These are the research documents that shaped NTL. They sit alongside the polished protocol docs and are intentionally rawer — they capture the why behind the architecture, not only the what. Read these when you want to understand why NTL is designed as an actual neural network rather than a messaging protocol, why the signal is a new concurrency primitive rather than a message type, and why IPv6 is a hard requirement rather than a preference.Documentation Index
Fetch the complete documentation index at: https://openntl.org/llms.txt
Use this file to discover all available pages before exploring further.
| # | Document | What it covers |
|---|---|---|
| 01 | The Signal Primitive | A new concurrency primitive. Nodes, signals, synapses as the NTL triad. |
| 02 | Machine Learning at the Transfer Layer | PyTorch for applications; NTL for networks. ML as infrastructure. |
| 03 | Neural Network as Base Layer | The routing engine is a neural network, executable on NPU silicon. |
| 04 | Network Transport & IPv6 | Why GSPN’s fabric layer cannot live on IPv4 NAT. |
| 05 | The Twelve Principles | The full neural repertoire beyond PyTorch’s five. |
| 06 | First Steps | The concrete path from now to research preview. |
These documents cross-reference SiafuDB’s research. See
siafudb.org/docs/research for the complementary architecture work on the memory layer.