Regarding the user instruction aggregation process in the agent loop, I'm curious how you manage context retention in multi-turn interactions. Have you explored any techniques for dynamically adjusting the context based on the evolving user requirements?

The 'Broad Safety' guideline seems vague at first, but it might be beneficial to incorporate user feedback loops where the AI adjusts based on real-world outcomes. This could enhance its adaptability and ethics over time, rather than depending solely on the initial constitution.

Been using GitHub Copilot to handle the tedious webpack/babel config files and it's a game changer for modern web dev. No more spending hours debugging build pipeline issues - it generates 90% correct configs that just need minor tweaks.