Bi

Bin CLI – A simple task/script runner for any programming language

Hacker News

Bin CLI – A simple task/script runner for any programming language

Most projects I've worked on included a bunch of scripts for common tasks (installing dependencies, starting/stopping dev servers, testing and deploying changes, and so on). There are a few tools designed for this, such as Just ( https://just.systems/ ) and Task ( https://taskfile.dev/ ), and I know some people use Make as a task runner (e.g. https://news.ycombinator.com/item?id=40333481 ), but I've always preferred the simplicity and universality of shell scripts. Bin CLI is a tool I wrote to add some of the niceties of these tools to shell scripts: it works even when you're currently in a subdirectory, only requires you to type enough of the command (script) name to uniquely identify it, and allows aliases to be defined. It also allows a project's commands to be listed (along with some help text), supports subcommands, and supports tab completion of command names. Of course scripts can be written in other languages too, just by adding the appropriate shebang. Collaborators/contributors who don't have Bin CLI installed can just run the scripts directly - so I can enjoy the benefits without adding a hard dependency or extra barrier to entry. However, I also added support for defining one-liner scripts/aliases in a config file (much like "npm run" scripts are defined in package.json) for times when I want to avoid creating many trivial/wrapper scripts for common tasks. It is implemented as a single Bash script, with minimal dependencies, small enough to keep in my Dotfiles repo (62 KB). I also made .deb and .rpm packages so it can easily be installed system-wide.

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Actual performance

5points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
79%79% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, single, tasks · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
15%15% 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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