Multi Agent Systems
267 episodes · Page 5 of 12
#2478: MCP File Handling: Why Your Base64 Upload Breaks at 4MB
MCP has no standard file input. Base64 breaks at 4MB, presigned URLs need whitelisting, and MinIO workarounds aren't standardized.
#2461: How Claude Code's Conversation Compaction Actually Works
The three-tier system, what survives, what dies, and why you shouldn't rely on auto-compact.
#2425: Can One Button Solve Your Streaming Frustrations?
A deep dive into JustWatch, Trakt, Letterboxd, and why your ideal streaming app doesn't exist yet.
#2403: Choosing Your LLM Eval Framework
An architectural shootout of four major LLM evaluation harnesses — where each shines and where each breaks down.
#2400: Claude Code’s Hidden Context Tax
How Claude’s eager-loaded primitives silently consume context—and how to optimize your setup for sharper performance.
#2356: Why AI Coding Needs Two Brains
Discover how specialized fast apply models streamline AI-powered code edits, cutting costs and latency while maintaining precision.
#2328: How to Spot a Real AI Hackathon
Discover how to identify worthwhile AI hackathons, build meaningful connections, and maximize your impact in virtual communities.
#2278: Visual Programming's Enduring Tradeoff
Why do visual programming tools keep resurfacing—and why do power users keep hitting their limits?
#2254: How to Test an AI Pipeline Change
When you tweak one part of a complex AI agent system, how do you know if it actually improved anything? The answer lies in engineering checkpoints.
#2253: Why AI Agents Get Three Steps, Not Infinity
Why do AI agents get exactly three rounds of tool use? It's a critical guardrail against infinite loops and runaway costs, not a limit on intellige...
#2251: Agent-to-Agent Protocols: What Actually Needs Standardizing
When autonomous agents call other agents, what does a working protocol actually require? Exploring session handling, state management, security, an...
#2249: Building Custom Benchmarks for Agentic Systems
Public benchmarks fail for agentic systems. Learn how to build evaluation frameworks that actually predict production behavior.
#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...
#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...
#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...
#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.
#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.
#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 ...
#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...
#2184: The Economics of Running AI Agents
Production AI agents can cost $500K/month before optimization. Learn model routing, prompt caching, and token budgeting to cut costs 40-85% without...
#2182: Can You Actually Review an AI Agent's Plan?
Most AI agents have plans the way you have a plan while half-asleep—something's happening, but you can't see it. We map the five major planning pat...
#2181: When RAG Becomes an Agent
RAG in chatbots is simple retrieval. RAG in agents is a multi-step decision loop. Here's what actually changes.