Ga

Gaiman language for Web-based Terminal applications

Hacker News

Gaiman language for Web-based Terminal applications

I've created programming language based on Ruby that simplifies creating Terminal text-based games and applications in the browser. I've released first 1.0 beta version, but I'm still adding features and fixing bugs. I yet need to add more documentation and improve Gaiman playground. The repo for the language can be found on GitHub: https://github.com/jcubic/gaiman the only documentation so far is the Wiki with [Reference manual][1] and [examples directory][2]. And playground can be found at gaiman.js.org. There is proper tutorial on my TODO list. I've created a game for it (guess the number) that is only 31 lines of code (with empty lines), I've added it to [git][3]. On my road map there is also node.js version, so you will be able to run your apps in the terminal. [1]: https://github.com/jcubic/gaiman/wiki/Reference-Manual [2]: https://github.com/jcubic/gaiman/tree/master/examples [3]: https://github.com/jcubic/gaiman/blob/master/examples/guess_the_number.gs

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4points
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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: apps, code · Missing: mac, agents, macos
45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
34%34% 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
16%16% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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