A

A tool that crawls a subreddit, downloads images

Hacker News

A tool that crawls a subreddit, downloads images

This is a tool I coded in AutoIt that crawls a Subreddit RSS and finds images to download. If the link leads to a gallery, it crawls that gallery for other images. This is intended for private use only. I don't want you to vacuum pictures off the various image boards this program crawls and call them your own and blame it all on me. Just use it if you want to get cool wallpapers every day or something that's not copyright infringing. Source is included this time. Yes it's a mess, yes I didn't use switch(), yes I should have added more comments and I should name my variables better. And yes, I should put more things into functions. But I wrote it iteratively from a 10 line program I came up with late at night and by the time I was finished, it was 4:30 early morning. The list of URLs to fetch is in sitelist.txt Add your own there. Download it here: https://sites.google.com/site/bryanbottebell/bryan-bottebell-s-projects/RedditImageGrab.zip

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% 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.
TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: google, code · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
39%39% 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
13%13% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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