Amazon Kiro HandsOn: Build Apps with AI Agents | Q Developer

Install Cairo.dev on macOS Apple silicon and sign in with Google, GitHub, or AWS builder using two-factor authentication. Import VS Code settings or skip, and set up themes.

Install Cairo cli on Amazon Linux via the command line, download and unzip with curl, authenticate with a builder id, and test a Python calculator, noting ide similarity.

Configure the Kydo router console by setting up identity providers, including identity center and external providers. Create users, invite them, and enable MFA to access the portal.

Learn wipe coding, a natural language prompt approach that focuses on intent and outcomes. AI generates code and architecture, enabling rapid prototyping and easier development for non-technical founders.

Begin your first interaction with Amazon Q Kydo Dev in the IDE, using chat and autopilot to convert a prototype Python calculator to production and explore MCP steering.

Explore the four core components of Cairo—steering, specs, hooks, and MCPs—and how they shape a structured workflow from NLP prompts to task execution.

Navigate the Cairo IDE with a VS Code-like interface, exploring chat, toggleable sidebars, multi-file workspaces, and built-in run and debug, extensions, and settings to tailor your workflow.

Define and refine AI outputs using steering in AWS Cairo by issuing initial prompts and applying constraints. Iteratively refine instructions to deliver production-ready, context-aware code and results.

Learn Cairo steering to give your AI a permanent memory, reducing prompting treadmill and amnesia by applying project rules via markdown files and inclusion modes for personal and workspace rules.

See how Amazon Q uses Cairo to help you understand existing code, clone a GitHub repo, and explore Node.js projects. Explore Vibe and Spec options and project structure.

Understand specs and specification driven development, turning natural language prompts into formal requirements, acceptance criteria, and design to create a clear, production ready architecture and maintainable code.

Discover spec driven development (SSD) for AI agents, replacing guesswork with a formal contract and a three-step markdown workflow that feeds into the next, enabling developers to architect.

Create a custom spec within the project to add features using credits, build a feature, generate design.md, gather requirements, and outline high-level and low-level designs with previews.

Continue creating the custom spec by generating requirements and a task list, then build the spec in steps—from design to code—highlighting documentation, test cases, and five requirement areas.

Execute the specs workflow to generate the task from task.md, run tasks (all or individually), push changes to GitHub, and explore test coverage, init modules, and task notes.

Engage with the specs workflow hands-on by executing custom specs, running required tasks, and validating test cases as the project grows, while addressing node and npm setup issues.

Create and apply a project spec to formalize artifacts and commands, detailing the folder layout, package.json scripts, and npm start steps to enable local execution and testing.

Clone a node.js SPA from github, set up node and npm, and implement a random fact feature with a fact service and fact display service.

Explore an end-to-end project workflow to build apps with ai agents: initialize npm, install express, run the server, and validate results.

Explore how agent hooks use event-driven triggers to automate maintenance tasks—testing, code generation, documentation updates, and deployments after file changes.

Explore how agent hooks work in Cairo, using manual or Cairo-based prompts and predefined templates to build agents, sync docs, and scaffold a basic Node.js calculator project.

Master manual agent hooks for end-to-end control, creating and deleting hooks, configuring file save events with regex or absolute paths, and updating readme and docs.

Perform a power hands-on session with aws cairo, learning to bundle tools, steering instructions, hooks, and mcp into a custom power using power.md and mcp.json, guided by official docs.

Create a custom steering in Cairo to shape code behavior, generate a markdown file, and enforce rules, comments, and documentation for readable, industry-standard code.

Build an end-to-end calculator app using ai agents, specs, and hooks to auto-update tests and HTML, covering add, subtract, multiply, and divide.

Learn how modern context protocol (MCP) servers enable context aware AI access across disparate systems, connecting resources like GitHub and Datadog via authenticated connectors to generate precise outputs.

Learn to set up MCP using AWS Labs, explore MCP model context protocols and external resources, and load MCP servers with Cairo CLI for workspace and user configurations.

Explore how to visualize and manage GitHub repositories with the MCP server, including viewing functions and files, using Cairo CLI, and providing personal access tokens and logs.

Explore artificial intelligence fundamentals, including machine learning, NLP, and computer vision, and see how AI powers healthcare, finance, and autonomous driving, while highlighting deep learning trends and future job impact.

Explore generative AI and the Amazon Q stack, learning how large-data training enables models to generate original text, images, and content across creative arts, healthcare, and education.

Explore opportunities across healthcare diagnostics, creative arts, and customer service automation with genai, and address risks like misuse, misinformation, bias, and IP ownership through ethical development.

Explore the double-edged potential of generative AI to transform banks and hospitals, while weighing environmental costs, security risks, and embedding ethics from the start to guide responsible adoption.

Understand how Amazon Q, a generative AI assistant, uses company data to answer questions, summarize policies, and automate tasks with code generation and debugging from Q Developer and Q Business.

Learn how the Amazon Qt developer is embedded in the AWS console to enable chat, diagnose errors, and access architecture guidance, documentation, and support through chat channels.

Explore the latest amazon queue update in the amazon console, featuring a ui reposition, a prompt library, and support for listing s3 buckets by production tag.

Learn to diagnose AWS console errors with Amazon Q, using an EC2 placement group issue to illustrate automated analysis and documented resolutions.

Learn how to raise a support ticket in the AWS console from Amazon queue, selecting account and billing or technical, drafting the case, and submitting via email, chat, or phone.

Explore how Amazon QDeveloper handles conversations; it answers questions related to AWS services and your account, not general knowledge like ChatGPT. See its coding-focused usage in the console.