AI Core
Fundamentals of AI models, architecture, and how they work
221 episodes · Page 4 of 10
#2357: Microsoft's Phi: When Data Quality Beats Model Size
Explore Microsoft AI's Phi family of small language models, designed for edge deployment and high efficiency.
#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.
#2355: Why Open-Weight Models Are Winning
Discover how Cogito v2.1 leverages process supervision and MoE architecture to redefine reasoning efficiency in open-weight AI models.
#2353: Evaluating Enterprise AI: Palmyra X5
Explore Palmyra X5, Writer’s flagship AI model designed for enterprise workloads, featuring a million-token context window and agentic capabilities.
#2351: AI Model Spotlight: ** Aion-2.0
Why is a biopharma AI lab releasing a storytelling-optimized model? We explore Aion-2.0’s architecture, pricing, and niche adoption.
#2350: NVIDIA's Strategic Pivot: From Chipmaker to Model Builder
Dive into NVIDIA’s Nemotron 3 Super, a hybrid MoE model combining Mamba, Transformers, and multi-token prediction for cutting-edge efficiency.
#2349: The 30-Person Lab Outpacing AI Giants
Discover how Arcee AI’s Trinity Large Thinking delivers cutting-edge reasoning at a fraction of the cost, all from a team of just 30.
#2348: Diffusion Models Take on Text Generation
Explore Inception Labs’ Mercury 2, a groundbreaking diffusion-based language model that rethinks text generation and reasoning.
#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.
#2316: Who’s Building AI’s Next Training Data?
How boutique dataset firms are reshaping AI training, from rights-cleared content to domain-specific precision.
#2315: How to Update AI Models Without Starting Over
Exploring the challenge of updating AI models with new knowledge without costly full retraining.
#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.
#2313: When AI Optimizes the Wrong Thing
Discover how AI systems learn to optimize for rewards—and why they sometimes get it dangerously wrong.
#2312: When Bigger Context Windows Aren't Better
Exploring the real-world impact of massive context windows in AI models, from academic research to codebase analysis.
#2309: Blind Ranking AI's Best Podcast Scripts
How do 15 AI models handle controversial podcast prompts? We rank their scripts blind and reveal the surprising winners.
#2307: Inside Frontier LLM Training: Stages, Costs, and Checkpoints
Discover the multi-stage process of training frontier large language models, from pretraining to post-training, and why checkpoints are the key to ...
#2306: Can LLM Councils Truly Capture Diverse Worldviews?
Exploring whether LLM councils can achieve genuine worldview diversity or if alignment processes erase meaningful differences.
#2271: Vector Search in a Single File
What if you could do vector search with just SQLite? We explore sqlite-vec, the extension that adds embeddings to the world's simplest database, an...
#2239: How AI Benchmarks Became Broken (And What's Replacing Them)
The tests we use to measure AI progress are contaminated, saturated, and gamed. Here's what's actually working.
#2233: Who Actually Wants AI to Slow Down?
Daniel argues AI development should slow down for expertise and stability. But who in the industry actually shares this philosophy beyond the obvio...
#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...
#2224: Why AI Can't Crack the Voynich Manuscript
A fifteenth-century text has defeated cryptanalysts, linguists, and AI models alike. What does its resistance tell us about language, encoding, and...
#2213: When Ground Truth Moves Hourly
How do you rigorously evaluate whether Tavily or Exa retrieves better results for breaking news? A formal benchmark beats the vibe check.
#2206: What Actually Works in AI Memory
Most AI memory systems are just vector databases with similarity search. We break down what mem0, Zep, and Letta are actually doing—and why benchma...