Thirteen interactive angles on the autonomous AI loops that iterate on d3-power-tools visualizations.
Autoresearch is a simple idea: let an AI propose a code change, run it, compute a score, have an auditor judge whether the result actually improved, and keep or discard the change. Repeat. d3-power-tools applies the pattern to D3 visualizations, using four quality dimensions and a composite score to decide what survives.
The first seven pieces explain how the loop works today. The last six explore ways to make it work better: vision auditors, multi-objective rewards, smarter proposer strategies, compressed context, ensemble auditing, and cross-block transfer learning.
Autoresearch has five recurring roles. Every glyph on this page colors its elements by these roles, and every article uses the same palette in its figures.