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CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python, Matlab/Octave, Julia or Javascript

automatic differentiation made easier for C++

  • automatic-differentiation
  • auto-differentiation
  • autodiff
  • autodifferentiation

Universal SKILL.md catalog for research workflows: literature review, research design, project memory, manuscript writing, and cross-agent delegation for Claude Code, Codex, Gemini, Cursor, OpenClaw, and generic AI clients.

  • academic-writing
  • ai-skills
  • notebooklm
  • obsidian

The fastest automatic differentiation library for C++, with Python bindings. Forward, adjoint and higher-order derivatives, checkpointing, Eigen support and JIT. Reproducible benchmarks against CppAD, Adept and autodiff.

  • automatic-differentiation
  • aad
  • algorithmic-differentiation
  • autodiff

High-performance Bayesian Data Analysis on the GPU in Clojure

  • clojure
  • bayesian-inference
  • bayesian-data-analysis
  • gpu-computing

The TiGL Geometry Library to process aircraft geometries in pre-design.

💬 Trace citation chains for any concept across research papers and render them as an interactive graph.

  • academic-research
  • bibliography
  • citation-graph
  • cli

Ruby gem to calculate statistics from text to determine readability, complexity and grade level of a particular corpus.

  • textstat
  • flesch-kincaid-grade
  • smog
  • flesch-reading-ease

ODE system solver made simple. For IVPs (initial value problems).

  • runge-kutta-fehlberg
  • runge-kutta
  • simulation
  • ode-solver