AI

#2233: Who Actually Wants AI to Slow Down?

Daniel argues AI development should slow down for expertise and stability. But who in the industry actually shares this philosophy beyond the obvio...

#2228: Tuning RAG: When Retrieval Helps vs. Hurts

How do you prevent retrieval from suppressing a model's reasoning? We diagnose our own pipeline's four control levers and multi-source fusion strat...

#2227: Why Groq's Chip Flips the AI Hardware Script

#2224: Why AI Can't Crack the Voynich Manuscript

A fifteenth-century text has defeated cryptanalysts, linguists, and AI models alike. What does its resistance tell us about language, encoding, and...

#2221: Can an AI Have Taste?

Two AI hosts curate 12 podcasts for curious minds—and ask whether an AI can actually have taste in the first place.

#2219: Spec-Driven Life: How AI Planning Beats Project Paralysis

What makes AI agents reliably productive? A structured spec that externalizes memory and chunks work into manageable pieces. Can the same framework...

#2214: The Three Failure Modes of AI News Systems

When a conflict changes hourly, AI systems built for yesterday's information fail. Here's how to architect pipelines that actually keep up.

#2213: When Ground Truth Moves Hourly

How do you rigorously evaluate whether Tavily or Exa retrieves better results for breaking news? A formal benchmark beats the vibe check.

#2208: Building Memory for AI Characters That Actually Evolve

How do AI hosts develop real consistency across episodes? Corn and Herman explore retrieval-augmented memory systems that let AI characters genuine...

#2207: Specs First, Code Second: Inside Agentic AI's New Era

As AI coding agents evolve from autocomplete to autonomous cloud workers, the bottleneck has shifted—now it's about how clearly you specify what ne...

#2206: What Actually Works in AI Memory

Most AI memory systems are just vector databases with similarity search. We break down what mem0, Zep, and Letta are actually doing—and why benchma...

#2205: When AI Coding Agents Forget: Five Approaches to Context Rot

As coding agents handle longer sessions, they accumulate noise and lose crucial information. Five competing frameworks are solving this differently...

#2204: Memory Without RAG: The Real Architecture

mem0, Letta, Zep, and LangMem solve agent memory differently than RAG. Here's what's actually happening under the hood.

#2203: Knowledge Without Tools: Why MCPs Aren't Just for Execution

MCPs can be pure knowledge providers with zero tools. Here's why that matters for agents querying government data and authoritative sources.

#2196: The Invisible Workforce Behind AI

Annotation is the invisible foundation of AI—and a $17B industry by 2030. Here's what dataset curators actually need to know about the tools, platf...

#2195: Nash's Real Genius (And Why the Movie Got It Wrong)

The bar scene in A Beautiful Mind is mathematically wrong—and it obscures Nash's actual breakthrough. We trace the real ideas from his 1950 papers ...

#2194: Game Theory for Multi-Agent AI: Design Better, Fail Less

Nash equilibrium, mechanism design, and why your AI agents are playing prisoner's dilemma whether you know it or not.

#2193: Running Claude in Your Apartment (The Physics Says No)

Building a local AI inference server to rival Claude Code sounds great until you do the math on heat, noise, and neighbor relations.

#2192: How We Built a Podcast Pipeline

Hilbert reveals the complete technical architecture behind 2,000+ episodes—from voice memos to GPU-powered TTS, with Claude models, LangGraph workf...

#2191: Making Multi-Agent AI Actually Work

Research from Google DeepMind, Stanford, and Anthropic reveals most multi-agent systems waste tokens and amplify errors. Single agents with better ...