Pornwitch.com – Category-Based Porn Search

Hacker News

Pornwitch.com – Category-Based Porn Search

Hey HN, We’ve launched a new porn search site built with Next.js and Elasticsearch. The main problem we’re tackling is how most adult sites struggle to handle complex searches with multiple categories. On PornWitch, you can mix and match as many categories as you want, and the results will always match all your selected filters. Right now, we’re pulling results from Pornhub, XHamster, XVideos, and Eporner, with plans to add more sources soon. Check it out and let us know what you think! We’re open to any feedback, feature requests, or ideas. If it fits with what we’re aiming for, there’s a good chance it’ll be added.

Visit product View on Hacker NewsNov 16, 2024

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3points

9comments

Did not reach leaderboard

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

78%78% 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.

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

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% 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

16%16% 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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