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Makedown – A Markdown powered Makefile alternative

Hacker News

Makedown – A Markdown powered Makefile alternative

`makedown` allows you to organise your shell scripts in one or several markdown files, by mix and matching different scripting languages for various commands and needs. zsh/bash/sh, python, javascript or anything else available on your system. Handy for replacing one-line-based package.json scripts or shell-based Makefiles. One can also write documentation and explanations to various commands in the same `.md` file. Most editors highlight correctly most languages in the markdown code blocks, even when you use several scripting languages. Here is a demo .md file https://github.com/tzador/makedown/blob/main/DEMO.md More information available in the https://github.com/tzador/makedown/blob/main/README.md Provided the following `example.md` in the root of your project, the defined commands are available to run from any of the projects subfolders: --- Start of example.md --- # hello Prints "Hello" to `stdout` using Zsh. ```zsh echo "Hello" ``` # world Just prints "World" to `stdout` using JavaScript. ```js console.log("World"); ``` # weather-tomorrow Prints the weather for tomorrow to `stdout` using Zsh. ```zsh curl wttr.in/tomorrow ``` # generate-password Prints a random password to `stdout` using Python. ```python import random import string length = 16 characters = string.ascii_letters + string.digits + string.punctuation password = ''.join(random.choice(characters) for _ in range(length)) print(password) ``` --- End of example.md --- You can run any of the commands from anywhere in the project, just by typing `makedown a-command-name` or a shorter `m a-command-name`. $ makedown --help hello - Prints "Hello" to `stdout` using Zsh. world - Just prints "World" to `stdout` using JavaScript. weather-tomorrow - Prints the weather for tomorrow to `stdout` using Zsh. generate-password - Prints a random password to `stdout` using Python. $ makedown hello Hello $ makedown world World $ m weather-tomorrow Sunshine # prints more details actually $ m generate-password 4444444444444444 $ m generate-password --help Prints a random password to `stdout` using Python. The commands have simple syntax, they start with a header with a link and stop when the next header starts. Like so: # [a-command-name]() A short description. Some documentation. ```bash some command ``` You can use other interpreters, like `python`, `node`, `ruby`, etc. You can also use a custom interpreter specified using hashbang, like: # [run-deno-script]() Runs a script using the Deno interpreter. Deno has to be installed on your system. ```typescript #!/usr/bin/env deno run const message: string = "hello, world"; console.log(message); ``` All the .md files in the current directory and all the parents are examined when looking for commands. Would be very grateful for any suggestions or other feedback. Thank you.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: using, code · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
45%45% 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
42%42% 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
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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