DOCSEARCHbyAlgolia
Submit Docs

Tanthanh Knowledge Hub

website·Indexed 30 days ago

https://docs.tanthanh.dev/

A comprehensive technical knowledge hub covering test automation (Karate, Selenium, Gatling), AI/ML agents and Model Context Protocol (MCP), CI/CD and DevOps with Jenkins, and enterprise networking including pyATS, Cisco DNA Center SDK, and SD-Access.

Tokens
29.2K
Snippets
124
Records
197
Agent score
48%

What's inside Tanthanh Docs

  1. Overview of Test Automation and AI/ML Knowledge Hub

    This knowledge hub provides comprehensive guides and practical resources across several technical domains:

    • Test Automation: Focuses on frameworks such as Karate, Selenium, Robot Framework, and Gatling, covering API testing, web automation, BDD, and performance testing.
    • AI/ML & Agents: Covers MCP servers, AI agents, agent-to-agent communication, and machine learning integration for automating complex workflows.
    • CI/CD & DevOps: Focuses on Jenkins pipeline automation, deployment strategies, and integrating testing into continuous delivery workflows.
    • Networking: Covers Cisco technologies, network automation with pyATS, 802.1X authentication, SDA, and CCNA fundamentals.

  2. Overview of the Gas Expense Tracker Architecture

    The Gas Expense Tracker is a zero-cost full-stack finance application that uses Google's ecosystem for hosting and data storage. The system allows users to record expenses via a mobile-first React PWA, stores data in Google Sheets, and provides notifications and summaries via a Telegram Bot. AI-driven financial analysis is integrated using Gemini 2.5 Flash Lite.

    Data Flow: User $\rightarrow$ React PWA $\rightarrow$ google.script.run $\rightarrow$ GAS Backend $\rightarrow$ Google Sheets. Additionally, GAS Triggers handle Telegram Bot notifications and Gemini AI analysis.

  3. Overview of Model Context Protocol (MCP)

    Model Context Protocol (MCP) is an open protocol that enables AI assistants (such as Claude) to securely connect to external data sources and tools. This allows models to access external data (databases, APIs, file systems), execute external tools (scripts, commands), maintain conversation context across tools, and operate within a permission-based security framework.

  4. Overview of Test Automation learning paths

    The Test Automation guide provides comprehensive coverage of modern automation practices divided into three primary domains:

    1. API Testing: Focuses on REST API testing techniques and tools including Karate DSL (advanced framework), Gatling (performance and load testing), and Postman/Newman (automation).
    2. Web & UI Testing: Covers browser and mobile automation using Selenium WebDriver (Python/Java), Robot Framework (keyword-driven), Appium (mobile apps), and Visual Testing for UI regression.
    3. BDD & Test Design: Covers Behavior-Driven Development using Cucumber/Gherkin, effective test scenario design, and test data management.

  5. Overview of Karate API Testing Framework

    Karate is an open-source tool that combines API testing, mocking, and performance testing in a single framework. It uses Gherkin syntax, meaning no deep Java or programming knowledge is required to write tests. Key capabilities include native JSON/XML support, rich assertions, Cucumber-style HTML reports, and built-in Gatling integration for performance testing.

  6. Overview of AI/ML and Agent Development capabilities

    The AI/ML & Agents section provides guidance on building autonomous systems, focusing on four primary areas:

    1. Model Context Protocol (MCP): Development of custom MCP servers, client integration, and extending AI models (like Claude) with custom tools.
    2. AI Agents & Agentics: Design patterns for agent architecture, Agent-to-Agent (A2A) communication, multi-agent system orchestration, and complex workflow management.
    3. Agent Development: Utilizing AgentSDKs, building specialized agents, testing/validation, and production deployment.
    4. Machine Learning: LLM integration, incorporating ML models into applications, and AI testing strategies.

  7. Overview of the Webex Testbed Monitor Bot

    The Testbed Monitor Bot is a Python-based chatbot integrated with Webex to manage and track test environment resources. Unlike traditional bots, it uses WebSocket connections instead of webhooks, allowing it to operate securely behind corporate firewalls without requiring a public IP address, SSL certificates, or ngrok tunnels. Key capabilities include real-time monitoring of testbed status, interactive registration via Adaptive Cards, and automated broadcasting to multiple Webex rooms.

  8. Understand Model-Driven Programmability concepts

    Model-driven programmability is a standardized way of representing network devices and their capabilities. Unlike traditional CLI management, which relies on unstructured text, model-driven programmability uses data models to define the syntax, semantics, and constraints of data objects (e.g., VLAN structures, valid ID ranges, and data types). This allows automation scripts to interact with devices consistently regardless of the specific hardware.

  9. Understand Model-Driven Programmability concepts

    Model-driven programmability is a standardized way of representing network devices and their capabilities. Unlike traditional CLI management, which relies on unstructured text, model-driven programmability uses data models to define the syntax, semantics, and constraints of data objects (e.g., defining the structure, valid range, and data type of a VLAN ID). This allows automation scripts to interact with diverse network devices in a consistent manner.

  10. Analyze the Model-Driven API architecture

    A model-driven API is composed of four core layers that separate the definition of data from its transmission:

    1. Data Models: The foundation that defines syntax and semantics (e.g., YANG).
    2. Transport: The method of moving data (e.g., SSH, TLS, HTTP, HTTPS).
    3. Encoding: The serialization format of the data (e.g., XML, JSON, Google Protocol Buffers).
    4. Protocols: The communication rules (e.g., NETCONF, RESTCONF, gRPC).

  11. Understand Model-Driven Programmability concepts

    Model-driven programmability is a standardized way of representing network devices and their capabilities. Unlike traditional CLI management, which relies on unstructured text, model-driven programmability uses data models to define the syntax, semantics, and constraints of data objects (e.g., defining if a VLAN ID is an integer or the valid range of that ID). This allows automation scripts to interact with devices consistently regardless of the specific hardware.

  12. Implement a Model-Driven API architecture

    A complete model-driven API architecture consists of four distinct layers. It is critical to distinguish between the data model (the framework) and the encoding/protocol (the transmission method).
    1. Data Models: Define syntax and semantics (e.g., YANG).
2. Transport: The communication channel (e.g., SSH, TLS, HTTP, HTTPS).
3. Encoding: The serialization format (e.g., XML, JSON, Google Protocol Buffers).
4. Protocols: The communication rules (e.g., NETCONF, RESTCONF, gRPC).