digital-brain

Build a personal knowledge OS by structuring identity, content, and network data into separate modules. This approach uses progressive disclosure to load only necessary data on demand, keeping context lean and preventing memory overload.

Manage your brand voice and content pipeline by defining identity in Markdown and storing network data in JSONL. This structured data approach enables append-only memory, preserving history for retroactive analysis while generating content drafts.

Append-only memory preserves data history by continuously adding records to JSONL and YAML files without overwriting past entries. This mechanism enables retroactive analysis of content, relationships, and operations while maintaining a consistent, auditable data surface.

No external dependencies are required to run this AI operating system beyond a basic Python runtime. It uses standard formats like JSONL, YAML, and Markdown for data interchange, requiring no external installation to manage your identity and content modules.

Start creating content by defining voice and brand in Markdown files, then populate network contacts in JSONL. Use included content templates and Python scripts to generate ideas, drafts, and posts on demand while keeping the operating system context lean.

Organize identity and network data for AI workflows by separating modules into Markdown for brand definitions and JSONL for contacts. This modular separation ensures progressive disclosure, loading only required data on demand to keep operations efficient and auditable.