AI

AI is where I'm spending most of my new learning energy and most of my hands-on weekend hours. My perspective comes from two angles: the architectural — pattern selection, governance, integration, the boring-but-foundational work — and the practitioner's, from running real systems I built myself.

The two personal projects I lean on most:

The Research Curation Daemon (RCD) A self-hosted, fully autonomous weekly pipeline that scouts research signals, synthesizes them, and ships podcast-style briefings across four domains. It coordinates three LLM providers (Perplexity for breadth, Claude for synthesis, OpenAI for selected stages) and uses NotebookLM for audio. Architecturally, it's a scheduled daemon with resume detection and incident recovery, a custom MCP server exposing each pipeline stage as a tool, telemetry from day one, and an A/B prompt-evaluation framework that has changed my mind about prompts more than once. There is exactly one human-judgment gate in the pipeline: episode approval before dev-to-production promotion. That single gate is more interesting than it sounds — it's where I've learned the most about where humans should and shouldn't be in agentic loops.

Open Brain (OB1). A personal deployment of Nate B. Jones's open-source AI memory layer (Supabase + pgvector + an MCP tool surface), integrated across every AI client I use — Claude Desktop, Claude Code, web Claude. The single most valuable thing about it isn't the memory itself, it's that the memory travels with me regardless of vendor. I added a custom feedback loop where RCD reads OB1 context as a signal source for query evolution, so the two systems compose.

Convictions I hold somewhat strongly, from this practice:

• MCP is a strategic abstraction, not a fad. Tool surfaces that are language-model-agnostic and discovery-driven are how multi-vendor agentic systems will actually compose. Locking your tool surface to a single provider's function-calling format is a near-term decision that ages badly.
• Multi-provider orchestration is already the right default for any non-trivial agentic system. No single model is best at every task; the difference between "best available" and "what we already integrated" is meaningful.
• The human-judgment gate pattern matters more than the autonomy story. Pipelines that name exactly where a human is required, and earn full automation everywhere else, are far stronger than pipelines that hedge with reviews everywhere.
• A/B prompt evaluation is underused. Prompt quality compounds over a pipeline; the habit of measuring is worth more than the habit of crafting.
• Telemetry first. I will say this for every domain I work in until I retire.

Posts under this section will mostly be deep-dives on the patterns and decisions inside RCD and OB1, and broader takes on enterprise AI practice — RAG, agentic governance, the operating-model question.

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