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Optimize .NET loops with SIMD, which processes multiple values in one operation, or review existing SIMD code. Check behavioral equivalence, memory safety, platform fallbacks, and benchmarks to establish whether the optimization is correct and worthwhile.
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Design, implement, optimize, and review SIMD code in .NET. USE FOR: vectorizing scalar loops with TensorPrimitives, Vector64/128/256/512, or platform hardware intrinsics; reviewing existing SIMD code, including the generic Vector type, for contract equivalence, tail handling, memory safety, portability, fallbacks, and measured performance. DO NOT USE FOR: performance work unrelated to SIMD or vectorization.
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Plan and review PufferLib reinforcement learning workflows, where agents learn through rewards. Covers version-specific environment checks, parallel execution, training and evaluation plans, plus checkpoint inspection before loading saved models.
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Transform sequential Python code into parallel/concurrent implementations. Use when asked to parallelize Python code, improve code performance through concurrency, convert loops to parallel execution, or identify parallelization opportunities. Handles CPU-bound (multiprocessing), I/O-bound (asyncio, threading), and data-parallel (vectorization) scenarios.
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The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules fr
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Decides hand-vs-compiler for loop transforms (unrolling, SIMD, fusion, branchless). Use when reviewing/authoring a hot loop or tempted to hand-optimize one.
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Design and produce raster images, native SVG artwork, brand-controlled visuals, and documented image edits with Recraft's API. Use for Recraft model and route selection, exact request construction, image-to-image, inpainting, outpainting, background work, vectorization, remix/exploration, palette and typography control, API migration, safety and rights review, and production QA.
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Optimize MATLAB code for better performance through vectorization, memory management, and profiling. Use when user requests optimization, mentions slow code, performance issues, speed improvements, or asks to make code faster or more efficient.
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Portrait Mode for SVGs — foveated vectorization with 4-zone selective detail. Combines vision annotations, MediaPipe segmentation/landmarks, and optional saliency. Like phone portrait mode, but vectorized. Use when vectorizing a portrait or photo where subject detail should outrank background detail.
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Use after EZ_math_model coding has produced src/*.py and before packaging when code quality, reuse, vectorization, naming, or unnecessary complexity should be reviewed without changing behavior.
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Use when writing or porting AArch64 SIMD to SVE or SVE2: arm_sve.h intrinsics, predicates, vector-length-agnostic loops, auto-vectorization, or SVE registers in GDB. Not for NEON: use simd-intrinsics.
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