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My porn startup

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My porn startup

Ever find good porn but can't get to it right away? What about something you really enjoyed, where do you save it? If you're like me, you don't want nsfw sites in your browsing history and especially not in your bookmarks. So I built http://xstashed.com/hn/ (clickable in comments) - a place to stash your nsfw links. While it's a minimal viable product, I do have a basic roadmap that can be viewed here http://xstashed.com/todo/. I just wanted to get this in the hands of potential users to get feedback early on. On the tech side of things I've built this thing nimble so that I can quickly shape the site from your feedback. For those who are interested in the technical site of xstashed, it's a custom Django (Python) application continuously deployed with Chef. MySQL for database, Celery for background task, and Redis for cache and Celery broker.

Visit productView on Hacker NewsJan 28, 2013
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
30%30% 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
15%15% 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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