OYoyahud·Mar 27, 2026
In Show HN: Think Fast. Type Fasterdwyl
english-words
Python2016–2026
dwyl / english-words
english-words
:memo: A text file containing 479k English words for all your dictionary/word-based projects e.g: auto-completion / autosuggestion
7Distinct people
7Mentions
0Explicit recommenders
12.3KGitHub stars
What to know before exploring
What it is
:memo: A text file containing 479k English words for all your dictionary/word-based projects e.g: auto-completion / autosuggestion
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
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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 year7 dated mentions
Observed mentions: 2016-11-23 10:16:56 UTC to 2026-03-27 13:13:38 UTC.
* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
7 sourcesHey HN, Josh here. I’m a frontend developer at a devtools company, and I keep noticing obvious mistakes like typos and accessibility problems make it into production.
I wanted to find something like pagespeed insights that would run against commits when they were first pushed, instead of after changes were deployed
That’s why I built Website Linter, a zero-configuration tool that scrapes pages on your site to find layout, spelling, and accessibility issues. It also gives you screenshots of the above-fold content across multiple device sizes.
Horizontal and spelling errors are easily overlooked during code reviews, which is why they enter prod often. Website linter helps you catch those issues as they appear instead of weeks later.
Under the hood, website linter uses:
- Lighthouse to get performance metrics and screenshots for different de
Read the rest
vice sizes
- Pa11y to find accessibility issues
- Chromedp in Go to get HTML nodes that are larger than the width of the screen
- Fuzzy (in Golang) to find spelling errors on a trained dictionary (using a cleaned version from the words_alpha.txt file here: https://github.com/dwyl/english-words
I built Website Linter by re-using the infrastructure from the startup I work for, but you can use it on any internet visible page. Would love to know if you try it out!
To see what it looks like, visit here: https://webapp.io/website-linter-report/0 for a sample report (netlify’s homepage) or install webapp.io on GitHub: https://github.com/apps/webappio/installations/new and go to the “website linter” tab in the webapp.io dashboard after signing up.
There is also /usr/share/dict/words or https://github.com/dwyl/english-words which can be used.
In Solving Wordle with Z3Same here.
bff [1] has no fancy assembly, not even a compiler, just some basic loop unrolling and jump precomputation, but I too wonder how it compares to wc.
[1] https://github.com/apankrat/bff/
-- edit --
Found words.txt with 466551 words, so replicating it 28 times gets it to the OP's test size. Bff is ~5x slower than 'wc -w' ... which is not unexpected, but still slightly disappointing.
https://github.com/dwyl/english-words/blob/master/words.txt?...
In Beating C with one line of BrainfuckIt's basically just a DNS system (providing friendly names) for GPS coordinates (which, like IP addresses, are numbers and are hard to remember off the top of your head).
I think it's kind of dumb that it requires a company to run, though. There are around 170,000 words in the oxford english dictionary. If you take any combination of three random words, you would have a grand total of 170,000 x 170,000 x 170,000 possible permutations, or 4,913,000,000,000,000 possible permutations.
If you only took the top 50,000 most commonly used words, then you would have 50,000 x 50,000 x 50,000 = 125,000,000,000,000 possible permutations. That's 125 trillion combinations.
Guess how much disk space a word list of 479,000 words takes up? Oh, only 4.5 megabytes. (see: https://github.com/dwyl/english-words)
It would be extremely easy to build an index of
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words, number them, then correlate them to GPS coordinates, all with a simple client-side algorithm.
There is literally no reason at all why this needs to be a single company in control of this. It could be a completely open source project, maintained by the community. And it wouldn't need servers, because all you would need is a small client-side app that would convert the numbers of your current GPS coordinates to/from the word list combos (which would only be a couple megabytes in size). This could be easily distributed as an android or ios app that would run completely offline.
I'd go so far as to say that 2048 words aren't that many.
Here's Merriam-Websters 3000 _core_ words [1].
Here's a list of 355k words [2].
[1] http://learnersdictionary.com/3000-words
[2] https://github.com/dwyl/english-words
In NIST’s new password rules – what you need to know