Candidate Selection - Intro to Embeddings (Agentic Pipeline #32)

A conceptual lesson on the originality multiplier. I explain vector embeddings and cosine similarity at a high level, how we'll penalize candidates too similar to recently published ideas, and why we're using ChromaDB as our vector store. This is lesson 32 of The Agentic Pipeline, my free course on building an autonomous AI research pipeline using agentic engineering practices. I use Claude Code, but you can follow along with the agentic coding tool of your choice. Full course playlist:    • The Agentic Pipeline: Build an Autonomous ...   ▶ Next lesson:    • Candidate Selection - Implementing Embeddi...   ◀ Previous lesson:    • Candidate Selection - Testing Variety & Fr...   Everything for the course lives in one GitHub repo, including the reference implementation, starter files, diagrams, and up-to-date model recommendations: https://github.com/digitalhobbit/agen... The full production version of this pipeline runs my daily startup-idea newsletter, fully autonomously. See it live: https://gammavibe.com More from me: • Newsletter: https://gammavibe.com/updates/ • X: https://x.com/GammaVibe • GammaVibe community Discord: https://gammavibe.com/community #AgenticPipeline #ClaudeCode #AIAgents