7 boosters for "pytorch" — open source, verified from GitHub, ready to install
Real-time monocular depth estimation using Depth Anything v2. Transforms camera feeds with colorized depth maps — near objects appear warm, far objects appear cool. When used for privacy mode, the blend mode fully anonymizes the scene while preserving spatial layout and activity, enabling security
Ray is an expert booster for Apache Ray distributed computing that helps developers convert Python code to Ray workloads, debug applications, and optimize performance across Ray's ecosystem (Core, Data, Train, Serve, Tune). Ideal for ML engineers and Python developers scaling computations from single machines to clusters.
FHIRy is a Python package that converts FHIR healthcare data into pandas DataFrames, enabling data scientists and healthcare developers to perform analytics, machine learning, and AI on standardized health records. It's ideal for researchers and engineers working with health data who need quick, programmatic access to structured clinical datasets.
Lár Windsurf Rules is a cursor ruleset that guides developers in building graph-based AI agents using the Lár framework with strict typing, explicit node linking, and auditable patterns. It benefits developers building complex, self-correcting agentic systems who need clear structural guidelines.
Automates cleanup of JupyterHub Docker environments by stopping containers and removing orphaned resources. Ideal for data scientists and ML engineers managing local Jupyter development platforms who need to free up disk space and reset their environments.
An ML engineer agent that handles end-to-end production ML workflows including model serving, feature engineering, A/B testing, and monitoring for TensorFlow/PyTorch deployments. Ideal for teams building scalable ML systems who need guidance on MLOps best practices and production readiness.
Heuristic scoring (no AI key configured).
