Manish Bainsla
A results-oriented professional with over 4 years of industry experience, including 1+ years specializing in building AI solutions for workflow automation. Proven ability to design and implement AI agents and scalable backend services, with a focus on creating reusable frameworks to accelerate development cycles.
Red Hat
Senior Software Engineer
Sessions
In an era of AI-generated PR descriptions and automated linting, the "Human API" of code reviews is under threat. We often treat reviews as a technical gate to pass, but for a software engineer, they are the highest-bandwidth channel for growth, mentorship, and domain mastery.
Drawing on my journey from Intern to Senior Engineer, I’ll share how code reviews served as my "fast-track" to technical knowledge and team leadership mindset. We’ll discuss why AI can check your syntax but can't understand your business logic—and why a simple suggestion like adding a specific logger can save hours of production downtime. This talk shares why we should move beyond "LGTM" and transform code reviews into a powerful tool for building cohesive teams and scalable products in the age of AI.
Workshop Outline:
Phase 1: The Foundation
The "Why" & The Architecture: Brief overview of why standardized engineering (via FastAPI) is the secret sauce for moving AI from "cool demo" to "production tool."
Environment Check: Rapid-fire validation of Git, Python, VSCode, and Postman environments.
The Blueprint: Introduction to the template-mcp-server repository and its core components.
Phase 2: From Zero to Local
Cloning & Configuration: Initializing your local environment and understanding the configuration files.
The "Hello World" Run: Getting the base template running on your machine.
Walkthrough: Navigating the repository structure—where the logic lives and how the server communicates.
Phase 3: Building Your Custom Tool
The Code-Along: Step-by-step implementation of a custom "tool" within the MCP framework.
Logic Injection: Defining the tool's purpose, input parameters, and execution logic.
Live Testing: Running the server locally and verifying that the tool is discoverable and functional.
Phase 4: Integration & Orchestration
Connecting to the Agent: Integrating your local MCP server with an agent (like Cursor) or a remote orchestrator.
The Real-World Test: Watching the agent invoke your custom tool to solve a live task.
Containerisation: A quick look at the "deployment-ready" state—wrapping your server for enterprise scale.
Outcomes:
Development: Move from a blank slate to a FastAPI-based MCP server.
Extensibility: Learn exactly how to add new tools to existing templates.
Integration: Connect your server to real-world agents like Cursor.
Scalability: Leave with a containerised solution ready for production deployment.
Note: This is a high-speed lab. We prioritize doing over discussing. Ensure your Python/Git/VSCode/Postman is warmed up and ready to pull images!