ma

manpages-tldr – short manpages with examples

Hacker News

manpages-tldr – short manpages with examples

My small project similar to bropages and tldr, but (finally!) using manpages only. Manpages generated from Markdown files by Pandoc.

Share card

Actual performance

48points
31comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
27%27% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
15%15% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Ho
How To CoffeeScript | Handy and Short Examples Of CoffeeScript38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

How To CoffeeScript | Handy and Short Examples Of CoffeeScript

Hacker News2
Re
RegexGo, get regex by providing examples44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

RegexGo, get regex by providing examples

Hacker News1
Re
RegexGo – Regex Generator from Examples40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

RegexGo – Regex Generator from Examples

Hacker News8
Si
Simple XState Examples53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Simple XState Examples

Hacker News4
Fr
Freeter dashboard examples58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Freeter dashboard examples

Hacker News1
Co
Counting 1121 BigData and MachineLearning Framework Toolset and Examples49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Counting 1121 BigData and MachineLearning Framework Toolset and Examples

Hacker News3
Ge
Generalized CSS Selector via Examples66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Generalized CSS Selector via Examples

Hacker News1
Mi
Minimal Terraform Examples47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Minimal Terraform Examples

Hacker News3
TD
TDD Bottom-up vs. Top-down by examples51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

TDD Bottom-up vs. Top-down by examples

Hacker News4
Py
PyQt Courses and Examples58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PyQt Courses and Examples

Hacker News10