Slick

AppSumo

Customers appreciate Slick for its user-friendly interface and customizable subtitles. Some users have reported issues with AI-generated B-roll quality and limitations in video upload size.

Customers appreciate Slick for its user-friendly interface and customizable subtitles. Some users have reported issues with AI-generated B-roll quality and limitations in video upload size. Despite these minor drawbacks, Slick is a solid buy for those in need of a viral shorts creation tool. With an overall rating of 3.5 and a 60-day money-back guarantee, it's worth giving Slick a try.

Visit product View on AppSumoMar 4, 2024

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Actual performance

22reviews

Did not reach leaderboard

Traction signals

Rating3.5 / 5

Purchases830

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: friendly, interface, overall rating · Missing: plus, platform, intuitive

92%92% predicted probability of success on AppSumo, 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

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, users · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

23%23% predicted probability of success on Hacker News, 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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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