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#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 ...
#2190: Simulating Extreme Decisions With LLMs
LLMs fail at the exact problem wargaming was built to solve—simulating irrational, extreme decision-makers. A new study reveals why.
#2189: Scaling Multi-Agent Systems: The 45% Threshold
A landmark Google DeepMind study reveals that adding more AI agents often degrades performance, wastes tokens, and amplifies errors—unless your sin...
#2188: Is Emergence Real or Just Bad Metrics?
The debate over whether AI models exhibit genuine emergent abilities or just appear to because of how we measure them—and why it matters for safety...
#2187: Why Claude Writes Like a Person (and Gemini Doesn't)
Claude produces prose that sounds human. Gemini reads like Wikipedia. The difference isn't capability—it's how they were trained to think about wri...
#2186: The AI Persona Fidelity Challenge
Advanced LLMs dominate benchmarks but fail at staying in character—especially when asked to play morally complex or antagonistic roles. What does t...
#2185: Taking AI Agents From Demo to Production
Sixty-two percent of companies are experimenting with AI agents, but only 23% are scaling them—and 40% of projects will be canceled by 2027. The ga...