#machine-learning

Kusto and Log Analytics MCP server help you execute a KQL (Kusto Query Language) query within an AI prompt, analyze, and visualize the data.

Fast Multi-dimensional Sparse Attention

Self-tuning multi-agent AI trading system. 8-source signal fusion, Bull/Bear/Judge debate on Claude Opus 4.7, Kelly + ATR position sizing. Python · Kalshi + Polymarket adapters.

Self-hosted classroom for teaching Python, data science and ML together. Students join from a browser with just a name

Smart Media enhancements for WordPress

Model-agnostic generative vision for Python and AbstractCore — text/image/video generation via MLX-Gen, Diffusers, stable-diffusion.cpp, and OpenAI-compatible APIs.

🤖 Faker provider for generating AI/ML fake data - models, companies, frameworks, datasets

A command-line tool to extract plain text from Wikipedia dumps with category and section filtering

Simple python interface for the OpenProtein.AI REST API.

AI-enhanced computational chemistry

Fast and memory-efficient library for WordPiece tokenization as it is used by BERT.

An interactive HTML pretty-printer for machine learning research in IPython notebooks.

Google Research

LLMs as Copilots for Theorem Proving in Lean

An imperative command-line-interface for AI workload orchestration

Anomx is a library for time series datasets, forecasting, modeling, and anomaly detection. It provides modular tools for building interpretable workflows that help researchers and engineers understand temporal patterns, detect unusual behavior, and evaluate predictive models.

Comprehensive toolkit for MALDI-TOF mass spectrometry data preprocessing for antimicrobial resistance (AMR) prediction purposes

ObjectiveAI API Server, SDKs, and utilities

A scientific-method runtime for AI agents: hypotheses, experiments, evidence, provenance, reproducibility, and auditable conclusions.

Fast DVC-compatible accelerator for add/status and S3-backed data sync

A Python library for audio data augmentation. Useful for making audio ML models work well in the real world, not just in the lab.

Use any AI model, locally or in the cloud, while tracking on-device token usage and collecting token telemetry data.