《深入理解 AI Infra：量化分析与系统设计》（李博杰 著）开源书稿：从硬件约束和模型架构出发，量化推导 LLM 推理与训练系统设计。含全书正文、PDF、配套计算工具与实验
Coz: Causal Profiling
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
Performance monitoring and benchmarking suite
A reading path for CPU performance engineering, from one instruction to production inference. Primary sources only, with a runnable benchmark for every section.
A code analyzer for Julia. No need for additional type annotations.
An operator for running distributed k6 tests.
Runtime evidence that helps agents trace, profile, and burn down hotspots in application and native code, GPU kernels, and inference stacks.
MLOS is a project to enable autotuning for systems.
A curated collection of publicly available resources on how companies around the world perform load testing
Ultra-Fast Python Solutions: 110x Speed Optimization Guide 2026
An application-agnostic framework for automated performance testing and analysis.
Julia wrapper for the performance monitoring and benchmarking suite LIKWID.
A component render time benchmarking suite for React
Experiment tracker: organize, visualize, compare and share runs. Removes toil from algorithm/performance R&D and tuning.
🦗 Learn Locust from scratch 🐍
🔎 Rust TUI that finds & AI-fixes Lighthouse image issues (CLS, lazy loading, WebP, srcset) across your entire codebase in seconds.
Learn k6 Series
A curated, opinionated collection of tools and resources dedicated to Performance Engineering, covering both Observability and Performance Testing.
Production-grade k6 performance testing framework for smoke, load, stress, and soak tests with GitHub Actions, Docker, thresholds, custom metrics, safety guardrails, and machine-readable results.
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