AI Core
Fundamentals of AI models, architecture, and how they work
221 episodes · Page 3 of 10
#2487: Why AI Calls Everything a "Prediction" (Even Images)
Machine learning calls everything a "prediction" — even generated images. Here's why the terminology matters more than you think.
#2483: Substitution Anonymization: Privacy Without Utility Loss
How to generate realistic synthetic voice notes and calendar data with zero PII exposure risk.
#2478: MCP File Handling: Why Your Base64 Upload Breaks at 4MB
MCP has no standard file input. Base64 breaks at 4MB, presigned URLs need whitelisting, and MinIO workarounds aren't standardized.
#2470: Where Intelligence Should Live in Your Pipeline
When should you fine-tune a tiny model for prompt enhancement instead of prompting a large one? The answer depends on latency, precision, and domain.
#2469: Embedding Model Deprecation: RAG's Silent Killer
When OpenAI retires an embedding model, your RAG pipeline breaks silently. Here’s how to fix it.
#2466: The Hidden Trap of Embedding Model Lock-In
What happens when your vector database works great — until your embedding model gets deprecated and your vectors become useless.
#2465: JSON-L vs Parquet: When Each Format Wins
How far can JSON-L scale before it breaks? And why does Parquet dominate for millions of rows?
#2464: Batch APIs: The 50% Discount You're Probably Misusing
Batch inference APIs offer 50% off — but only for the right workloads. Here's when they actually make sense.
#2461: How Claude Code's Conversation Compaction Actually Works
The three-tier system, what survives, what dies, and why you shouldn't rely on auto-compact.
#2458: Can Graph Databases Go Mainstream?
Graph databases are powerful but niche. Will they ever power mainstream CRMs and ERPs?
#2456: Choosing Between AI Cloud Providers
A practical guide to choosing between Modal, RunPod, Nebius, and Baseten for AI workloads.
#2431: The 3 Markets in an AI Trench Coat
GPUs, LPUs, and ASICs: why the best hardware for AI depends entirely on what you're trying to do.
#2426: Why DeepSeek V4's Prose Feels More Vivid Than Claude or GPT
A million-token context window at 2% the KV-cache cost — and prose that actually breathes. Here's what makes V4 different.
#2408: How Backpropagation Actually Unlocks Neural Networks
How error signals flow backward through networks to make learning possible — and why "it's just calculus" misses the point.
#2406: Why Million-Token Context Windows Can't Handle 3 Reasoning Steps
Needle-in-a-haystack is dead. Here's what actually measures whether models can think across long documents.
#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.
#2404: What Tool-Calling Benchmarks Miss About Production Failures
BFCL, tau-bench, and Nexus each reveal different failure modes. None of them test what actually kills production agents.
#2403: Choosing Your LLM Eval Framework
An architectural shootout of four major LLM evaluation harnesses — where each shines and where each breaks down.
#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.
#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.
#2377: Is Geopolitical Neutrality a Sustainable AI Strategy?
How DeepSeek carved a niche with efficiency, neutrality, and innovative dialogue handling — and what it means for AI's future.
#2374: How Granular Can MoE Experts Get?
Exploring the limits of expert granularity in Mixture of Experts models—how narrow can segmentation go before efficiency or accuracy suffers?
#2368: The Multi-Stage Pipeline Behind Netflix's Recommendations
Unpacking the multi-stage AI pipeline behind Netflix, Spotify, and Amazon’s "you might also like" suggestions—from candidate generation to real-tim...
#2366: Why LLMs Forget the Middle of Long Conversations
Why do large language models struggle with the middle of long conversations? Explore the science behind attention dilution and practical fixes.