Floodcraft – Short videos on any topic in 30 seconds

Hacker News

Hey HN, Floodcraft gives you a short AI-generated video slide show on any topic in about 30 seconds. It mimics the style and format of an Instagram Reel/ YouTube Shorts. Here's a launch video for Floodcraft that I generated using Floodcraft! - https://www.floodcraft.com/videos/Y_rOqAMpy22t7bXx3AyiJ The home page has a few more sample videos [1]. Since the images are generated, it works best for non-realtime topics like history, literature, arts etc; basically anything that wouldn't need to reference specific or latest images. Will incorporate real images soon. I'm a visual learner and have spent hours watching YouTube Shorts (especially from the Epic History channel) so built Floodcraft to get similar videos in realtime for any topic that I wanted. I've always been interested in knowledge ingestion and improving the way people learn. Previously, I built Smort.io [2] to let people easily edit, annotate, and share articles & ArXiv papers. Just prepend smort.io/ before any article url read in Smort. The Show HN post [3], got on the front page of HN and more than 50,000 people from 132 countries have used Smort so far. [1] https://floodcraft.com [2] https://smort.io [3] https://news.ycombinator.com/item?id=30673502

Visit product View on Hacker NewsMay 14, 2024

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

59%59% 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: video, way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% 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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