Adam Smith from UC Santa Cruz joins us to discuss local Small Language Models (SLMs) and building open, autonomous tools. We explore his BayLeaf AI counterplatform, transagency (the "me-and-the-car" concept of human-agent collaboration), context distillation, and running models locally without relying on data-harvesting corporate clouds.
Adam also shares practical strategies for working with coding agents, including why plain-text AGENTS.md files outperform model weight fine-tuning for long-term knowledge retention. He also describes how he uses open-source tools like OpenWebUI, OpenCode and OpenChamber to integrate small and local LLM integration into development environments and web chats.
Discussion Resources & Links
Adam's Projects & Research:
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BayLeaf AI Counterplatform: Hyper-local, open-source AI infrastructure built for UC Santa Cruz. See also the BayLeaf Getting Started Guide and BayLeaf Blog.
Open-Source Agent Harnesses & Wrappers:
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OpenCode – Open-source command-line coding agent harness with VS Code integration (comparable to Claude Code)
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OpenChamber – Graphical desktop wrapper for OpenCode with VS Code support (comparable to Claude Cowork)
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OpenWeb UI – Self-hosted web interface and desktop app for local and remote LLM models
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OpenRouter – Token broker/middleman routing requests across competitive LLM inference providers
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r/LocalLlama – Community subreddit for local LLMs, open weights, and hardware advice
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AGENTS.md Convention & Agent Skills Standard – Open standards for persistent context management
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Context Distillation Paper (arXiv)
Adam's Guide to Building Transagent Capabilities:
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Scope by Project Roots: Harnesses like OpenCode and OpenChamber use project root folders to establish clear, safe boundaries for agent execution.
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Store Context in Plain Text: Keep global and project preferences in simple .md files (like AGENTS.md or GLOSSARY.md). Plain text files make your accumulated knowledge portable across different model front-ends and harnesses.
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Handle Errors by Rewinding: When your LLM or agent makes a mistake, don't yell at it or try to correct it inline—go back in time to before the error and alter your prompt or context to lead it away from the mistake.
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Refactor Preferences into Skills: As your AGENTS.md context grows, ask the agent to factor recurring preferences out into modular skill files (.agentskills).
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Teach Transagency: Distinguish between standalone agent capabilities (autonomous work) and transagent capabilities (how the agent works with you).
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Build Local Tools: Ask the agent to build desktop helpers (QuickLook triggers, push notifications, or fuzzy glossary lookup) to keep a shared orientation.
Elecia: "Complete one project or start a dozen?"
Adam: "Tens of thousands, and start hundreds more within the starting of the other projects recursively forever."
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