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I Built this to repurpose YouTube Videos into posts to save time

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I Built this to repurpose YouTube Videos into posts to save time

So I watch a lot of educational YouTube videos for work. I'd always find myself wanting to share the good stuff with my network on LinkedIn or tweet about it. But here's the thing that drove me crazy: I'd spend ages trying to summarize a 20-minute video into a decent post. Copy some quotes, try to capture the main points, format it nicely... and it would take me like 30 minutes just to write one post. Then I started thinking - if this is painful for me as just a consumer, imagine how much worse it is for actual content creators and YouTubers + social media managers. They're making all this great content on YouTube but then have to spend hours manually converting it for LinkedIn, Twitter, etc. just to reach different audiences. I figured there had to be a better way, so I built this thing called ThinkYT. You just paste a YouTube URL and it turns into posts for different platforms. No more sitting there rewinding videos trying to get the exact quote or struggling to remember what the third point was. What used to take me 30+ minutes now takes like 2 minutes. I would love to hear your feedback!

Visit productView on Hacker NewsAug 21, 2025
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
90%90% 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.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, way, education · Missing: mobile apps, ios, personal
28%28% 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
24%24% 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
23%23% 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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