AI Core
Fundamentals of AI models, architecture, and how they work
221 episodes · Page 8 of 10
#1913: AI Context Windows Are Junk Drawers
Stop paying for old messages. Here's how to keep your AI sessions clean and on-topic.
#1910: Our Podcast Is Now a Permanent Research Artifact
Why we're uploading every episode to CERN's Zenodo archive, giving our AI experiments a permanent DOI and a life beyond streaming platforms.
#1909: The Unbakeable Cake: AI's Copyright Problem
Why can't we just delete stolen data from AI models? It's not a database—it's a baked cake.
#1907: Why We Still Fine-Tune in 2026
Despite million-token context windows, fine-tuning remains essential. Here’s why behavior, not just facts, matters.
#1906: Is Your AI Model Agentic-Ready or Just Wearing a Suit?
Native tool calling is the difference between a working product and a debugging nightmare.
#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.
#1882: The Hidden Human Labor Behind AI
AI isn't free—it costs billions for humans to label data. See why annotation is the real engine behind models like Gemini.
#1856: Two AIs Chatting Forever: Why They Go Crazy
What happens when two ChatGPT instances talk forever? They hit a politeness loop, forget their purpose, and spiral into gibberish.
#1849: When Forum Etiquette Becomes Prompt Engineering
Forget simple chatbots—this is how roleplayers taught AI to remember entire worlds, from 90s MUDs to just-in-time lore delivery.
#1839: AI's Data Kitchen: From Hoovering to Fine-Tuning
We go behind the curtain of the AI data pipeline, revealing the messy, multi-billion-dollar war over data curation.
#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...
#1834: Owning Your AI Memory: The Data Exit Strategy
Why your AI remembers your coffee order but forgets your son’s name—and how to build a portable, federated memory layer you actually own.
#1831: The 79% AI Coder: Reasoning vs. Memorization
AI models now score 79% on coding benchmarks, but a 40-point drop on harder tests reveals the truth.
#1828: Mastering 2M Token Context in Agentic Pipelines
A massive context window sounds like a dream, but it can quickly become a nightmare for complex AI workflows.
#1824: Why Governments Are Building Bunkers for AI
Public clouds can’t handle the security or scale of classified AI. Governments are retreating to fortified bunkers.
#1822: Quantum in the Cloud: Hype vs. Hardware
Is QCaaS a billion-dollar breakthrough or an expensive science experiment? We explore the gap between hype and hardware.
#1819: Claude's 55-Day Personality Transplant
Anthropic leaked 55 days of system prompt updates. See exactly how they rewired Claude's personality, safety rules, and self-awareness.
#1818: Inside Claude's Constitution: A System Prompt Deep Dive
We analyzed Claude Opus 4.6's full public system prompt to uncover its hidden rules for safety, product behavior, and refusal logic.
#1817: The Hidden Taxonomy of AI: Why Specialized Models Outperform Giants
Explore the vast ecosystem of niche AI models for computer vision and document understanding, far beyond large language models.
#1811: Stop Hardcoding User Names in AI Prompts
Three methods for storing user identity in AI agents—and why the "Fat System Prompt" breaks production apps.
#1810: Why Your TTS Sounds Great in English, Terrible Everywhere Else
English AI voices are polished, but global languages hit a wall. Here's why text-to-speech breaks down for Hebrew, Hindi, and beyond.
#1799: The Original AI Blueprints: BERT & CLIP
Before GPT, two models changed everything. Discover how BERT and CLIP taught machines to read and see the world.
#1794: RAG Is Cheaper Than You Think (Until It’s Not)
From a $1 embedding bill to a $10k/month vector database bill, here’s the real math behind RAG in 2026.
#1792: Google's Native Multimodal Embedding Kills the Fusion Layer
Google’s new embedding model maps text, images, audio, and video into a single vector space—cutting latency by 70%.