Development
Building software: programming, developer tools, APIs, deployment and DevOps, and building with AI (agents, MCP servers, RAG, AI coding tools). An extra channel alongside an episode's main one, so AI engineering episodes sit in both.
#2016: Andrej Karpathy: The Bob Ross of Deep Learning
Why the most influential AI mind prefers a blank text file to proprietary black boxes.
#2014: Coding Tools Are Secretly System Agents
They call it a coding assistant, but real users are treating it like a personal operating system.
#2013: Non-Coders Are Hijacking the Terminal
Why finance analysts and researchers are ditching GUIs for command-line AI tools like Claude Code.
#2012: Pixels vs Protocols: The Computer Use Showdown
Is visual AI a bridge or the future? We debate the efficiency and longevity of "Computer Use" agents versus API-first automation.
#2011: Saving AI Knowledge Beyond the Chat Window
We're brilliant at prompting AI, but terrible at saving the answers. Here's why that "digital masterpiece on a chalkboard" vanishes.
#2010: Building Better AI Memory Systems
We obsess over AI inputs but treat outputs like Snapchat messages. Here's why that's a massive blind spot.
#2009: The Plumbing of AI Safety: Guardrails, Not Vibes
We dive deep into the specific libraries, proxy layers, and architectural decisions that keep an LLM from emptying a bank account.
#2008: Needle-in-a-Haystack Testing for LLMs
New AI models claim to be genius-level, but can they actually find a specific fact in a massive document?
#2007: AI Grading AI: The Snake Eating Its Tail
We asked an AI to write this script. Then we asked another AI to grade it. Here’s what happens when the judges have biases.
#2006: How Do You Measure an LLM's "Soul"?
Traditional benchmarks can't measure tone or empathy. Here's how to evaluate if an AI model truly "gets it right."
#2004: The AI Control Plane Is Here (But Is It Safe?)
Your LLM, tools, and costs are scattered across dashboards. Here’s how a unified AI control plane fixes the chaos.
#2003: The Velocity Paradox: Why Faster Code Means Slower Ships
Agentic coding tools let you build features in minutes, but they also make it easy to build the wrong thing.
#2002: Brainstorming a Stable-by-Design Smart Home
We explore why Home Assistant is so fragile and brainstorm a stable-by-design future for the platform.
#2001: Stop Writing "It Feels Slow" Tickets
The "Golden Trio" of bug reports, why Jira is a tax, and how AI capture tools are changing the game.
#1994: Why Can't AI Admit When It's Guessing?
Enterprise AI now auto-filters low-confidence claims, but do these self-reported scores actually mean anything?
#1993: Hiding the Kitchen: Why AI Shouldn't Show Its Work
Why single-model chatbots fail at complex tasks—and how multi-agent swarms solve it.
#1963: RPA: Dead or Just Getting Smart?
Traditional RPA is brittle and blind. See how AI vision and agentic orchestration are turning it into a self-healing powerhouse.
#1959: How Constrained AI Models Handle the Unexpected
Your AI assistant promised to only use your documents. Instead, it invented a case law that doesn't exist. Here's why.
#1957: Why AI Agents Think in Circles, Not Lines
Linear AI pipelines are brittle. Learn why loops, reflection, and state management are the new standard for reliable, autonomous agents.
#1956: AI Skills: From Vibe Coding to Procedural Playbooks
Forget messy system prompts. Agent skills turn AI into a Swiss Army knife of modular, auditable procedures.