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I generate a week of posts from one YouTube video

Hacker News

I generate a week of posts from one YouTube video

Most people struggle with what to post. The blank page is hard to beat. But I found that the next story is often hidden in the videos we already watch. I built a tool called Mermaid Mind that mines story prompts from YouTube videos. Here’s the workflow I use: - Step 1. Pick a video(Paste any YouTube link into Mermaid Mind.) - Step 2. Collect themes The tool extracts 50+ “pill tags” — condensed ideas or phrases from the video. For example, from one talk I got: •Confidence + Humility = Likability •Go Twice As Slow As You Think •Rule of Three - Step 3. Expand into posts Each tag becomes a writing prompt: •Confidence + Humility → A story about earning trust. •Go Twice As Slow → A lesson on slowing down for clarity. •Surprise → A story about an unexpected pivot. Instead of forcing an idea, I now have a story inventory I can dip into for LinkedIn posts, blog articles, or newsletters. Why this helps? Tips are easy to forget. Stories tend to stick. By turning video content into story prompts, I skip the “what should I write?” problem and move straight into writing. The project is live here: https://yttomermaid.xyz Feedback is welcome.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
44%44% 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
40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released
32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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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