Candidate Selection - Testing Variety & Freshness (Agentic Pipeline #31)

We verify the new ranking actually works. After fixing a timezone error, I add logging of the top-scored candidates so we can see the freshness and fatigue multipliers being applied and confirm the archetypes start cycling as intended. This is lesson 31 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 - Intro to Embeddings ...   ◀ Previous lesson:    • Candidate Selection - Variety and Freshnes...   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