Results for “rs256”

Implement AES-256-GCM encryption for files and data at rest, including key derivation, IV management, and authenticated encryption.

Aggregate RSS feeds from YouTube, Vimeo, Behance, Twitter/X, Bilibili, Weibo, Douyin, Xiaohongshu, and Zhihu with incremental updates, deduplication, and AI classification.

Perform high-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames, including overlap, nearest, merge, coverage, complement, subtract, and reading/writing BED, VCF, BAM, GFF, FASTA, and FASTQ formats with streaming and cloud-native support.

Enables fast similarity search and clustering of dense vectors using FAISS, supporting billions of vectors, GPU acceleration, and various index types.

Compute, verify, and compare file hashes using MD5, SHA-1, SHA-256, SHA-512, and more. Use when checking file integrity, verifying downloads against expected checksums, comparing files for equality, generating checksums for directories, hashing strings, or validating checksum files (sha256sum/md5sum format). Supports.

RICE feature prioritization with scoring and capacity planning. Usage: /rice prioritize <features.csv> [options]

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Process large-scale ML datasets with distributed streaming execution across CPU/GPU, supporting Parquet, CSV, JSON, images, and integration with PyTorch, TensorFlow, and Ray Train.

Perform fast genomic interval operations (overlap, nearest, merge, coverage, cluster, complement, subtract, count-overlaps), multi-format bioinformatics I/O, DataFusion SQL, and pileup on Polars DataFrames via the polars-bio library, serving as a scalable alternative to bioframe and bedtools.

RICE feature prioritization with scoring and capacity planning. Usage: /rice prioritize <features.csv> [options]

Process large ML datasets in parallel across CPU or GPU clusters, with streaming execution, multi-format I/O, and integration with Ray Train, PyTorch, and TensorFlow for batch inference and preprocessing pipelines.

Generates kernel-DT overlay fragments to enable 25G/10G/1G MGBE QSFP interfaces on Jetson Thor, verifying pinmux and integrating with the BSP customization workflow.