Systems | Development | Analytics | API | Testing

How do you solve problems that nobody reports? In this episode, Robin-Yann Storm, tool design director and former developer at IO Interactive, Guerrilla Games, and Apple, shares how a small usability issue with a 3D transform gizmo led to a workflow improvement that developers still use years later, saving thousands of hours. The discussion looks beyond the solution itself to examine the process of discovering the problem, understanding its root cause, and exploring how to solve it. Robin-Yann explains why observing users is often more valuable than collecting feature requests and how small UX improvements can deliver outsized returns.

10x faster coding. Same QA. Until now. You're coding faster with AI. But if your agents are siloed and disconnected, those gains aren’t yet showing up in your quality or delivery – it's getting lost in the handoffs. Join SmartBear for a live demonstration of the connected agentic SDLC. See how BearQ extends beyond creating tests to coordinating quality work across Jira, GitHub, and AI-powered workflows – detecting issues, triggering the right next action, and carrying context from one agent to the next.

Kong Context Mesh composes APIs, data, and documents into a single MCP interface that AI agents can call directly, with no custom harness or extra instructions. Built on Kong Konnect, it supports different authentication strategies and data structures, and avoids context window bloat through progressive disclosure and on-the-fly execution. Agents discover tools just in time and retrieve only the context needed for the task. The demo connects internal APIs and external MCP servers, then uses Claude Code to complete a multi-source task.

Your Kafka consumer is lagging, logs are full of deserialization errors, and it's 2 AM. A producer team deployed a schema change without telling you, and now your consumer can't parse the data. This is one of the most common production incidents -- and it's completely preventable. In this clip, watch as Marcus, the on-call engineer, uses Lenses to quickly diagnose a real P1 incident caused by upstream schema incompatibility. He compares message payloads side-by-side, detects the breaking schema change, and has the fix deployed in seconds.

Before building an AI-powered real estate product, understand the data that will power it. In this episode of The Innovation Blueprint Podcast, Roman Havrylyuk of ORIL and David Lovins, CEO of The Warren Group, explore why understanding your data — where it comes from, how it’s structured, and how different datasets connect — should come before building AI solutions. The better you understand your data, the better equipped you are to build technology that delivers reliable, meaningful results.