Mollusk is a lightweight test harness for Solana programs, designed to execute program ELFs in a minified Solana Virtual Machine (SVM) without a full validator runtime. It provisions Agave's program cache, transaction context, and invoke context directly, enabling fast, deterministic testing of single instructions, instruction chains, and full transactions. The repository is organized as a Cargo workspace with core harness logic (mollusk-svm), a result validation crate (mollusk-svm-result), a benchmarking crate (mollusk-svm-bencher), a CLI tool (mollusk-svm-cli), fuzz fixture infrastructure, and bundled SPL program helpers (memo, token, token-2022). Key architectural patterns include a pluggable AccountStore trait for stateful testing, a flexible Check builder for result validation, and a Compare enum for cross-version execution comparison. The CLI supports fixture replay, benchmarking, and generating compute unit reports. The harness also supports fuzz fixture generation (both Mollusk and Firedancer formats) via protobuf schemas, enabling regression testing and cross-implementation comparison. While primarily a testing library, the inclusion of fuzz fixture infrastructure and benchmarking tools gives it a hybrid character similar to data research pipelines for test generation and analysis.
Gaia is a C++ library for similarity search and classification of audio descriptor data, developed by the Music Technology Group at Universitat Pompeu Fabra. It provides a rich set of algorithms for transforming and analyzing feature collections: normalization, PCA, RCA, SVM training/prediction, feature selection, and cleaning. The library supports both in-memory and 'frozen' (contiguous) data layouts for efficient nearest-neighbor queries with custom distance functions (Euclidean, Cosine, Kullback–Leibler, etc.) and a custom filter language for WHERE-clause predicates. Python bindings via SWIG expose the full API, and a suite of command-line tools (gaiamerge, gaiainfo, gaiafreeze) handle dataset management. A built-in classification framework with scripts for SVM grid search, cross-validation, and confusion-matrix generation complements the library. Although designed for music information retrieval (MIR) workflows in conjunction with Essentia audio extractors, the library could be adapted to any domain requiring distance-based search and transformation pipelines on structured point data. The codebase is predominantly an application-level library, with a strong research flavor due to its MIR focus.