parallel▌

Load a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/GPU variants. Use when no single-call loader fits, including when per-shard row counts differ across files. Prefer cupynumeric.load or legate.io.hdf5.from_file when they apply.

Distributed computing for larger-than-RAM pandas/NumPy workflows, enabling parallel processing and scalability across clusters.

Self-improving browser automation via the auto-research loop, iteratively enhancing navigation skills for specific website tasks.

Coordinate multiple specialized agents for complex tasks requiring diverse expertise domains. \n \n Includes 17 pre-built agents covering security, backend, frontend, testing, DevOps, database, mobile, API design, debugging, documentation, performance, planning, SEO, and game development \n Supports three orchestration patterns: comprehensive analysis (discovery through synthesis), feature review (domain-specific agents plus testing), and security audits (auditor plus penetration tester) \n Agen

Status: Experimental | Version: 0.2.0 | Last Updated: 2025-01-27

Resolve all unresolved PR review comments by spawning parallel agents for each thread.

Deep web research, competitive intelligence, entity discovery, and data enrichment using Parallel AI's specialized APIs.

When launching parallel agents for code implementation, prevent type duplication.

Orchestration through Claude Code's built-in Agent Tool

When launching multiple agents in parallel, follow this pattern to avoid context bloat.