auto-sklearn is an automated machine learning library offering a scikit-learn-compatible interface for classification and regression. It performs hyperparameter optimization via Bayesian optimization (SMAC), integrates meta-learning to warm-start the search from prior runs, and builds ensembles using Caruana's ensemble selection. The codebase is modular: pipeline components (classifiers, regressors, preprocessors) are defined with ConfigSpace and can be extended via a third-party component system. Parallel execution uses dask, and custom metrics, cross-validation splitters, and early stopping are supported. The repository also contains a substantial research component—precomputed meta-features and algorithm run data stored in ARFF format—and scripts for reproducing experiments. The architecture is well-tested with a comprehensive test suite, though some dependencies are pinned to older versions and there is moderate technical drift.
ATLAS is a framework for autonomous AI trading agents that self-improve through market feedback. It implements a 4-layer multi-agent architecture (Macro, Sector Desks, Superinvestors, Decision) where each agent's prompt is treated as trainable weights and optimized via a git-based evolutionary loop. The codebase includes a meta-weighting layer (JANUS) that blends predictions from multiple trained cohorts, and a MiroFish integration for simulating future market scenarios using swarm intelligence. While the repository contains well-structured Python modules for the orchestration layer, simulation bridge, and forward training, the critical agent prompt files and internal data clients are missing (marked proprietary). The result is a reference architecture rather than a runnable system; however, the patterns demonstrated in janus.py and the mirofish bridge are cleanly implemented and could serve as templates for similar meta-learning or simulation-augmented agent systems.