Ex
Extract image URL's from scribd
Hacker NewsExtract image URL's from scribd
# pipe scribd html to this filter # pipe output to your favorite tcp client # pipe the images to your favorite image viewer, pdf writer, djvu creator of whatever scrib(){ sed -n ' s,http://html.scribd.com,\ &,g; s,http://html[1-9].scribdassets.com,\ &,g; s,assets,,; s,pages,images,; s,jsonp,jpg, ; s,html[1-9].scribd.com,html.scribd.com,; s,pageParams.contentUrl = \",,; s,\";,,; s,<img class=\". orig=\",,; s,\"/>,,; /^$/d; /html.scribd.com. images.*jpg/p; ' \ |sed '/http/!d'; } scrib_(){ scrib \ |while read a;do feh $a ; done; }
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: pipe · Missing: https docs, excited, just released62%62% 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: para · Missing: supports, reddit linkedin, podcasting55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos29%29% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue17%17% 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, crypto16%16% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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