Made a quick filesharing bash script inspired by another Show HN post

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I got inspiration from this post https://news.ycombinator.com/item?id=33094627 Here is the script #!/bin/bash qrcode() { qrcode-terminal "http://"$(ip addr show wlp3s0 |grep -Eo $ipregex |head -n1 )":9000/" cd ~/.webshare && python3 -m http.server 9000 } share() { if [[ "$1" = "f" ]] then echo "success $1" [[ ! -d ~/.webshare ]] && mkdir -p ~/.webshare #cp -r --reflink "$2" ~/.webshare/ path="$(realpath "$2")" ln -s "$path" "/home/sanbotbtrfs/.webshare/" qrcode elif [[ "$1" = "s" ]] then path="$( find "$2" |fzf )" path="$(realpath "$path")" ln -s "$path" "/home/sanbotbtrfs/.webshare/" qrcode else error fi } cleanup() { echo 'cleanup operation ' rm -vrf ~/.webshare/* } error() { echo 'usage share [s/f] <filename/dir> ; s is for fuzzy search and f is regular ' } trap cleanup 1 2 3 6 14 15 ; [[ $# -eq 2 ]] && share "$1" "$2" || error

Visit product View on Hacker NewsOct 6, 2022

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Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

49%49% 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 HuntUnlikely to reach the leaderboard · Strong signals: new, code · Missing: mac, agents, macos

34%34% 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

32%32% 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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

24%24% predicted probability of success on BetaList, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

19%19% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

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