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.

エピソード

  1. 1月16日

    Lance Martin - LangChain

    Chroma CEO Jeff Huber sits down with Lance Martin to discuss the current state of agents and more. Find Lance on X, https://x.com/RLanceMartin, and his website, https://rlancemartin.github.io/ 0:00 Introduction & Welcome 0:09 Context Engineering: What It Is and Why It Matters 2:05 Context Rot and Performance Degradation 3:31 Year in Review: 2025 AI Trends 4:28 Giving Agents a Computer (File System & Shell) 5:00 Model Context Protocol (MCP) and Tool Bloat 6:07 Multi-Tier Action Space Architecture 8:24 Tool Search and Progressive Disclosure 10:33 Agent Harness Structure & Deep Agents 12:17 Skills and Standard Operating Procedures (SOPs) 14:13 Context Offloading Techniques 15:49 Plan Offloading & The Ralph Wiggum Loop 18:00 Context Caching for Cost & Speed 18:27 Sub-agents and Context Isolation 21:16 Summary: Key Context Engineering Principles 22:00 Evolving Context & Continual Learning 25:02 Claude Diary: Reflecting on Sessions 26:06 Skill Learning from Agent Trajectories 27:00 Memory Management in Token Space vs Weights 28:35 RLMs: Reason Language Models & Learned Context Management 31:30 What Can Be Absorbed Into Models (The Classifier Test) 35:30 Memory: Writing vs Retrieval Challenges 40:00 File Systems as Agent Primitives 42:46 Limitations of File Systems for Large Codebases 45:02 Multi-Agent Collaboration & Concurrency Challenges 49:35 Layers of Context: Session, Agent, Organizational, Global 52:27 File Systems vs Databases: A Hot Take 55:51 Sandboxing and Agent Infrastructure 58:42 What's Most Exciting: Memory, Personal Agents & Bioscience 1:02:18 Wrap Up — Chroma is the open-source AI application database. Batteries included. Embeddings, vector search, document storage, full-text search, metadata filtering, and multi-modal. All in one place. Retrieval that just works. As it should be. Try it today:https://trychroma.com/cloud

    1時間3分
  2. Drew Breunig

    2025/12/11

    Drew Breunig

    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) -- Chroma is the open-source AI application database. Batteries included. Embeddings, vector search, document storage, full-text search, metadata filtering, and multi-modal. All in one place. Retrieval that just works. As it should be. Try it today: https://trychroma.com/cloud

    58分
  3. Dex Horthy

    2025/12/11

    Dex Horthy

    Chroma CEO Jeff Huber chats with Dex Horthy about agents and context engineering. Dex on X: https://x.com/dexhorthy 0:00 - Introduction 0:23 - Context Engineering Origins 0:33 - 12 Factor Agents 0:43 - New AI Models 1:43 - Model Switching Strategy 3:07 - Personal Productivity Systems 7:57 - AI UX Patterns 13:07 - Todo List Management 15:01 - Collaborative AI Workspaces 22:09 - In-Person vs Remote 24:05 - Tab Complete Patterns 25:00 - Shared Context Layer 27:00 - Markdown & Airtable 32:35 - Data Storage Systems 34:00 - AI-Native Organizations 36:00 - OAuth & Authentication 38:00 - Desktop App Development 43:42 - Context Engineering Evolution 45:00 - Evals & Observability 46:00 - LM-as-Judge Discussion 48:00 - Snapshot-Based Evals 54:47 - Agent Memory Systems 56:00 - Instruction Following Limits 57:57 - Closing Remarks -- Chroma is the open-source AI application database. Batteries included. Embeddings, vector search, document storage, full-text search, metadata filtering, and multi-modal. All in one place. Retrieval that just works. As it should be. Try it today: https://trychroma.com/cloud

    58分
  • 3話のエピソード

番組について

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.

情報

  • クリエイター
    Chroma
  • 配信期間
    25年 - 26年
  • エピソード
    3
  • 制限指定
    不適切な内容を含まない
  • Copyright
    © Chroma
  • 番組のWebサイト
    Chroma | Context Engineering