Discover how modern ERP integrations are transforming e-commerce efficiency in 2025, helping brands streamline workflows, boost productivity, and scale with confidence. CTA: Stay tuned and subscribe for more deep dives into the future of e-commerce technology! https://mobisoftinfotech.com/resources/blog/ecommerce-retail/ecommerce-erp-integration-guide

Learn how to build a fully functional React admin dashboard integrated with Supabase, covering authentication, CRUD operations, and real-time updates. CTA: Follow the podcast and start building smarter, data-driven React dashboards today! https://mobisoftinfotech.com/resources/blog/web-development/react-admin-dashboard-supabase-tutorial

A quick and practical breakdown of how to set up Firebase Cloud Messaging in React Native, covering everything from configuration to best practices for reliable push notifications. CTA: Tune in, follow the podcast, and level up your React Native skills with every episode! https://mobisoftinfotech.com/resources/blog/app-development/react-native-push-notifications-tutorial

In this episode, we explore how distribution logistics is evolving into a smarter, faster, and more connected ecosystem. From AI-driven decision-making to IoT-enabled visibility and advanced automation, today’s supply chains are transforming at a rapid pace. We break down the key technologies, trends, and sustainability practices that are shaping the future of logistics—and discuss why companies must adapt to stay competitive. CTA: Tune in now and stay ahead of the logistics revolution! https://mobisoftinfotech.com/resources/blog/transportation-logistics/future-of-distribution-logistics-technology

AI Agent Development Example with Custom MCP Server: Build A Code Review Agent – Part II Welcome to Part 2! Haven’t read Part I yet? Start here to understand how the AI-powered code review agent was built. We built all the core components for our AI agent example Now in Part 2, we'll bring it all together by building the MCP server, configuring Claude Desktop, and testing our complete AI agent. Table of Contents Building the MCP Server Configuring Claude Desktop Testing Your Agent Troubleshooting Common Issues Next Steps Building the MCP Server Now we create the MCP server that ties everything together. This is the primary entry point through which Claude Desktop communicates. What it does: Exposes code review functionality as MCP tools, which Claude Desktop can call using natural language. How it works: Initializes FastMCP server with tool definitions Each tool is decorated with @mcp.tool() to register it Tools receive arguments, execute logic, and return JSON responses Global state tracks active reviews for status queries Available tools: detect_tech: Identifies programming language from project file get_available_checklists: Lists available YAML checklists get_checklist: Retrieves specific checklist details review_code: Executes full code review with progress tracking get_review_status: Checks status of active/completed reviews This process supports MCP server integration and helps streamline workflows in AI-powered software development. Example tool implementation: from mcp.server.fastmcp import FastMCP mcp = FastMCP(name="Code Reviewer") active_reviews = {} @mcp.tool() def detect_tech(project_path: str) -> str: """ Detect technology stack from a project file (e.g., package.json, pyproject.toml). Args: project_path: Absolute path to a project configuration file (not a directory) Returns: JSON string with detected technology, frameworks, and confidence """ try: path = Path(project_path) if path.is_dir(): return json.dumps({"error": "project_path must be a file, not a directory. ..."}) if not path.exists(): return json.dumps({"error": f"File does not exist: {project_path}", ...}) # Get the parent directory for detection project_dir = str(path.parent) result = detect_technology(project_dir) return json.dumps(result, indent=2) except Exception as e: return json.dumps({"error": str(e), ...}) # Entry point if __name__ == "__main__": mcp.run() Create the file: Create main.py with the complete MCP server implementation, including all five tools: detect_tech() (shown above as full example) get_available_checklists() - Similar pattern, calls ChecklistEngine get_checklist() - Similar pattern, loads and formats YAML review_code() - Main review tool with progress callbacks get_review_status() - Queries active_reviews dictionary Note: Full implementation available in the GitHub repository. After setting up your MCP server and exploring the available tools, learn how to integrate and test your MCP server setup with real-world AI agents through our expert MCP server development consultation services. Read More: https://mobisoftinfotech.com/resources/blog/ai-development/ai-agent-development-mcp-server-integration-deployment

AI Agent Development Example with Custom MCP Server: Build A Code Review Agent – Part I Using MCP servers can make your AI agents intelligent and more rooted in the context of the task. This ensures that the LLM model gets right context about your task so that it can produce results specific to your goals. In this comprehensive guide, we'll demonstrate AI agent development by building a practical example: a code review agent that integrates with Claude Desktop using the Model Context Protocol (MCP). Through this hands-on MCP development tutorial, you'll learn how to create AI agents for software development that can automatically detect your project's programming language, load appropriate review checklists, and provide structured feedback. By the end of this guide, you'll understand the fundamentals of building AI agents and have a fully functional code review tool that you can customize for your team's specific needs or adapt for entirely different use cases. Explore more about MCP and its role in AI systems: Learn more about MCP and its role in AI systems here What we will build: A code review agent that works with Claude Desktop Automatic technology detection for Python, JavaScript, Java, Go, Rust, and TypeScript Customizable review checklists with security, quality, and performance checks Pattern-based code analysis using regular expressions Real-time progress tracking during reviews Time required: 30-45 minutes Skill level: Intermediate Python knowledge Prerequisites: Python 3.11 or higher installed Claude Desktop application Basic command line familiarity Understanding AI Agent Development Through a Code Review Implementation Before we dive in, let's clarify exactly how this code review system works: How the Code Review Process Works in Our AI Agent Our implementation uses static code analysis, not AI-based code review. Here's what happens: Pattern Matching: Devs create a YAML checklist containing regex patterns based on specific requirements. The system used this checklist to check flag errors line by line (e.g., eval(, hardcoded passwords, console.log). File Validation: Checks if required files exist (e.g., requirements.txt, package.json) Static Analysis: No actual code execution - just text pattern matching Discover more about AI solutions for businesses: Check out our AI services for businesses to explore custom AI solutions. The AI Agent's Role: How Claude Orchestrates MCP Tools Claude's role is limited to: Natural Language Interface: You can ask "review my Python code" instead of calling command-line tools Tool Orchestration: Claude decides which MCP server development tools to call based on your request Result Presentation: Claude formats and explains the findings in conversational language What This Means for AI Agent Development Regex patterns do the actual code review you define in YAML files Not AI-based: Claude doesn't analyze your code semantically or understand logic Pattern-based: You define what to look for (like "find all eval() calls") Customizable: You control exactly what gets checked by editing YAML checklists Deterministic: Same code always produces the same results (no AI variability) Why Building AI Agents with MCP This Way Is Effective ? This hybrid approach gives you: Control: You define the exact rules via YAML checklists Speed: Pattern matching is fast, no AI inference needed for scanning Consistency: Deterministic results every time Extensibility: Easy to add new checks without AI training Convenience: Natural language interface via Claude Desktop Think of it as linting rules + Claude's conversational interface. You're essentially building an AI agent with MCP that runs as a customizable linter through natural conversation. Read More: https://mobisoftinfotech.com/resources/blog/ai-development/ai-agent-development-custom-mcp-server-code-review

“Developing a Startup Mobile App with Flutter: A Comprehensive Guide” “Explore how startups can leverage Flutter to build high-quality, cross-platform mobile apps faster and with lower costs. Learn about the benefits, challenges, and best practices of using Flutter for mobile product innovation.” READ MORE: https://mobisoftinfotech.com/resources/blog/developing-startup-mobile-app-flutter

“From Design-Led to Experience-Led Mobile App Development” In this episode, we explore why mobile apps must move beyond great visuals to holistic experiences. We’ll discuss how experience-led development differs from traditional design-led approaches, why it matters for adoption and retention, and what teams must do to build apps that truly engage users. Hashtags:#MobileAppDevelopment #ExperienceLedDesign #UXDesign #ProductDesign #AppInnovation #AppEngagement #UserCentric #MobileUX READ MORE: https://mobisoftinfotech.com/resources/blog/app-development/design-led-to-experience-led-mobile-app-development