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  1. 01Spotify · scaling

    Advancing Generative Recommendation Inference: our collaboration with NVIDIA on TensorRT LLM

    Spotify migrated its generative recommendation inference to NVIDIA TensorRT-LLM to address performance bottlenecks with vLLM and feature gaps in other engines. Through a collaboration with NVIDIA, the team added support for beam search, multi-item scoring, and soft token injection into TensorRT-LLM. This integration resulted in significant cost reductions of up to 54 percent for beam search and 7 percent for scoring workloads.

    DEPTH ■■■■■7 min read11 days ago

    Machine Learning

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  2. 02GitHub · architecture

    From 2,500 requests to 24: Rebuilding star history without the stargazers

    GitHub faced abuse and performance issues because tools were scraping user identities to reconstruct repository star history. The engineering team collaborated with maintainers to build a purpose-built aggregate endpoint that returns daily star counts without identities and filters by time before aggregation. This reduced request volume from thousands to a few dozen and eliminated heavy database scans.

    DEPTH ■■■■■8 min read13 days ago

    Microservices

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  3. 03GitHub · migration

    Improving site performance by shipping more CSS

    GitHub faced performance degradation on server-side rendering and initial page loads due to scaling issues with its CSS-in-JS solution. The engineering team executed a multi-year migration to CSS Modules and eliminated dynamic styling props using incremental rollouts, wrapper components, and automation tools. This resulted in significant performance gains, including 55% faster server-side rendering and the complete removal of styled-components.

    DEPTH ■■■■■8 min read4 weeks ago

    FrontendPerformance

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  4. 04GitHub · performance

    How we make AI coding more cost efficient without sacrificing task quality

    GitHub optimized GitHub Copilot to improve cost efficiency without sacrificing task quality by evaluating changes across the entire agentic workflow. The team implemented four targeted adjustments including selective output compression, removing unused line-number formatting, shortening prompts via meta-prompting with behavioral tests, and delivering background work completions directly. These modifications reduced token usage and model inference costs globally without causing task-success regressions.

    DEPTH ■■■■■9 min read7 weeks ago

    Machine Learning

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