Capture
Capture ideas, notes, links, tasks, and inputs across the tools I already use.
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Project
Aurora
A personal system for contextual intelligence and workflow-driven thinking.
Aurora is an ongoing experiment in building a system that captures, processes, and surfaces context across work, ideas, and decisions.
Why I built it
Too much information was scattered across tools and contexts. Notes, chats, tasks, reminders, and references were disconnected.
The goal was to build a system that could remember, organize, and surface context when needed—reducing cognitive load and improving continuity of thought.
The system
Process
Clean, categorize, and structure those inputs into usable knowledge and retrievable context.
Recall
Surface relevant context when needed so past work, decisions, and notes become usable in the moment.
Reflect
Turn stored context into daily and weekly summaries, patterns, and higher-level insight.
Stack
Built with a workflow-first stack
Aurora was built using tools like n8n, Notion, Telegram, and OpenAI models, with a focus on workflow automation, contextual memory, and reducing cognitive load.
- n8n
- Notion
- Telegram
- OpenAI
What it actually did
In practice, Aurora became a dependable operating layer that turned scattered inputs into structured, retrievable context.
Captured notes, voice notes, and URLs from Telegram.
Brought in calendar context and daily events.
Pulled knowledge and references into one place.
Generated daily briefs.
Organized memory into structured categories.
Supported recall across past notes and context.
Why I paused it
The first layers were working. The next frontier was reflection, and that part felt more open-ended and ambitious.
I paused the project there, but it fundamentally shaped how I think about memory, workflows, and AI systems. Aurora may return in a different form later.
Aurora remains one of the clearest expressions of how I think about workflow-first systems, contextual memory, and operator leverage.