#model-context-protocol
62 episodes · Page 2 of 3
#2469: Embedding Model Deprecation: RAG's Silent Killer
When OpenAI retires an embedding model, your RAG pipeline breaks silently. Here’s how to fix it.
#2441: When One Sentence Beats Four Clicks
What happens when you ditch the admin panel and let AI agents manage your systems directly?
#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.
#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.
#2314: One Model or Three? Inside Claude's Architecture
What makes Claude’s Haiku, Sonnet, and Opus different? Discover how architecture shapes their unique strengths and weaknesses.
#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.
#2167: Sync vs. Async: Architecting Agents for Scale
Why most enterprise AI agents fail in production has less to do with models and more to do with whether they're built synchronously or asynchronously.
#2075: AI Agents for Israel: Hyper-Local Skills in Action
How reusable AI "skills" are solving real Israeli problems—from shelter navigation to tax compliance.
#2039: CLIs vs. MCPs: How AI Agents Actually Talk to Services
Why give an AI agent a terminal? We compare CLIs and MCPs for AI integration.
#2021: Your Frozen AI Is Getting Smarter (Here's How)
Your AI model might be static, but the system around it can make it learn in real-time.
#2014: Coding Tools Are Secretly System Agents
They call it a coding assistant, but real users are treating it like a personal operating system.
#1945: The "USB-C for AI" Is Finally Here
MCP standardizes how AI tools connect to data, solving the N-times-M integration nightmare.
#1906: Is Your AI Model Agentic-Ready or Just Wearing a Suit?
Native tool calling is the difference between a working product and a debugging nightmare.
#1858: Multi-Model Agents: The Instruction & Context Gap
Mixing AI models creates chaos. Learn the practical fixes for context windows, tokenization, and output formats.
#1857: The Death of the Dashboard
Why build a dashboard when you can just talk to your backend? Meet the MCP server that runs this show.
#1846: Right-Sizing Your Agent's MCP Toolkit
AI agents slow down when overloaded with tool schemas. Just-in-time usage is the fix.
#1834: Owning Your AI Memory: The Data Exit Strategy
Why your AI remembers your coffee order but forgets your son’s name—and how to build a portable, federated memory layer you actually own.
#1832: From Local Chaos to Cloud Control
Local MCP servers are a configuration nightmare. Cloud aggregators like Composio offer a unified control plane for AI tools.
#1812: When AI Gets a Truth Tether to the Talmud
Sefaria's new MCP server connects AI directly to 2,700 years of Jewish texts, transforming how scholars and curious learners study ancient literature.
#1731: Why Deep Research Agents Are Being Forgotten
Specialized research agents outperform general orchestrators by 40-60% on verification tasks, yet developer hype is fading. Here's why.
#1652: AI Gateways: The Nginx for Your AI Stack
Why agentic AI needs a unified control plane to route models, aggregate tools, and cut costs.
#1620: Why VRAM Is the Wrong Way to Measure Your AI PC
Forget VRAM—bandwidth is the new king. Discover why your local AI feels slow and how to build a true "agent computer" for professional coding.
#1618: The Rise of AI Microservices: Beyond the Mega-Prompt
Say goodbye to mega-prompts. Explore the shift toward modular AI microservices, agentic hierarchies, and high-signal control artifacts.
#1612: The End of Apps: Why Agents Are Replacing Your Desktop
Is the era of the app over? Explore how AI agents are transforming operating systems from static tools into proactive digital partners.