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#ai-reasoning

47 episodes

#3816: How to Stop AI Scripts From Falling Apart

Why long-form AI generation breaks down and how hierarchical memory fixes it.

large-language-modelscontext-windowai-reasoning

#3814: The Day We Lost Our Minds: What Temperature Does to an AI

A two-host autopsy of the day the podcast's AI hosts briefly lost coherence due to excessive sampling temperature, and what it reveals about how language models actually work.

large-language-modelsai-reasoninghallucinations

#2780: Building Self-Healing Agent Pipelines

How to build an agent that monitors and fixes other agents in production — without the hype.

ai-agentsai-reasoningfault-tolerance

#2693: When AI Ignores Your Style Guide

Why your AI ignores formatting instructions and how to fix it with pipeline architecture, not model swaps.

prompt-engineeringfine-tuningai-reasoning

#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.

model-context-protocolai-reasoningcontext-window-tax

#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.

geopolitical-strategyai-reasoninginternational-relations

#2241: When More Frameworks Make Worse Decisions

Benjamin Franklin's 250-year-old pro/con list still dominates how we decide—but research shows it's riddled with bias. We map five frameworks that ...

human-factorsproductivityai-reasoning

#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.

benchmarkstraining-dataai-reasoning

#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...

cryptographylinguisticsai-reasoning

#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 ...

ai-agentsprompt-engineeringai-reasoning

#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...

ai-agentsai-reasoningai-safety

#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...

ai-agentsai-reasoninghuman-computer-interaction

#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...

prompt-engineeringreasoning-modelsai-reasoning

#2173: Inside MiroFish's Agent Simulation Architecture

MiroFish generates thousands of AI agents with distinct personalities to predict social dynamics. But research reveals a critical flaw: LLM agents ...

ai-agentsknowledge-graphsai-reasoning

#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?

large-language-modelsai-reasoningai-alignment

#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...

context-windowai-reasoningai-memory

#2024: Your AI Council: Digital Committee or Groupthink?

A digital boardroom of AI models promises better decisions, but risks amplifying the same old biases.

ai-agentsai-reasoningai-ethics

#2016: Andrej Karpathy: The Bob Ross of Deep Learning

Why the most influential AI mind prefers a blank text file to proprietary black boxes.

ai-trainingopen-source-aiai-reasoning

#1894: Engineering Serendipity: Tuning AI for Better Brainstorming

Stop asking chatbots for generic ideas. Learn how to configure AI as a structured, critical partner for business innovation and career pivots.

ai-agentsprompt-engineeringai-reasoning

#1893: AI as a Strategic Adversary for Startups

Can AI stress-test your startup idea before investors do? We explore using AI as a strategic adversary to find blind spots.

ai-agentsai-reasoningstartups

#1838: Tuning Search Without Losing Your Mind

Modern search bars are AI decision engines. Here's how small teams can tune fuzzy matching, semantic search, and reranking without breaking everyth...

ragvector-databasesai-reasoning

#1668: Kimi K2's Hidden Reasoning: A New AI Architecture

Moonshot AI's Kimi K2 Thinking model uses a hidden reasoning phase to solve complex logic puzzles and coding tasks, beating top proprietary models.

ai-reasoningopen-source-aiai-models

#1633: Can a Character Actor Model Beat a Generalist?

We grill MiniMax M2.7 to see if a model built for "virtual companions" can actually handle high-level comedy and complex character logic.

ai-agentsai-reasoningtransformers

#1630: When a Reasoning Model Overthinks Comedy

Xiaomi’s new MiMo 2.0 Pro model auditions for a comedy podcast, promising deep reasoning over raw speed.

ai-agentsprompt-engineeringai-reasoning