4 boosters for "gpu-accelerated" — open source, verified from GitHub, ready to install
A practical guide for deploying serverless Python applications on Modal, enabling developers to run GPU-accelerated AI/ML workloads, web APIs, and batch jobs with minimal infrastructure configuration.
Generate production-grade animations using Motion.dev following Apple/Jon Ive principles: ❌ Don't use for: Determine project context and animation goals:
"name": "inference-builder", "description": "Generate deployable Vision AI pipelines with high-performance video and streaming capabilities on NVIDIA GPUs. Create GPU-accelerated inference microservices using DeepStream, Triton, vLLM, TensorRT-LLM, or PyTorch backends.",
"name": "claude-local-docs", "version": "1.0.23", "description": "Local-first Context7 alternative — indexes JS/TS dependency docs with a 4-stage RAG pipeline (vector + BM25 + RRF + cross-encoder reranking). Uses TEI Docker containers for GPU-accelerated embeddings and reranking.",
