digital-brain

Consolidate scattered personal data by coordinating identity, content, knowledge, network, and operations domains across a modular structure to power AI-assisted personal management. This approach minimizes context pollution by loading only relevant modules for specific tasks.

Automate content creation by defining your voice in identity/voice.md and your brand in identity/brand.md, then instructing your agent to generate posts using those parameters. The system references these identity modules to ensure output matches your defined style.

Append-only memory improves AI agent reliability by enabling predictable context handling through a three-level loading pattern of L1 metadata, L2 module instructions, and L3 data files. This progressive disclosure allows reliable auditing and pattern analysis for weekly reviews.

Yes, you can use JSONL logs and YAML configs to manage AI agent context for meeting preparation. The system reads append-only data files and configurations to load only the necessary network and knowledge modules, ensuring targeted context without pollution.

Yes, you need to format contacts as JSONL in network/contacts.jsonl before automating meeting prep. The system relies on structured JSONL logs and YAML configs to parse network data and load relevant modules for AI-assisted preparation.

Modular domain loading prevents context pollution by separating identity, content, knowledge, network, and operations into distinct modules. The system loads only the relevant modules required for a specific task, ensuring predictable context handling and efficient AI processing.