A Raycast script for the HN bookmarklet

Hacker News

Since Arc doesn't have bookmarks, this became the best way for me to post. ``` #!/bin/bash # Required parameters: # @raycast.schemaVersion 1 # @raycast.title Submit to Hacker News # @raycast.mode fullOutput # Optional parameters: # @raycast.icon ./images/yc.png # @raycast.packageName Web Tools # @raycast.argument1 { "type": "text", "placeholder": "URL to submit", "percentEncoded": false } # Documentation: # @raycast.description Submit a URL to Hacker News with the current page title. # @raycast.author DigitalNoumena # Extract the <title> tag page_title=$(curl -sL "$1" | sed -n 's:.*<title>$.*$</title>.*:\1:p') # If no title is found: if [ -z "$page_title" ]; then page_title="No title found" fi # URL encode the title and the URL encoded_url=$(echo -n "$1" | jq -sRr @uri) encoded_title=$(echo -n "$page_title" | jq -sRr @uri) # Open the Hacker News submission page with the encoded URL and title open "https://news.ycombinator.com/submitlink?u=$encoded_url&t=$encoded_title" ```

Visit product View on Hacker NewsSep 12, 2024

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, code, open · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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

27%27% 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

17%17% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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