Annuaire de skills
Chaque recommandation reste clairement reliée à son dépôt, son audit et son chemin d’installation.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
🌱 Construct Merkle Trees and verify proofs in JavaScript. By @miguelmota
Ultra-fast matching engine written in Java based on LMAX Disruptor, Eclipse Collections, Real Logic Agrona, OpenHFT, LZ4 Java, and Adaptive Radix Trees.
Python implementation of behaviour trees.
Rust implementation of behavior trees for deterministic AI (now with Python bindings)
Local-only web GUI for inspecting agent skills (SKILL.md) across user, project, plugin, cache, and marketplace sources
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals.
A framework for parsing and transforming text in Markdown format written in Swift 6 for macOS, iOS, and Linux. The syntax is based on the CommonMark specification. The framework defines an abstract syntax for Markdown, provides a parser for parsing strings into abstract syntax trees, and comes with generators for HTML and attributed strings.
BTGenBot: a system to generate behavior trees for robots using lightweight (~7 billion parameters) large language models (LLMs)
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
A workflow-driven AI agent framework that executes YAML-defined decision trees.
Annuaire de skills
Chaque recommandation reste clairement reliée à son dépôt, son audit et son chemin d’installation.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
🌱 Construct Merkle Trees and verify proofs in JavaScript. By @miguelmota
Ultra-fast matching engine written in Java based on LMAX Disruptor, Eclipse Collections, Real Logic Agrona, OpenHFT, LZ4 Java, and Adaptive Radix Trees.
Python implementation of behaviour trees.
Rust implementation of behavior trees for deterministic AI (now with Python bindings)
Local-only web GUI for inspecting agent skills (SKILL.md) across user, project, plugin, cache, and marketplace sources
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals.
A framework for parsing and transforming text in Markdown format written in Swift 6 for macOS, iOS, and Linux. The syntax is based on the CommonMark specification. The framework defines an abstract syntax for Markdown, provides a parser for parsing strings into abstract syntax trees, and comes with generators for HTML and attributed strings.
BTGenBot: a system to generate behavior trees for robots using lightweight (~7 billion parameters) large language models (LLMs)
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
A workflow-driven AI agent framework that executes YAML-defined decision trees.
Annuaire de skills
Chaque recommandation reste clairement reliée à son dépôt, son audit et son chemin d’installation.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
🌱 Construct Merkle Trees and verify proofs in JavaScript. By @miguelmota
Ultra-fast matching engine written in Java based on LMAX Disruptor, Eclipse Collections, Real Logic Agrona, OpenHFT, LZ4 Java, and Adaptive Radix Trees.
Python implementation of behaviour trees.
Rust implementation of behavior trees for deterministic AI (now with Python bindings)
Local-only web GUI for inspecting agent skills (SKILL.md) across user, project, plugin, cache, and marketplace sources
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals.
A framework for parsing and transforming text in Markdown format written in Swift 6 for macOS, iOS, and Linux. The syntax is based on the CommonMark specification. The framework defines an abstract syntax for Markdown, provides a parser for parsing strings into abstract syntax trees, and comes with generators for HTML and attributed strings.
BTGenBot: a system to generate behavior trees for robots using lightweight (~7 billion parameters) large language models (LLMs)
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
A workflow-driven AI agent framework that executes YAML-defined decision trees.
Annuaire de skills
Chaque recommandation reste clairement reliée à son dépôt, son audit et son chemin d’installation.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
🌱 Construct Merkle Trees and verify proofs in JavaScript. By @miguelmota
Ultra-fast matching engine written in Java based on LMAX Disruptor, Eclipse Collections, Real Logic Agrona, OpenHFT, LZ4 Java, and Adaptive Radix Trees.
Python implementation of behaviour trees.
Rust implementation of behavior trees for deterministic AI (now with Python bindings)
Local-only web GUI for inspecting agent skills (SKILL.md) across user, project, plugin, cache, and marketplace sources
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals.
A framework for parsing and transforming text in Markdown format written in Swift 6 for macOS, iOS, and Linux. The syntax is based on the CommonMark specification. The framework defines an abstract syntax for Markdown, provides a parser for parsing strings into abstract syntax trees, and comes with generators for HTML and attributed strings.
BTGenBot: a system to generate behavior trees for robots using lightweight (~7 billion parameters) large language models (LLMs)
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
A workflow-driven AI agent framework that executes YAML-defined decision trees.