A one-click random NSFW subreddit button

Hacker News

A one-click random NSFW subreddit button

Hey folks — I built a super simple side project called NSFW Subreddit Picker that does exactly what it says: Click one button, get dropped into a random adult-themed subreddit. Why I built it: I noticed people were Googling things like “random NSFW subreddit” and there wasn’t a good solution. Reddit’s content discovery is great, but chaotic — especially for NSFW stuff. I was curious how far I could take a single static HTML page with good UX and SEO. How it works: urls.txt powers the backend (just a flat file of curated subreddits) JavaScript fetches the list and opens a random one in a new tab There’s also a preview bar and a grid view that dynamically pull image thumbnails from the Reddit API No signups, no tracking — just fun and simple Results so far: Site ranks #6 on Google for “random nsfw reddit” Gets 1,000+ users/month with no ads or promotion Built with plain HTML/JS + some light SEO optimization

Visit product View on Hacker NewsJun 30, 2025

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, google, users · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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