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The Everything Trap: Building AI Software That Lasts with Sam Hilsman
Generating working software with AI has never been easier, but every shortcut a team takes today is a debt it pays back later. 

In this episode, David talks with Sam Hilsman, co-founder and CEO of CloudFruit, a studio that builds ERPs, analytics platforms, and custom products — including...

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Why Inference Engineering Is the Next Big Role in AI with Philip Kiely
​Most teams moving AI into production quickly discover that generating an output is the easy part; running it reliably, efficiently, and at scale is a discipline of its own.In this episode, David talks with Philip Kiely, engineer at Baseten and author of

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Distributed Systems, Linkerd, and the Cost of Network Calls with William Morgan from Buoyant
In Kubernetes environments, concerns like retries, security, observability, and traffic control often end up scattered across application code, leading to duplicated logic, inconsistent behavior, and systems that are harder to operate and debug. The service mesh was designed to solve exactly that.

In this episode, David talks with William Morga...

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Making Software as Durable as Data with Peter Kraft from DBOS
Most workflow and orchestration tools rely on external systems and fragile state management, making it hard for applications, especially AI agents to recover from failures or long running interruptions.

In this episode, David talks with Peter Kraft, co-founder of DBOS, about a new approach to durable execution. DBOS is a lightweight, database-backed l...

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Breaking the Pillars: Rethinking Observability with Charity Majors
Most observability platforms are built around siloed signals, metrics, logs, and traces; not the rich context engineers actually need. The architecture of observability tooling hasn’t kept pace with the architecture of today’s distributed systems. 

In this episode, David sits down with Charity Majors, co-founder and CTO of Honeycomb.io and co-author o...

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