NiLang.jl

A differential eDSL that can run faster than light and go back to the past.

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5Distinct people

5Mentions

0Explicit recommenders

251GitHub stars

REPOSITORY AT A GLANCE

What to know before exploring

What it is

A differential eDSL that can run faster than light and go back to the past.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

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Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month5 dated mentions

Observed mentions: 2020-04-08 19:58:57 UTC to 2021-03-06 05:55:34 UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
Apr 2020	2020-04-01 00:00:00 UTC	2020-05-01 00:00:00 UTC	2	0	2
May 2020	2020-05-01 00:00:00 UTC	2020-06-01 00:00:00 UTC	0	0	0
Jun 2020	2020-06-01 00:00:00 UTC	2020-07-01 00:00:00 UTC	0	0	0
Jul 2020	2020-07-01 00:00:00 UTC	2020-08-01 00:00:00 UTC	0	0	0
Aug 2020	2020-08-01 00:00:00 UTC	2020-09-01 00:00:00 UTC	0	0	0
Sep 2020	2020-09-01 00:00:00 UTC	2020-10-01 00:00:00 UTC	0	0	0
Oct 2020	2020-10-01 00:00:00 UTC	2020-11-01 00:00:00 UTC	2	1	1
Nov 2020	2020-11-01 00:00:00 UTC	2020-12-01 00:00:00 UTC	0	0	0
Dec 2020	2020-12-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	0	0	0
Jan 2021	2021-01-01 00:00:00 UTC	2021-02-01 00:00:00 UTC	0	0	0
Feb 2021	2021-02-01 00:00:00 UTC	2021-03-01 00:00:00 UTC	0	0	0
Mar 2021	2021-03-01 00:00:00 UTC	2021-04-01 00:00:00 UTC	1	0	1

What Hacker News says

5 sources

peter_d_sherman·Mar 6, 2021

Interesting! Hadn't heard about NiLang, but will check it out... Update: Looks like the related HN discussion is here: "Nilang.jl – A Reversible Julia DSL": https://news.ycombinator.com/item?id=24743813 https://github.com/GiggleLiu/NiLang.jl (Worth it, in my opinion, for the chart showing the "reversed-functions/variables" preceeded by '∂', i.e., ∂x, ∂y, ∂sqxN, etc.) But anyway, NiLang definitely looks interesting!

In Reversible Computing

e12e·Oct 11, 2020

Given the example of reversing over a function that calculates the first Fibonacci number greater than 100, it seems to be a bit more going on than "just" a numerical approximation: https://github.com/GiggleLiu/NiLang.jl/blob/master/examples/... Ed: after skimming the paper (that went mostly over my head) - this does indeed seem to be about "actually" running functions in reverse - given a function only defined "forward" in the nilang DSL. It appears the graph embed examples are missing in the master branch, unfortunately. I wonder if this can be used more trivially to solve simple problems too - like calculating values/sums pertaining to compound interest/investment, given a naive function for calculating sums etc (its trivial to add up compounded interest and deposits, but a tiny bit more complicated to answer the question "at what time

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is my portfolio at X or more dollars).

In Nilang.jl – A Reversible Julia DSL

Fishysoup·Oct 11, 2020Submission

Nilang.jl – A Reversible Julia DSL

64 points · Open discussion

GiggleLiu·Apr 10, 2020

The github page is https://github.com/GiggleLiu/NiLang.jl. Search “Reversible Computing” and “Reversible programming” for more discussions about reversible programming in Hacker news.

In Show HN: Differentiate Everything with a Reversible Programming Language

oxinabox·Apr 8, 2020

We have so many AD packages than that in julia. Julia suffers the lisp problem for AD. http://winestockwebdesign.com/Essays/Lisp_Curse.html It is a real problem. One of my on going projects is ChainRules (http://www.juliadiff.org/ChainRulesCore.jl/dev/) which will unite them under one set of custom senstitivities and more generally make it easier to mix and match them I need to update the JuliaDiff website, I want to list all of them in a table with some some key points. Forward mode: - [ForwardDiff](https://github.com/JuliaDiff/ForwardDiff.jl) - [ForwardDiff2](https://github.com/YingboMa//ForwardDiff2.jl) Reverse Mode: - [Nabla](https://github.com/invenia/Nabla.jl/) - [Tracker](https://github.com/FluxML/Tracker.jl) - [Yota](https://github.com/dfdx/Yota.jl) - [Zygote](https://github.com/FluxML/Zygote.jl) - [ReverseDiff](https://github.com

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/JuliaDiff/ReverseDiff.jl) - [AutoGrad.jl](https://github.com/denizyuret/AutoGrad.jl) - [NiLang](https://github.com/GiggleLiu/NiLang.jl) (arguably not reverse mode) Symbolic: - [ModelingToolKit](https://github.com/JuliaDiffEq/ModelingToolkit.jl) - [XGrad.jl](https://github.com/dfdx/XGrad.jl) Finite Differencing: - [Calculus](https://github.com/JuliaMath/Calculus.jl) (please stop) - [FiniteDifferences](https://github.com/JuliaDiff/FiniteDifferences.jl) - [FiniteDiff](https://github.com/JuliaDiff/FiniteDiff.jl)

In Jax – Composable transformations of Python and NumPy programs