Provides hands-on code and architecture for developers building custom, high-performance retrieval systems.
Sentence Transformers v6.0 introduces a new MultiVectorEncoder model type, with a detailed guide for finetuning or from scratch for late-interaction/ColBERT-style retrieval.
A practical guide on effectively using AI coding agents by providing context, breaking down problems, and maintaining control over the development process.
Guide for serving Markdown content to AI agents via Accept: text/markdown headers to reduce token usage and improve RAG performance.
Explains how RAG rerankers actually work at the token level rather than just architectural level, showing they learn statistical token associations conditioned on query-passage pairs.
An accepted MATS 11.0 applicant shares their experience with the OpenAI safety team stream application process and advice for future candidates.
Explains the mathematical rationale behind Random Forest's feature subsampling, showing how it reduces correlated errors between trees beyond what bagging alone can achieve.
