Technical Experimentation:

Structured output from an LLM
The first real challenge was getting Claude to return consistent, parseable JSON on every run. The solution was a strict schema prompt — explicit field names, fixed enum values, and hard constraints like "return only the JSON object, nothing else." After a few iterations it became reliable enough to wire directly into the pipeline without validation failures.

Prompt caching
The system prompt is the same on every run, so there's no reason to re-process it each time. Sending it with cache_control: ephemeral tells Anthropic's API to hold it in memory for up to 5 minutes, cutting both latency and cost on every daily synthesis call.

Building RAG from scratch
The archive search started as simple keyword matching — fast, but rigid. Searching "funding" would miss any article that said "raised" or "Series B" instead. Replacing it with real retrieval-augmented search meant embedding every article as a vector at generation time, then ranking results by semantic similarity at query time. The embedding model runs entirely locally, so there's no extra API, no extra cost, and no latency on searches.

Hybrid retrieval
Pure semantic search has a subtle flaw: an article with your exact search word in the title can rank below a loosely related one. The fix was a score boost — exact title matches get +0.25, summary matches get +0.12, applied on top of the similarity score. Exact matches always surface first, semantic results follow. This is how production search engines like Elasticsearch handle it.

MCP for live debugging
When the archive broke due to a React rendering error, Claude attempted to connect directly to the running browser tab via MCP — Model Context Protocol — to read the JavaScript console without me having to describe what I was seeing. The Chrome extension wasn't active at that moment so it fell back to reading the source code, but the tooling is wired up for future sessions where Claude can see the browser directly.