Turn 10,994 Notes Into Memory - Paul Iusztin, Decoding AI & Louis-François Bouchard, Towards AI

AI Research OS turns research into files that a later agent session can reuse. Its repository separates copied source material from per-source summaries and synthesis pages about concepts, entities and comparisons. An index tells subsequent sessions what is already available.

When answering from an existing research directory, the agent starts with the index and follows relevant pages toward the original material as needed. That provides a concrete alternative to repeatedly rebuilding context from pasted links. The implementation uses a local research wiki; its value is preserving and organizing prior work, rather than proving that a larger note collection produces more accurate answers.

“Turning thousands of notes, videos, documents, and repositories into usable AI context requires more than a bigger context window.”

“It requires memory and context engineering: organizing sources, indexing what matters, and loading only what the model needs.”

“Build a token-efficient memory layer from plain files, not a vector or graph DB”