for the Modern End-to-End IBRD Credit Score AI Predictor.

▶ Watch the Full YouTube Playlist

Modern E2E IBRD Credit Score AI Predictor — Full-Stack Application Development

Modern End-to-End IBRD Credit Score AI Predictor — Full-Stack & Chat Assistant Testing Pipeline

Modern End-to-End IBRD Credit Score AI Predictor — Full-Stack & Chat Assistant CI/CD Pipeline

๐Ÿ”ท

Full-Stack Ecosystem & Development

Engineered with a modular React frontend, an efficient Node proxy layer, and a high-performance FastAPI ML backend. Features a component-first UX strategy with robust, end-to-end error propagation to guarantee predictable, bulletproof score forecasting.

๐ŸŸฉ

Robust Unit Testing

Powered by Jest and React Testing Library to rigorously verify complex component logic. Built-in validation mechanisms gracefully handle extreme edge cases across input forms, conversational chatbot mechanics, and systemic error mitigation matrices.

๐ŸŸจ

Feature & End-to-End (E2E) Testing

Utilizes Cucumber feature specifications paired with Playwright to simulate real-world user journeys. Seamlessly exercises holistic browser actions including data-driven scoring forms, live conversational AI prompts, and comparative internet lookup tasks.

๐ŸŸฅ

API Smoke Testing

Implements automated Postman & Newman execution sweeps to validate secure, low-latency connectivity between the Node proxy and downstream ML models. Designed for instant anomaly detection and rapid failure localization.

๐ŸŸช

CI Orchestration & Automation

Managed seamlessly via azure-pipelines.yml to drive an automated deployment funnel from linting and code analysis to compilation, dockerization, and secure container publishing. Leverages localized docker-compose*.yml configurations to guarantee precise environment parity.

๐ŸŸง

Health, Stability & Resiliency

Employs systemic engine health checks and dynamic wait gates to eliminate flaky E2E environment initialization. Codebases proactively assert styled fallback mechanisms—including warning thresholds like specialized red-on-yellow indicators—for unexpected third-party service outages.

๐Ÿ”Ž

Enterprise Visibility & Analytics

The deployment pipeline auto-publishes exhaustive, interactive HTML/JUnit validation sheets alongside precise coverage metrics via Cobertura. This telemetry makes regressions fully traceable as applications graduate from local Testing, to UAT, and straight into Production environments.

Connect & Follow

Follow the project journey, technical updates, videos, and engineering insights.

Sunday, 1 February 2026

HOW TO BUILD PRODUCTION GRADE CRM MANAGEMENT SYSTEM FOR MOBILE + WEB - FULL STACK

๐Ÿš€

HOW TO BUILD A PRODUCTION-GRADE CRM MANAGEMENT SYSTEM

Full-Stack Web + Mobile CRM Application with Testing, Automation, and CI/CD

๐ŸŽฌ VIDEO SERIES

Watch the Complete CRM Development Journey

Explore the complete production-grade CRM journey from full-stack development to automated testing and CI/CD deployment.

๐Ÿงฉ

Full-Stack CRM Application Development

Production-grade Web + Mobile CRM architecture and implementation

๐Ÿงช

Full-Stack CRM Testing

Unit, integration, API, browser, mobile, and end-to-end testing

⚙️

Full-Stack CRM CI/CD Pipeline

Automated quality gates, test execution, reporting, and deployment workflow

๐Ÿ’ผ

Production-Grade CRM Management System

A complete Web + Mobile CRM solution designed with full-stack engineering, automated quality assurance, API validation, cross-browser testing, mobile emulation, and continuous integration and deployment.

๐Ÿงฉ

Full-Stack Application Development

Developed a complete Web + Mobile CRM application covering frontend, backend, APIs, business workflows, data management, and shared services. The architecture is designed to support scalable application development while maintaining consistency across web and mobile experiences.

๐Ÿงช

Unit Testing

Individual components, functions, utilities, and application logic are validated through automated unit tests to improve correctness, maintainability, reliability, and regression protection.

๐Ÿ”—

Integration & API Testing

Seamless interaction between application modules and backend services is validated using Postman and Newman for automated API-level integration testing, connectivity verification, and rapid failure detection.

๐ŸŒ

End-to-End Testing with Playwright

Automated browser-based testing validates complete user journeys across multiple environments and real-world usage scenarios.

๐Ÿ’ป Web Browser Testing
๐Ÿ“ฑ Mobile Device Emulation
๐Ÿงญ Microsoft Edge-Specific Scenarios
๐Ÿง  Edge Case & Validation Scenarios
⚙️

Continuous Integration & Deployment

The project demonstrates a structured CI/CD workflow that automates testing, validation, reporting, and deployment activities to improve delivery speed and software quality.

✅ Automated Test Execution
๐Ÿ“Š JUnit Test Reporting
๐Ÿšฆ Automated Quality Gates
๐Ÿ” Parallel CI/CD Workflow Execution

๐Ÿ† Engineering & Quality Highlights

Full-Stack Architecture Web + Mobile Unit Testing API Testing Playwright E2E Mobile Emulation Microsoft Edge CI/CD Automation JUnit Reporting Quality Gates
๐ŸŒŸ

Follow the Complete Engineering Journey

Subscribe for more full-stack development, automated testing, Playwright, CI/CD, DevOps, and software engineering projects.

Wednesday, 31 December 2025

๐ŸŽฌ AI-Powered Stock Price Movement Prediction: Playwright + Python + Claude Desktop LLM + MCP Server Demo

๐ŸŽฌ AI-Powered Stock Price Movement Prediction

Playwright + Python + Claude Desktop LLM + MCP Server Demo

๐Ÿš€ Watch the AI-Powered Prediction System in Action!

In this demo, I showcase a complete end-to-end pipeline that predicts stock price movements using modern AI, machine learning, web automation, and Model Context Protocol (MCP) technologies.

The system analyzes Reliance Industries Ltd (RIL) stock data scraped from BSEIndia.com and transforms the collected information into easy-to-understand, human-readable predictions and insights.

๐Ÿ”ง TOOLS & TECHNOLOGIES USED

๐ŸŽญ

Playwright + Python

Web automation and scraping of live stock market data.

๐Ÿ“ˆ

Machine Learning

Predictive modeling for forecasting future closing prices.

๐Ÿค–

Claude Desktop LLM

AI-powered analysis, interpretation, and summarization of results.

๐Ÿ”Œ

Local MCP Server

Custom MCP server connecting the automation, data, and AI components.

๐Ÿ“Š WHAT THIS DEMO COVERS

๐Ÿ”ท Real-time data scraping from BSEIndia.com

๐Ÿ”ท Automated capture of market depth and financial data

๐Ÿ”ท Generation of analytical visualizations

⭐ Open / High / Low / Close Price Comparison Chart

⭐ Trading Volume & Spread Analysis

⭐ Future Close Price Predictions Table

๐Ÿ”ท AI-powered summarization into actionable insights

๐Ÿ› ️ MCP SERVER ARCHITECTURE

Tool 1: run_playwright_test

Executes the Playwright automation script to collect and process stock market data.

๐Ÿง 

Tool 2: summarize_outputs

Processes analytical outputs and visualizations for interpretation by the Claude LLM.

๐Ÿ”„ END-TO-END AI WORKFLOW

๐ŸŒ BSEIndia.com   →   ๐ŸŽญ Playwright   →   ๐Ÿ Python   →   ๐Ÿ“Š ML Model   →   ๐Ÿ”Œ MCP Server   →   ๐Ÿค– Claude Desktop   →   ๐Ÿ’ก AI Insights

⚠️ Disclaimer: This demonstration is for educational and technology demonstration purposes only. Stock price predictions generated by machine-learning or AI systems are not guaranteed and should not be considered financial or investment advice.

๐ŸŒŸ Stay Connected

๐Ÿ’ผ Subscribe on LinkedIn ▶️ YouTube Channel ๐Ÿ‘ค LinkedIn Profile

๐Ÿ”” Follow me on LinkedIn for more content on AI, automation, Python, machine learning, MCP, and LLM-powered applications.

Tuesday, 25 November 2025

VSCode Integration with Local MCP Server To Automate the P2P Business Process Flow in SAP S/4HANA



VSCode Integration with Local MCP Server To Automate the P2P Business Process Flow in SAP S/4HANA

 

Overview: 

๐Ÿ”ต Integrate VS Code with a local AI LLM to automate the P2P (Procure-to-Pay) process flow in SAP S/4HANA. 

๐Ÿ”ต A local MCP server is created to host AI tools. 

๐Ÿ”ต The MCP server registers “test” as a tool for execution. 

๐Ÿ”ต A CLI interface is implemented to manage standard input/output (STD I/O). 

๐Ÿ”ต The CLI converts user commands into instructions understood by the MCP server. 

๐Ÿ”ต The MCP server receives the converted commands and executes the “test” tool. 

๐Ÿ”ต Results flow from the MCP server back through the CLI into VS Code, enabling automated workflow execution. 

 

Business Process Flows Automated in SAP S/4HANA: 

๐Ÿ”ต Purchase-to-Pay (P2P) 

 


 

Subscribe on LinkedIn  YouTube Channel 

 
 
 

 

Wednesday, 19 November 2025

SAP S/4HANA Business Process Flow Automation using Playwright MCP Agent

SAP S/4HANA Business Process Flow Automation using Playwright MCP Agent
⚡ SAP • TEST AUTOMATION • AI

SAP S/4HANA Business Process Flow Automation using Playwright MCP Agent

Explore how SAP S/4HANA business process flows can be automated using Playwright, JavaScript, TypeScript and the Model Context Protocol (MCP) Agent.

๐ŸŽญ Playwright ๐Ÿค– MCP Agent ๐Ÿ”ง MCP Server ๐Ÿ“˜ TypeScript ⚡ JavaScript ๐Ÿข SAP S/4HANA
▶️ SAP S/4HANA Automation Demo
● END-TO-END DEMO
๐Ÿ“‹ Overview

SAP S/4HANA business process flows are automated using Playwright, which is controlled by the MCP Agent. This approach brings AI-assisted interaction and browser automation together for enterprise business-process testing.

๐Ÿค–

Automation Technology

Modern browser automation powered by MCP

JavaScript & TypeScript

The automation implementation uses JavaScript and TypeScript to create maintainable and scalable browser automation workflows.

๐ŸŽญ

Playwright Automation

Playwright provides browser-level automation capabilities for interacting with SAP S/4HANA business applications.

๐Ÿค–

MCP Agent

The MCP Agent controls and orchestrates the Playwright automation flow, enabling an AI-assisted automation approach.

๐Ÿ”ง

MCP Server

The MCP Server acts as the integration layer between the automation capabilities and the agent-driven workflow.

⚡ JavaScript ๐Ÿ“˜ TypeScript ๐ŸŽญ Playwright ๐Ÿค– MCP Agent ๐Ÿ”ง MCP Server ๐Ÿข SAP S/4HANA
๐Ÿ”„

Business Process Flows Automated

Enterprise workflows automated inside SAP S/4HANA

01
๐Ÿ›’

Purchase-to-Pay

Automate the procurement lifecycle from purchasing activities through the payment process.

02
๐Ÿ’ฐ

Order-to-Cash

Validate the end-to-end sales process from customer order through fulfillment and financial processing.

03
๐Ÿ“Š

Project Management

Automate SAP project-related workflows and validate business-process execution through browser automation.

SAP S/4HANA → MCP Agent → Playwright → Business Flow
AI-ASSISTED TEST AUTOMATION

๐Ÿš€ Connecting MCP Intelligence with Browser Automation

The combination of MCP Agent technology and Playwright creates an automation layer capable of interacting with enterprise applications while keeping the underlying browser automation implementation structured and reusable.