Candidate Selection - Variety and Freshness (Agentic Pipeline #30)

We add database tracking for which candidate each run selected, then implement the freshness and archetype-fatigue multipliers in their own module with thorough tests. I also catch an ordering bug where the archetype history used creation order instead of selection order. This is lesson 30 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 - Testing Variety & Fr...   ◀ Previous lesson:    • Candidate Selection - The Selection Proble...   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