Feed it anything. Ask it everything. It evolves.
You don't organize it. You don't maintain it.
You feed it sources and ask it questions. The rest happens on its own.
Drop a PDF. Paste a URL. Forward an email.
Connect GitHub. Sync Notion. Subscribe to RSS.
It compiles while you sleep.
Early attempts at machine learning drew from neuroscience models of cognition.
The fundamental debate that shaped decades of artificial intelligence research.
How a simple calculus trick, once dismissed, transformed an entire field.
Attention mechanisms replaced recurrence and changed sequence processing.
Unexpected behaviors arise when models reach sufficient scale.
The symbolic-connectionist debate represents the deepest fault line in AI research. Symbolic AI treats intelligence as the manipulation of discrete symbols according to explicit rules — programs that reason through logic, search, and knowledge representation. Connectionist approaches treat intelligence as an emergent property of distributed numerical computation — networks that learn patterns from data without explicit programming.
The core disagreements center on three axes: whether knowledge should be explicit or implicit; whether reasoning should be compositional or holistic; and whether learning should be structured or tabula rasa.
Every question compounds the knowledge.
Fed 3 sources
Compiled 12 articles
Linted: 2 gaps found
Freshened: updated 4 stale articles
Discovered: 3 new connections suggested
It tends itself.
Finds gaps. Refreshes stale knowledge. Discovers connections you missed.
Your engram grows whether you're using it or not.
“What in my research is relevant to the product roadmap?”
Engrams don't just hold knowledge. They cross-pollinate.
You never create a page.
You never drag something into a folder.
You never write a tag.
You never maintain a link.
You never organize anything.
The system does all of it.