Rebugger.jl

An expression-level debugger for Julia with a provocative command-line (REPL) user interface

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2Distinct people

2Mentions

0Explicit recommenders

173GitHub stars

REPOSITORY AT A GLANCE

What to know before exploring

What it is

An expression-level debugger for Julia with a provocative command-line (REPL) user interface

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, 3:56:07 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 month2 dated mentions

Observed mentions: 2018-09-07 16:18:54 UTC to 2019-07-12 18:44:08 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	Comments
Sep 2018	2018-09-01 00:00:00 UTC	2018-10-01 00:00:00 UTC	1	1
Oct 2018	2018-10-01 00:00:00 UTC	2018-11-01 00:00:00 UTC	0	0
Nov 2018	2018-11-01 00:00:00 UTC	2018-12-01 00:00:00 UTC	0	0
Dec 2018	2018-12-01 00:00:00 UTC	2019-01-01 00:00:00 UTC	0	0
Jan 2019	2019-01-01 00:00:00 UTC	2019-02-01 00:00:00 UTC	0	0
Feb 2019	2019-02-01 00:00:00 UTC	2019-03-01 00:00:00 UTC	0	0
Mar 2019	2019-03-01 00:00:00 UTC	2019-04-01 00:00:00 UTC	0	0
Apr 2019	2019-04-01 00:00:00 UTC	2019-05-01 00:00:00 UTC	0	0
May 2019	2019-05-01 00:00:00 UTC	2019-06-01 00:00:00 UTC	0	0
Jun 2019	2019-06-01 00:00:00 UTC	2019-07-01 00:00:00 UTC	0	0
Jul 2019	2019-07-01 00:00:00 UTC	2019-08-01 00:00:00 UTC	1	1

What Hacker News says

2 sources

ddragon·Jul 12, 2019

>since I'm relying on fast editing/execution cycles While you can't do it from the shell very well right now (rerunning the program at each step like you would with an interpreted language), that kind of fast cycle is something very common in Julia development but with a particular REPL based workflow [1] in which you use a tool like Revise.jl [2] to automatically update the definition whenever you save a file in your project (the only restriction is that it doesn't automatically updates new type definitions) and directly interacting with the program in the REPL. This way it will only recompile what you just altered, and it's very fast to actually run the code. Other interesting tools are Rebugger.jl (debugger for the REPL) [3] and OhMyREPL (coloring for the REPL) [4], which you can add to your startup.jl to always automatically load them

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. [1] https://docs.julialang.org/en/v1/manual/workflow-tips/index.... [2] https://github.com/timholy/Revise.jl [3] https://github.com/timholy/Rebugger.jl [4] https://github.com/KristofferC/OhMyREPL.jl

In Statistics with Julia [pdf]

celrod·Sep 7, 2018

The beauty of Julia is in its design as a language. This is what gives people hope that the tooling and ecosystem will emerge -- they all seem easier to develop in Julia than in other languages, thanks to multiple dispatch, the type system, and powerful metaprogramming abilities. As far as fast bare metal languages go, you can write extremely general optimized matrix multiplication libraries in Julia. For matrices that fit in the L2 cache, I achieved performance similar to Intel MKL, but with far more generic code. https://discourse.julialang.org/t/we-can-write-an-optimized-... Writing a kernel in C means using SIMD intrinsics, where function names and types differ for every vector size, and then different numbers/sets of calls for each kernel (you'd want several sizes per architecture). Compare this to Julia, where parametric typing and

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multiple dispatch mean you only need one set of functions, and using an @generated function will generate whatever kernels you happen to need at compile time. Looking into the future, awesome projects like Cassete speak to the potential of what's possible: https://github.com/jrevels/Cassette.jl Cassete promises to let you do anything from getting automatic differentiation for arbitrary Julia code, even if it is strictly typed and buried in a chain of dependencies of the library you're using and written by someone who never imagined the idea of autodiff, to injecting custom compiler passes. Also, while I haven't tried it yet, many seem to like: https://github.com/timholy/Rebugger.jl Julia is more promise than practice right now, but that's largely because of just how much it promises. I (and many others) think it has done a great job delivering so far. That's why there's excitement, and why many are starting to embrace it.

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