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Gabriella439
post-rfc
OPEN SOURCE2015–2023
Gabriella439 / post-rfc

post-rfc

Blog post previews in need of peer review

29Distinct people
35Mentions
1Explicit recommenders
2.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Blog post previews in need of peer review

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

I strongly recommend that you learn Haskell. I have some reasons to back up my endorsement of Haskell. First and foremost, Haskell is the best language to learn if you want to…

nlander used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

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 year35 dated mentions

Observed mentions: 2015-08-17 05:54:14 UTC to 2023-05-03 17:23:43 UTC.

0369122015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 comments20152016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 12 mentions; 1 submissions, 11 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2023

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

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC413
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC12111
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC404
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC202
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC413
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC413
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC312
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC202

From the project README

An excerpt from the repository, fetched Nov 13, 2026.

Read the project’s introduction
Attribution 4.0 International Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an “as-is” basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. Creative Commons disclaims all liability for damages resulting from their use to the fullest extent possible. Using Creative Commons Public Licenses Creative Commons public licenses provide a standard set of terms and conditions that creators and other rights holders may use to share original works of authorship and other material subject to copyright and certain other rights specified in the public license below. 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To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. Section 1 – Definitions. a. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. b. 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Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material. f. Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License. g. Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license. h. Licensor means the individual(s) or entity(ies) granting rights under this Public License. i. Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them. j. Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere i
Read the full README on GitHub

What Hacker News says

35 sources
I went through most of the Hutton book but I already had an elementary background in the language. Then some of the recommended CIS194 course. (3-6 months spent studying total.) Finally I decided upon a project that was right in one of Haskell's strong points: parsing/compilation [0]. Everything was a Google search: how to start a Haskell project with Nix? How parse command-line arguments? How to write a parser for a calculator? What the hell does this compiler error mean??!! Find the answer and get straight back to it. I would still consider myself intermediate, and not proficient, at the language. While it is one person's opinion (albeit well explained) I strongly appreciated Chris Allen's efforts to evaluate existing learning material (prior to co-writing his book) as well as recommending a learning path [2]. Ultimately there is multit
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udinous material to study. For the textbook-like route, choose something that seems sensible and commit, leaving only when it appears to no longer be fulfilling the purpose. Juggling too many artifacts is like interrupting programmers. I don't use VS Code but again, my elementary knowledge of the language, plus sufficiency with GHCi, meant I could stay within my Vim comfort zone. If anyone has a good single resource for configuring Vim for Haskell I'd love to see it? [0] State of the Haskell ecosystem https://github.com/Gabriel439/post-rfc/blob/master/sotu.md [1] Functional Education https://bitemyapp.com/blog/functional-education/ (December 2014) [2] How to learn Haskell https://github.com/bitemyapp/learnhaskell
In Haskell for Beginners
Not the OC and (mostly) not related to Haskell performance, but the following could be useful to you: State of Haskell Ecosystem: https://github.com/Gabriel439/post-rfc/blob/master/sotu.md#c... Guide to Haskell (2018): https://lexi-lambda.github.io/blog/2018/02/10/an-opinionated... Haskell learner milestones: https://stackoverflow.com/a/1016986 GHC optimizations (rather advanced, but interesting): https://stackoverflow.com/questions/12653787/what-optimizati... Compilation of various Haskell tutorials on more advanced topics: https://markkarpov.com/learn-haskell.html Despite the popular saying, there’s a wealth of knowledge about Haskell beyond just academic papers, and in digestable form, but it’s scattered around little-known blogs. I’d recommend you to check out r/haskell from time to time, or subscribe to the Haskell newsletter - at le
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ast for me, those are the places where I got to know most of Haskell blogs.
In Lessons learned from writing ShellCheck
Longer names can be seen as unnecessary and duplicating information. One has to balance between being verbose and laconic when it comes to naming in general, but judging by the lack of annotations in this case, not much care was put into it being easily readable. It may be useful to report it, once that's an issue: chances are it just seemed obvious to the author, as it also often happens to code in general, and acceptable to users. If you see an insufficiently documented and/or straightforward API, and there's no good reason to use that one in particular, I think it may also be a good idea to look for alternatives, in any language. > how's package management? I think it's fine, but there are different opinions on how it should be. I prefer a better system integration and shared libraries coming from system repositories (which is usable w
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ith Debian), Cabal works fine, Stack seems to be popular, some seem to use Nix. > Did you guys ever resolve cabal vs stack? "Resolve" as in choosing a one true way to build/install things? I don't think so, but there is this annual "state of Haskell" survey that includes build tools, which was closed just a few days ago this year (but no results available yet, afaik), though there are the results from last year [1]. "State of the Haskell ecosystem" [2] may be of interest too. There certainly are imperfections/drawbacks (and/or varying approaches/opinions/preferences), and it may not be a good choice for you for making a dynamic website (I think in many cases the overall best choice is the language one is most fluent in at the time), but my replies were mostly with the "I really want to use Haskell for something useful" sentence in mind: being exposed to worse (or at least less suitable) bits and running into a dead end can be unfortunate in that case. [1] https://taylor.fausak.me/2018/11/18/2018-state-of-haskell-su... [2] https://github.com/Gabriel439/post-rfc/blob/master/sotu.md
In A Dead-Simple Web Stack in Haskell
There's a pretty good outline of which kinds of projects Haskell is well suited for by Gabriel Gonzalez here: https://github.com/Gabriel439/post-rfc/blob/master/sotu.md In terms of long-term jobs, I think learning concepts from statically-typed functional programming language will improve your code quality (read: chance at sustainable employment) in general, but actual prospects are probably area dependent. I am lucky enough to live in a tech hub so there are many Angel List jobs here for Haskell and Scala, but this may not be the experience for every city.
In Ask HN: What are you learning in 2019?
https://github.com/Gabriel439/post-rfc/blob/master/sotu.md Game programming Rating: Immature > Haskell is a garbage collected language, so Haskell is more appropriate for the scripting / logic layer of a game but not suitable manipulating a large object graph or for implementing a high-performance game engine due to the risk of introducing perceptible pauses due to GC pauses. Also, for simple games you can realistically use Haskell for the entire stack.
In Advice for Haskell beginners (2017)
It really depends on what you are trying to do and whether this is well-supported by the language. Data science stuff? Python of course. Haskell has things like repa but still very immature compared to Python. You will have to reinvent the wheel a lot if you choose Haskell. But if your developers are going to do something very different from data science, say building a typical web app, Haskell can be a good choice. As usual refer to Gabriel Gonzalez’s State of the Haskell Ecosystem: https://github.com/Gabriel439/post-rfc/blob/master/sotu.md Haskell especially excels in (2): bugginess of typical code is pretty low even without extensive unit testing; Go probably fares pretty well but Python is terrible for a large code base. But on the other hand (3) could also potentially be an issue for Haskell, depending on the geographical location, w
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hereas Python would likely not. Having worked (and still working) in a Haskell startup, though I believe ultimately technology doesn’t really matter that much. The business is more important than the technology. If you do choose Haskell though, my advice is that don’t use too many fancy type system features. A boring business app doesn’t usually need them. Stick to basic language features and perhaps just a dozen most popular language extensions; use the rest very sparingly.
In Python, Go or Haskell?

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