Reasoning Models
227 episodes · Page 6 of 10
#2405: LLM Benchmarks Are Full of Noise: Statistical Rigor in AI Evals
Why most benchmark claims in AI are statistically indefensible — and what to do about it.
#2388: From Tool Picker to Problem Solver
Discover how OpenRouter intelligently routes your prompts to the most optimized AI model, reshaping how we interact with AI tools.
#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.
#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.
#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.
#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.
#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.
#2308: When AI Forecasts Collide: Geopol Model Divergence
Five AI models forecast the Iran-Israel-US crisis — and their disagreements reveal surprising insights about geopolitical reasoning.
#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...
#2208: Building Memory for AI Characters That Actually Evolve
How do AI hosts develop real consistency across episodes? Corn and Herman explore retrieval-augmented memory systems that let AI characters genuine...
#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.
#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 ...
#2187: Why Claude Writes Like a Person (and Gemini Doesn't)
Claude produces prose that sounds human. Gemini reads like Wikipedia. The difference isn't capability—it's how they were trained to think about wri...
#2186: The AI Persona Fidelity Challenge
Advanced LLMs dominate benchmarks but fail at staying in character—especially when asked to play morally complex or antagonistic roles. What does t...
#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...
#2177: Skip Fine-Tuning: Shape LLMs With Alignment Alone
Can you build a personalized LLM by skipping traditional fine-tuning and using only post-training alignment methods like DPO and GRPO? We break dow...
#2175: Let Your AI Argue With Itself
What happens when you let multiple AI personas debate each other instead of asking one model one question? A deep dive into synthetic perspective e...
#2172: Council of Models: How Karpathy Built AI Peer Review
Andrej Karpathy's llm-council uses anonymized peer review to make language models evaluate each other fairly—but can it really suppress model bias?
#2164: Why Bigger Context Windows Don't Fix Attention
Frontier models have million-token context windows, but attention degrades well before you hit the limit. New research reveals why bigger isn't bet...
#2163: Designing Autonomy Boundaries for AI Agents
Production data reveals a surprising truth: fully autonomous AI agents waste 98% of their context window on tool descriptions. Here's why the indus...