Research Radar

Core techniques for finding and scoring relevant documents at scale — dense retrieval, hybrid search, and evaluation methodology.

Algorithms and architectures for personalized recommendations — collaborative filtering, sequential models, and LLM-based recommenders.

Learning-to-rank, relevance judgments, and query understanding that determine what shows up first.

Applied machine learning specific to shopping experiences — pricing, catalog understanding, visual search, and conversational commerce.

The model-and-research side — architectures, training and fine-tuning methods, reasoning, evaluation, and alignment.

Infrastructure patterns for running services at scale — networking, data pipelines, platform engineering, and distributed systems operations.

Production patterns and infrastructure for wiring LLMs into real systems — orchestration, routing, and the tooling that emerges as those patterns mature.

Release notes and version changes for languages and frameworks used in backend and frontend engineering.

Broader shifts in how engineering teams work — ThoughtWorks Radar calls, DORA metrics, and AI's effect on day-to-day practice.

Named developer tools and products — IDEs, coding agents, CLIs, and other dev-productivity software gaining real traction.