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I solved my movie night headache

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I solved my movie night headache

There we were, like every other Friday night. I was finishing my glass of wine while pushing away my plate, and out pops the dreaded question. “What should we watch tonight?” She asks. I saw it coming. In fact, I always see it coming. Do I have an answer? Never. But I know how our night will go. We'll sit on the couch, open up the first streaming app, and start the dreaded doom scroll. “This one?” I'd ask. “No, we already watched it.” “How about this?” “I’m not into it.” And so it goes through the apps one by one until we run out of time and settle for a re-run of Sex and the City or The Office. Typical. But this time, I did something different. I pulled out my phone, opened an app I was working on that treats movie selection like a conversation instead of category browsing, and held my breath. What happened next was better than I ever could have imagined. “Sweetheart, what kind of movie are you in the mood for?” “Well, what about something that’s a mix of Top Gun, The English, Amsterdam, Ted Lasso, The Family Stone?” Right. Let’s see how this goes. I entered the prompt, hit return, and out popped 10 suggestions. The Shawshank Redemption, The Green Mile, The Departed, A Beautiful Mind, The Prestige... Whoa. These are all great movies. But I didn’t stop there. The 10 were great, but I wanted something newer. So, I refined the search. “Show me more that were released within the last 5 years.” Bingo. Among the next 10 was a little gem named “The Marriage Story”. Movie night was a hit. And so will yours. Not convinced? Try it for free: watchnowai.com

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, open · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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.

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