AI Core
Fundamentals of AI models, architecture, and how they work
221 episodes · Page 9 of 10
#1784: Context1: The Retrieval Coprocessor
Chroma's new 20B model acts as a specialized "scout" for your LLM, replacing slow, static RAG with multi-step, agentic search.
#1779: AI Memory Is a Mess: Files, Vectors, or Cloud?
Why your AI forgets your instructions and what the battle over portable memory means for the future of agents.
#1777: Claude Called My Prompt "Rambling" and I'm Not Okay
When an AI coding tool critiques your prompt's literary quality, it raises a massive technical question about engineered personality.
#1765: The Agentic Internet: A Clean Web for Machines
We explore the tools building a parallel, machine-readable web—from SearXNG to Tavily.
#1764: Your Repo as a Knowledge Base
How to give AI agents instant memory of your entire project—without cloud costs or complex infrastructure.
#1762: Testing AI Truthfulness: Beyond Vibes
Stop trusting confident AI. We explore the formal science of testing LLMs for hallucinations and knowledge cutoffs.
#1753: AI Makes Coding Harder, Not Easier
Claude Code writes the syntax, but you need more technical knowledge than ever to guide it.
#1740: Why Open Source Is a Power Tool Strategy
We dissect Resemble AI's Chatterbox to see how its open-source TTS compares to commercial giants like ElevenLabs.
#1739: AI Just Designed a New Life Form
Meet Evo: the 40B parameter AI that writes DNA, designs novel CRISPR systems, and is reshaping synthetic biology.
#1737: Nous Research: The Decentralized AI Lab Beating Giants
Meet Nous Research, the decentralized collective outperforming billion-dollar labs with open-source AI and the self-improving Hermes-Agent framework.
#1736: The Hidden AI Economy: Following the Tokens
OpenClaw is processing 16.5 trillion tokens daily, dwarfing Wikipedia. Here’s why it’s #1.
#1734: You vs. Your Digital Twin: Who Wins?
Your AI clone is getting scarily good. We explore the tech behind high-fidelity digital twins and the uncanny valley of your own voice.
#1733: When AI Agents Build Their Own Societies
AI agents are forming neighborhoods, economies, and hospitals in server-side simulations that mirror real human behavior.
#1732: Why AI Agents Need an Operating System
AIOS aims to be the Linux for AI agents, managing memory, scheduling, and tools in one open-source kernel.
#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.
#1730: Are Multi-Agent Coding Frameworks Obsolete?
MetaGPT, SWE-agent, and OpenHands promised a team of AI devs. But in 2026, are they still useful, or has raw model power made them obsolete?
#1729: Why Is AI Code So Hard to Read?
AI writes code faster than ever, but the output is often a cryptic mess. We explore why and how to fix it.
#1728: The AI Carpool: Emergent Collaboration Through Role-Playing
CAMEL AI lets two agents role-play to solve tasks autonomously. No complex code—just emergent teamwork.
#1727: The Great Architectural Heist: LSP as AI's Universal Plumbing
Explore how the Language Server Protocol is being repurposed to integrate AI directly into code editors, unifying development workflows.
#1723: Why Agentic AI Needs a Hive Mind, Not a Single Brain
The single monolithic AI model is dying. Meet the new native multi-agent architectures that think like a team, not a solo genius.
#1717: The AI Framework Name Game
Why are there thousands of "AI frameworks" on GitHub? We unpack the naming mess and the cost of semantic inflation.
#1713: Why Native AI Search Grounding Still Fails
Native search grounding is expensive and flaky. Here’s why bolt-on tools still win for accurate, real-time AI answers.
#1710: Two Hundred Years of Calling Sloths "Miserable Mistakes"
Why did early naturalists mistake sloths for bears, monkeys, and giant rats?
#1709: Standard Deviation: The Map Without a Scale
Why the average number alone is misleading—and how standard deviation reveals the true story behind the spread.