AI Digital Brain / Personal Knowledge OS
Create a private, continuous-ingestion knowledge graph connecting PDFs, technical docs, bookmarks, code repositories, and 16+ years of engineering experience.
The Problem
What difficult challenge is this experiment tackling?Engineers accumulate massive knowledge across bookmarks, notes, chats, and git commits that become fragmented, forgotten, and unsearchable over time.
The Hypothesis
What do I believe autonomous AI & engineering can accomplish?Combining a hybrid vector database with a graph knowledge base allows an engineer to query their entire 16-year career history with zero hallucination and high relational retrieval precision.
System Architecture & Workflow
End-to-end design, data pipelines, and component interactionBenchmark & Empirical Metrics
Accuracy • Latency • Cost • Token Usage • Human InterventionSub-second retrieval latency on 100,000+ personal nodes, zero citation drift, cross-domain relationship discovery rate.
Security, Privacy & Ethical Guardrails
Risk mitigation, strict boundaries, and sandbox controls100% local or encrypted embedding storage, strict isolation of sensitive credentials, zero telemetry leak to third-party model providers.
Lessons Learned & Observations
Empirical findings from prototyping and testingDesign stage completed. Evaluation metric framework prepared.
Next Experiment Iteration
Upcoming modifications, scaling factors, or new hypothesesImplement voice interface for audio note auto-ingestion.