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Share and Critique Each Other's YC 120 Application Videos

Hacker News

Share and Critique Each Other's YC 120 Application Videos

As per [1], this space is for people to exchange and critique each other's YC 120 application videos. Applicants: Post the link to your YC 120 video in a top-level comment. See [2] for instructions. Minimize extraneous text: YC 120 applications don't leave a lot of room for additional text anyway, so it's best if your video can stand on its own. Critiques: Attach critiques as second-level comments under the videos you're critiquing. If you need additional guidance on how to critique, I recommend Mary Robinette Kowal's excellent infographic on the topic [3]. Replying to Critiques: Reply to critiques only for clarification. Avoid defending yourself; arguing doesn't accomplish anything. I recommend [3] if you're struggling to figure out how to interpret feedback. Others: Avoid discussion of YC 120 itself; there have been other posts for that and you can start a new one if you want to discuss it more. Good luck everyone, and don't forget to enjoy the journey! [1]: https://news.ycombinator.com/item?id=19028916 [2]: https://blog.ycombinator.com/yc-120/ [3]: https://www.patreon.com/posts/manuscript-arent-11552026 (while intended for novels, much of it is generally applicable)

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Launch Intel predictions

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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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
22%22% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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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