Explain the lifecycle of Merge Mind's knowledge base from first install to a mature, tuned state?

1. Fresh install: with no training yet, Merge Mind can only lean on GPT-4's general knowledge and framework awareness, with no team-specific context
2. Optional Initial Learning: running the local codebase training script gives it a baseline understanding of existing architectural patterns and conventions
3. Early live reviews: as the first merge requests get reviewed, feedback is a mix of general best practices and whatever baseline was learned
4. Continuous Learning accumulation: every merged request adds fresh patterns of what the team accepts as good code
5. Feedback Learning refinement: which comments get resolved versus dismissed steadily tunes what the bot chooses to flag
6. Mature state: after sustained usage, described as being able to understand a codebase better than most new team members within about a month

The maturity of Merge Mind's knowledge base is directly tied to usage volume and time, a deployment reviewed daily by an active team will reach a tuned state far faster than one used sparingly.

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