Most AI content stops at the prototype. These posts are about what comes after: tools that are reliable, affordable, and safe enough for real users. A lot of this work uses the Model Context Protocol (MCP), which changed how I connect models to real data and tools.
You’ll find end-to-end MCP server builds, notes on Amazon Bedrock and RAG, and lessons on cutting token costs, controlling model output, and keeping AI systems secure. I write from a builder’s seat. Every guide here comes from something I shipped, including where it went wrong.
Articles in this topic
- Identity-Aware SRE Agents with kagent on Akamai LKE Aug 2026
- Building an Agent on Amazon Bedrock AgentCore: End-to-End Notes Apr 2026
- Build a Semantic Cache with AWS Services (S3 Vectors + Bedrock) Jan 2026
- Designing AI Agent Tools: Cut Token Costs 70% (MCP Case Study) Jan 2026
- AWS DevOps Agent: AI-Powered Incident Investigation Dec 2025
- Docker MCP Catalog and Toolkit: Simpler AI Agent Integrations Oct 2025
- Build a Bible MCP Server: A Complete Custom AI Tool Guide Aug 2025
- How to Get Consistent AI Results: 7 Parameter Controls Jul 2025
- Streamline Location-Relevant Answers with SharePoint, Amazon Nova and Bedrock Apr 2025
- Docker Model Runner: Run AI Models Locally Apr 2025
- DeepSeek vs OpenAI: How the AI Race Is Heating Up Feb 2025
- Kubernetes Sidecar Containers: Beyond the Basics Dec 2024
- Intelligent Kubernetes Event Summarizer: A Step-by-Step Guide with a Demo Nov 2024
- Leveraging eBPF for Container Network Monitoring with Cilium Oct 2024
- Building a Unified Bible Platform: Q&A, Insights, and Ministry Matching Oct 2024
- Automating Code Reviews with GitLab CI/CD and Ollama May 2024
- AI-Driven ServiceNow Incident QnA Bot using Amazon Bedrock Jan 2024
- Revolutionize Chatbots with Pinecone OpenAI & Custom Data Aug 2023
- AI-Powered Data Parsing for Smart Answers Jul 2023