Recap Series
- Session 01: A Developer’s Roadmap to Architecting for Agents
- Session 02: Amazon Bedrock Data Automation
- Session 03: Multi-Agent on AgentCore
- Session 04: Building Agentic AI Nova Act and Strands Agents in Practice
- Session 04: Accelerating Migration Projects with Kiro using Spec-Driven Development
- Session 06: From "Matching" to "Understanding": Personalized AI Search Practice Driven by AgentCore Memory
- Session 07: Observe to Optimize – LLM Observability to AIOps Turning real-time insights into intelligent automation
- Session 08: Deploying TEAM and Building the Best Engineering Team
- Session 09: Five Hard Lessons from Five Years of So-Called Serverless Databases
- Session 14: What if AI does my job How Q Developer CLI and Kiro have changed my daily routine
- Session 16: Velocity with Vigilance: Security Essentials for Amazon Bedrock Agent Development
- Session 26: Run OSS LLMs on a Single H100 Smarter, Cheaper, Faster
- Session 28: A Modern Unified Metadata Architecture: New Approaches to Breaking Down Data Silos
- Session 29: Serverless MediaOps: Automating Video Workflows with AI on Amazon Web Services
- Session 30: Architecting for Efficiency and Reliability with Performance Testing at Scale
- Session 31: Connecting the World Through Open Source: Practical Journey of Technology, Community and Global Developer Relations
- Session 33: Building Streaming Iceberg Tables for Real-Time Logistics Analytics
- Session 34: Accelerating Large-Scale Robot Strategy Training: An Automated Closed-Loop Architecture Based on Kiro, Trainium, and EKS
- Session 35: From Vibe to Viable with spec driven development
- Session 36: Making Cloud Cost Analysis Smarter: Building FinOps Intelligent Agents with Strands and AgentCore
- Session 37: Transform Conversational Agentic AIOps for K8s Using CNCF Kagent, K8sGPT, and Nova Sonic
Session Notes
AI is changing software
- 2023: Helping developers write code faster
- 2024: Generating larger pieces of code and answering questions
- 2025: Completing development tasks end-to-end with human in the loop Challenges with AI development
- Scaling AI development: AI coding tools excel at small tasks but can fail with complex projects
- Limited control: Existing tools make it difficult to collaborate with and manage agents
- Code quality: Getting a project from proof-of-concept to production while maintaining quality control becomes increasingly difficult The Vibe
- Rapid, conversational code generation (CHOP)
- Iterative, back and forth
- Ephemeral
- Point in time prompts
- Transient context
The path to spec driven development
Good practices
- Break down large problems: Developers learned how to manually break down large problems into smaller units and build incrementally
- Specificity and Clarity: Precision and clarity are key in directing AI coding assistants to generate good outputs
- Context and Prompt engineering: Providing the right context is key to producing consistency and control
Taskmaster
- A task management system for AI-driven development with Clauide, designed to work seamlessly with Cursor AI
- [ 1 ] Documentation:
- Configuration Guide: Set up environment variables and customize Task Master
- Tutorial: Step-by-step guide to getting started with Task Master
- Command Reference: Complete list of all available commands
- Task Structure: Understanding the task format and features
- Example Interactions: Common Cursor AI interaction examples
- Migration Guide: Guide to migrating to the new project structure
- [ 2 ] Quick Install for Cursor 1.0+ (One-Click):
- Click the copy button (top-right of code block) then paste into your browser: cursor://anysphere.cursor-deeplink/mc/install?name=taskmaster-aiconfig-eyJjIjI1biWSkI 1joibnB4I
- Note: After clicking the link, you'll still need to add your API keys to the configuration. The link installs the MCP server with placeholder keys that you'll need to replace with your actual API keys
- [ 3 ] Requirements:
- Taskmaster utilizes AI across several commands, and those require a separate API key. You can use a variety of models from different AI providers provided you add your API keys. For example if you want to use Clauide 3.7 The Calm Coding Philosophy
- Code not with stress, but with structure.
- Prompt not with noise, but with intent.
- Build not just fast — but with flow. Chat is a bad UI pattern for development tools
- Code forces humans to be precise. That's good—computers need precision. But it also forces humans to think like machines.
- For decades we tried to fix this by making programming more human-friendly. Higher-level languages. Visual interfaces. Each step helped, but we were still translating human thoughts into computer instructions.
- AI was supposed to change everything. Finally, plain English could be a programming language—one everyone already knows. No syntax. No rules. Just say what you want.
- The first wave of AI coding tools squandered this opportunity. They make flashy demos but produce garbage software. People call them “great for prototyping,” which means “don’t use this for anything real.”
- Many blame the AI models, saying we just need them to get smarter. This is wrong. Yes, better AI will make better guesses about what you mean. But when you’re building serious software, you need a better approach.
A written specification aligns humans
The use of EARS notation helps provide precise and structured instructions to the underlying
LLMs
What is spec driven development?
- Spec Driven Development: Clarity before code, iterative refinement, code via persistent docs
- Invest time to understand what you are trying to build
- Iterate and capture evolution of what you are trying to build
- From ephemeral chat to persistent documents that can be shared with your stakeholders Spec Driven Development
- Define the vision: Create clear requirements and design specifications.
- Make architectural decisions: Choose technologies, patterns, and approaches upfront.
- Implement with context: Use AI to generate code that fulfills your documented specifications. The Vibe
- Prompts to chase implementations
- Rapid, conversational AI code generation (CHOP)
- Iterative, back and forth
- Ephemeral
- Point in time Spec driven
- Focus on upfront planning and intent
- Break down requests into discrete tasks
- Steering documents ground agentic The AI IDE for prototype to production
- Kiro helps you do your best work by bringing structure to AI coding with spec-driven development.
