I created a shell like website in plain JavaScript

Hacker News

Here's the link: https://ssrangisetti.github.io/jsshell/. It supports pipes, tab completions and history. It can type some commands on page load like https://ssrangisetti.github.io/jsshell/#cmd=cat.js%20docs/help.md|md.js&intervel=50. It's built to understand html elements instead of normal terminal codes. So we can do something like below: https://ssrangisetti.github.io/jsshell/#cmd=echo%20%3Ca%20href=%22javascript:term.typeAndExecute('cat.js%20docs/help.md%26%23x7c%253Bmd.js')%22/%3Eclick%20here%20for%20docs%3C/a%3E|md.js or https://ssrangisetti.github.io/jsshell/#cmd=echo%20%3Cbutton%20onclick='document.getElementById(%22rick-roll%22).style.display=%22block%22'%3Eclick%20here%20for%20a%20surprise%3C/button%3E%3Cimg%20src=%22https://media.tenor.com/SSY2V0RrU3IAAAAd/rick-roll-rick-rolled.gif%22%20id=%22rick-roll%22%20style=%22display:%20none%22%3E|md.js it's based on https://uni.xkcd.com/ and https://aava.sh/

Visit product View on Hacker NewsApr 21, 2023

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created · Missing: reddit linkedin, podcasting, latex

83%83% 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: code, plain · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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.

Correct prediction on native model

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