ABOUT THIS EPISODE
Jeff Huber sits down with Drew Breunig to talk about the origins of Context Engineering and more.
Drew has a wide range of his writing about AI on his website: https://www.dbreunig.com/
00:36 Why write about AI? (Writing as a searchable index)
01:28 The two buckets of AI writing: Hype vs. Research
04:08 The Gemini 1.5 Paper & Pokemon: The birth of Context Engineering 06:50 The "Karpathy Effect" on Context Engineering
08:17 Benchmarks, Model Cards, and the "Agent Harness"
11:41 The Weightlifting Metaphor for AI Benchmarks
14:02 Testing Opus 4.5: Building internal tools in one shot
15:54 Models are untapped: The gains are in the harness
17:05 Why isn't there a standard Context Engineering harness?
19:20 The "Hello World" Experiment: Testing Agent Frameworks (LangChain, Crew, etc.)
21:42 Compact and Grep vs. File Systems
23:45 "Naked" tool calls vs. Frameworks
24:50 The GPT-8 Thought Experiment: Why Software Engineering still matters
27:12 Compound AI Systems: What agents can learn from Data Pipelines
31:00 Reliability is the bottleneck (The MAP Report)
36:00 Token Speed: When code generates faster than humans can read (Groq/Cerebras)
41:00 The UX of Multi-Agent Systems (The "Starcraft" problem)
43:57 ChatGPT Deep Research: The Shopping Use Case
46:25 Building Trust: Agent Design as Client Services
49:30 Continual Learning: Weights vs. Context/Memory
51:50 The problem with "Black Box" memory (The Chocolate Example)
56:30 The need for "Modes" (Work vs. Home context)
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