#ai-agents
388 episodes · Page 5 of 17
#2441: When One Sentence Beats Four Clicks
What happens when you ditch the admin panel and let AI agents manage your systems directly?
#2440: Build Your Own CRM With AI Agents
Off-the-shelf CRMs are built for sales teams, not solo operators. Here's why building your own with AI might be smarter.
#2439: AI Collapses the Framework Decision
Why Airtable fails for multi-user tools, and how AI builders are changing the framework decision for small businesses.
#2404: What Tool-Calling Benchmarks Miss About Production Failures
BFCL, tau-bench, and Nexus each reveal different failure modes. None of them test what actually kills production agents.
#2403: Choosing Your LLM Eval Framework
An architectural shootout of four major LLM evaluation harnesses — where each shines and where each breaks down.
#2361: The Cognitive Load of Logs
How Claude Code transforms Linux system administration by automating log analysis and proactive maintenance.
#2340: How AI Models Track a Ship Seizure’s Ripple Effects
When the US seized an Iranian cargo ship, three AI models reshuffled their predictions overnight. Here’s what they saw—and where they disagreed.
#2336: How ADRs Solve AI's Institutional Memory Problem
Architectural Decision Records (ADRs) aren’t just documentation—they’re a way to give AI coding assistants the context they lack.
#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.
#2297: How to Scrape Geo-Restricted Israeli Sites with MCP Tools
Learn how to bypass advanced bot-protection on Israeli websites using MCP tools, residential IPs, and tunneling techniques.
#2274: Weekend Projects Gone Wild: Evaluating AI Startup Pitches
From fridge tax agents to guilt-scheduled cron jobs, we evaluate ten AI-driven startup ideas that could exist—but probably shouldn’t.
#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.
#2242: AI as Your Ideation Blind Spot Spotter
How to use AI not to answer questions you already know to ask, but to surface possibilities your expertise has made invisible to you.
#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...
#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.
#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.
#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...