Name the failure first, then pick the cheapest fix that clears your quality bar on Amazon Bedrock In this article Most founders ask this question in the wrong order....
A practical walk through of the managed harness: model, prompt, tools, limits and a clean path to code In this article Most first agents die in the plumbing. A...
The model is rarely the problem. These five gaps keep agent demos away from real customers. In this article Every founder we talk to has an AI agent demo....
Compare deployment speed, product control, security, costs, and engineering effort before choosing how to add an AI assistant to your application. Table of Contents What Are You Really Buying?...
How AI coding assistants can help new developers understand unfamiliar repositories, contribute safely, and become productive without replacing human mentorship. Table of Contents Why Onboarding Takes Time A new...
How to build useful AI systems with clear ownership, safer data practices, measurable quality, and controls that small teams can actually maintain. Table of Contents A startup can add...
Understand where AWS custom accelerators fit, what they cost, and when they make sense for production AI workloads AWS Inferentia and Trainium are purpose-built chips designed for machine learning...
Understand the AWS shared responsibility model and build a SOC 2-ready cloud environment without assuming AWS covers everything Moving your application to AWS can reduce a large part of...
A practical guide to writing clear, testable requirements that Kiro can turn into better designs and implementation tasks Kiro uses requirements.md as the starting point for its Spec workflow,...
A practical framework for monitoring AI quality, detecting performance changes, and responding before users notice a problem. AI systems can degrade without any obvious infrastructure failure. A model may...