AI Core
Fundamentals of AI models, architecture, and how they work
221 episodes
#3816: How to Stop AI Scripts From Falling Apart
Why long-form AI generation breaks down and how hierarchical memory fixes it.
#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.
#3767: How LLMs Actually Learn: Stages or Slurry?
Do large language models learn grammar first, then facts? The honest answer is messier and more fascinating.
#3751: Source-Restricted vs. Open Retrieval: How to Lock Down Your LLM
When should an LLM be locked to specific documents, and when should it search the web? A practical framework for grounding decisions.
#3673: Knowledge Graphs vs SQL: How Custom Relationships Change Retrieval
Why naming relationships (not just connecting data) transforms how you retrieve information.
#3596: Why an AI Model Kept Calling Itself Sonnet 4.6
When a Chinese model insists it's "Sonnet 4.6," is it theft, sloppy training, or something stranger?
#3595: How DeepSeek Feels More Open Than Western AI
Why Chinese AI models sometimes feel less censored on American political topics than American models do.
#3406: LoRA Isn’t Just for Image Generation
LoRA lets you fine-tune an LLM’s behavior with a 50MB file. Here’s how it works and why it matters.
#3283: Fine-Tuning DeepSeek for One Podcast
Can a purpose-specific fine-tune fix a model's stubborn writing tics? We explore the practical engineering behind it.
#3278: How to Get Early AI Model Access as a Solo Developer
How a solo developer spending $300/month can get early access to new AI models before the press release.
#3271: LLMs as Parsers, Not Calculators
Stop letting LLMs do math. Use them to parse messy text, then let deterministic code handle the numbers.
#3171: How to Break an LLM's Bad Verbal Habits
Blacklists fail and regex inverts meaning. Here's what actually works to clean up AI writing tics.
#3170: Pharmacokinetics vs Neural Nets: Two Meanings of "Model
Two things called "models" that work completely differently — and why the confusion matters for patient safety.
#3157: Opus 4.8: What Actually Changed Under the Hood
Anthropic dropped Opus 4.8 with no fanfare. New training data, faster inference, and smarter refusals — here's what changed.
#3098: The Pilot with the Flashlight: Inside Aviation's Pre-Flight Walkaround
Why pilots still physically inspect planes before every flight — and what a 1979 crash taught us about trusting machines.
#3067: How Glow-in-the-Dark Actually Works
The atomic-level physics behind phosphorescence and why oil-based glow markers don't exist.
#3038: Animating Toy Story: Math, Patience, and No Undo Button
Before Pixar could make Woody blink, animators typed coordinates by hand and waited hours to see if it worked.
#2923: Structured Outputs: Taming AI's Token Lottery
Why prompt engineering isn't enough to get consistent JSON from LLMs.
#2883: Correlation Beyond Pearson: 5 Techniques You Need
Pearson, Spearman, Kendall, partial, distance correlation — when to use each one and why most people stop too soon.
#2855: How Medieval Hebrew Became Israel's Handwriting
The surprising 800-year history of how Ashkenazi cursive became the handwriting taught in Israeli schools today.
#2810: Every Catalog Is an Argument
From clay spine labels at Ebla to the Pinakes of Alexandria — how organizing knowledge shaped civilization.
#2779: The Hidden Stateful Side of Serverless GPU
How Modal, RunPod, and other platforms handle container builds, caching, and versioning under the hood.
#2777: GPU Idle Waste and Serverless Green Computing
Why your dedicated GPU burns 130 watts doing nothing, and how serverless platforms cut energy waste by more than half.
#2741: What Theoretical Physicists Actually Do All Day
Chalkboards, arXiv firehoses, and 2 hours of real work. What the daily life of a theoretical physicist actually looks like.