Reasoning Models
227 episodes · Page 4 of 10
#5301: Why Talking Robots Are Really a Committee
That humanoid chatting while it moves? It's not one brain — it's a stack of separate models glued together.
#5256: Ruby Won the AI Benchmark. Is It Dying Anyway?
97% of public Ruby repos sat untouched for six months — yet Ruby beat Python in an AI coding benchmark. What's actually declining?
#5230: Why Your Research Agent Won't Stop Searching
Most agent search runs add no new evidence — and 66-85% of the context window is just search snippets. Here's what actually stops the loop.
#5188: DeepSeek's Point Release That Isn't
DeepSeek shipped a whole new architecture and called it a point release. Here's what actually changed inside the model.
#5184: AI Is a Number Factory, Not a Wordsmith
Under the prose, every AI model is just matrices of floating-point math. So where does the randomness actually come from?
#5182: DeepSeek V4.1 Flash: 1M Context, 437x Smaller KV Cache
DeepSeek V4.1 Flash landed with a 1M-token window and a KV cache 437x smaller than V1. Here's what actually changed — and why the middle of your co...
#5165: Weighting Memory in a RAG Pipeline
Daniel's memory leg won't fire. Turns out system prompts have no inherent vector-space privilege — but retrieval does have a real knob.
#5118: Model Identity Fraud: Distillation or Data Contamination?
When AI models lie about who they are, is it stolen knowledge or just contaminated training data?
#5041: GPT-6 Astra, 100K GPUs, and the AGI Question
OpenAI's GPT-6 Astra arrives with an AGI claim and a 100K-GPU training run. We break down what Stargate LLC actually is and whether the scale justi...
#5012: Masked Safety: How Post-Training Changes AI Behavior
Safety mechanisms aren't erased in post-trained models—they're masked. Here's how that changes everything for military AI.
#4834: Why Your AI Bill Keeps Surprising You
How a 37-cent project and a $100 simulation reveal the hidden math of AI API pricing.
#4786: Markdown vs Vectors for Agent Memory
Why coding agents need two kinds of memory — and why mixing them creates a sync nightmare.
#4720: Compass vs. Warning Sign: Prompting for Long-Term AI
How positive and negative instructions reshape AI behavior differently — and why "preserve this" beats "fix this" in system prompts.
#4712: Codex vs Claude Code: The Real Agentic Coding Showdown
Benchmarks, model variants, and MCP setups — which coding agent actually wins where it matters?
#4696: Why AI Over-Explains Simple Tasks
Why does AI turn a simple request into a 24-page document? We explore the training biases and architectural limits behind AI's tendency to over-del...
#4686: How Claude Code's Patience Works: Fork, Wait, and the Transcript Stack
Claude Code's "waiting" is an illusion. We break down the Unix primitives behind its background task system and where the analogy cracks.
#4682: When Context Windows Grow: What Survives?
If context windows jump to 10M tokens, which AI engineering practices survive? We separate load-bearing architecture from temporary scaffolding.
#4681: Loops vs Workflows: Defining AI's New Unit of Work
What makes a Claude Code loop different from a prompt or workflow? We pin down the actual definition—and who owns the off switch.
#4589: The Answer Reflex: Why AI Models Can't Follow Instructions
DeepSeek passes a simple test that stumps GPT and Claude. Why can't Western models stay in character?
#4570: One Negative Prompt Beat a Dozen Positives — Here's Why
Why one blunt "don't" outperformed a dozen careful "do's" — and how to structure your system prompts for maximum compliance.
#4477: Structuring Raw Voice Prompts at Scale
How to decompose messy voice dictation into clean, routable components for AI workflows.
#4456: Inside the Podcast Pipeline: How 15 Weekly Episodes Get Made
From prompt to published episode — a full walkthrough of the automated production system running 15 shows weekly.
#4446: What Claude Fable 5's Suspension Reveals About AI Regulation
Three days after launch, the US government suspended Anthropic's Claude Fable 5. Here's what happened and why it matters.
#4347: Building an AI That Finds Your Blind Spots
Can LLMs surface the chain reactions you never see? A four-agent system for proactive preparedness.