Chroma | Context Engineering
Chroma
Conversations with practitioners and researchers building with large language models, cutting through the hype to examine what actually works in production. Context engineering, agent architecture, model selection, and the gap between benchmark scores and real-world performance. Grounded technical perspectives for builders navigating the practical challenges of AI engineering.
Episodes
Mentioned books
17 snips
Jan 16, 2026 • 1h 3min
Lance Martin - LangChain
Join Lance Martin, an AI practitioner known for his expertise in context engineering and LangChain, as he dives into fascinating topics. He discusses the importance of context engineering and how to manage it with tools like shells and file systems. Lance explains context rot, model context protocols, and the challenges of memory management in AI systems. Delve into subagents for task isolation and the nuances of multi-agent collaboration. Get insights on evolving context and the future of personal assistant agents!
16 snips
Dec 11, 2025 • 58min
Drew Breunig
Drew Breunig, a writer focused on applied AI and context engineering, joins Jeff Huber to explore his passion for AI writing. They discuss the tension between hype and rigorous research in AI narratives. Drew shares insights on the impact of the Gemini 1.5 paper, the ‘Karpathy Effect,’ and the importance of harness design for optimizing model performance. He critiques the challenges of 'black box' memory and proposes better context management. The conversation dives into the future of multi-agent systems and fosters trust through thoughtful UX in AI.
10 snips
Dec 11, 2025 • 58min
Dex Horthy
Dex Horthy, a leading practitioner in context engineering and agent architectures, dives deep into the world of AI tooling and productivity. He shares insights on the evolution of context engineering, discusses the merits of master single models versus multi-agent systems, and explains personal productivity systems that integrate markdown with collaborative AI tools. Dex also emphasizes the significance of a shared context layer in AI-native organizations and explores innovative UX patterns that enhance user interactions with intelligent agents.
