Trails
Research
Deep dives into adjacent systems, benchmarks, and comparisons — from MemPalace and Mem0 to Karpathy's LLM Wiki and the long tail of RAG variants.
Research
Scaling Trail from 200 to 100,000 Neurons: An Engineering Note
A compile-time knowledge engine has three workloads that fight for compute — ingest, lint, and the curation queue. Each one breaks at a different corpus size, for different reasons, and requires a different fix. This is the long-form companion to Work That Fits in a Night: the full accounting of where Trail's bottlenecks are, when they hit, and what a deployment looks like at 200, 8,000, 25,000, and 100,000 Neurons.
Research
Trail and NotebookLM: Same Ethos, Different Artifact
NotebookLM proved source-grounded ingest-time tools can ship to millions. Trail shares that ethos and diverges on one question: what the system leaves behind.