nowfound

Life & fun · April 24, 2021

Kotlin∇: Shape-Safe Symbolic Differentiation with Algebraic Data Types

🧩 Shape-Safe Symbolic Differentiation with Algebraic Data Types - breandan/kotlingrad

Kotlin∇: Shape-Safe Symbolic Differentiation with Algebraic Data Types launched on April 24, 2021 with 15 votes, #1,247 of 3,754 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 5 years on.

Visit github.com ↗Alternatives

In plain words

Kotlin∇ is a symbolic differentiation library built for the Kotlin programming language using algebraic data types. It enables developers to compute derivatives of mathematical functions while maintaining type safety throughout the process. The tool is designed for mathematicians, scientists, and engineers who need reliable automatic differentiation capabilities within Kotlin projects. Its shape-safe approach ensures that dimensional and structural correctness is verified at compile time rather than runtime.

written from the facts on this page · September 2026

Does a similar job

all alternatives →
  • Kotlin Money2024 · blog.eriksen.com.br · ▲427

    Manipulating monetary amounts is a common computing chore. However, no mainstream language has a first-class data type for representing money, it’s up to programmers to code abstractions for it. This isn’t an issue per se until dealing with rounding issues from operations like installment payments (e.g., buy now, pay later), foreign exchange, or even simple things like fee processing and tax collection. Inspired by my days at N26 Brasil dealing with these challenges, I introduce Money: a Kotlin library that makes monetary calculations and allocations easy.

  • Boldly go where Gradient Descent has never gone before with DiscoGrad2024 · github.com · ▲232

    Trying to do gradient descent using automatic differentiation over branchy programs? Or to combine them with neural networks for end-to-end training? Then this might be interesting to you. We develped DiscoGrad, a tool for automatic differentiation through C++ programs involving input-dependent control flow (e.g., "if (f(x) < c) { ... }", differentiating wrt. x) and randomness. Our initial motivation was to enable the use of gradient descent with simulations, which often rely heavily on such discrete branching. The latter makes plain autodiff mostly useless, since it can only account for the…

  • CD
    Clojure DSL for pattern-matching, parsing and generic programming2012 · scattered-thoughts.net · ▲204
  • Eole, a Lévy-optimal lambda calculus evaluator written in Rust2019 · github.com · ▲106

    Éole, a Lévy-optimal lambda calculus evaluator without oracle - HerrmannM/eole

  • val – An arbitrary precision calculator language2025 · github.com · ▲34

    Wrote this to learn more about the `chumsky` parser combinator library, rustyline, and the `ariadne` error reporting crate. Such a nice DX combo for writing new languages. Still a work in progress, but I thought I'd share :)

  • Peridot – A functional language based on two-level type theory2022 · github.com · ▲151

    A fast functional language based on two level type theory - ehatti/peridot

Questions this answers

More life & fun this month

the category →

Source: Show HN post ↗

Launched alongside, April 2021

the whole month →
  • Lofi.cafe▲1,192

    Lofi music streams for studying, working, and relaxing.

    Life & fun · 2021 · lofi.cafe

  • Mighty▲931

    Make Chrome faster and use 10x less memory

    Work · 2021 · playgroundai.com

  • FigJam by Figma▲864

    An online whiteboard from Figma designed for teams

    Work · 2021 · figma.com

  • Butter▲848

    All-in-one tool for the smoothest virtual workshops 🧈

    Work · 2021

  • Landing Page Checklist▲809

    Build your landing page with these 100+ tools

    Growth · 2021 · landingpage.fyi

  • PF
    PDFs from HTML▲742

    Life & fun · 2021 · pdf.math.dev