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Another Yt-Dlp Wrapper

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Another Yt-Dlp Wrapper

I've recently been working on a computer vision project and needed to download quite a lot of sample clips from YouTube. The existing online tools are littered with ads, watermarks or paywalls just for >360p quality videos. I discovered yt-dlp but it wasn't the most user friendly, so I built a desktop GUI wrapper to speed up my workflow. yt-clipper lets you download specific segments from YouTube videos without grabbing the entire file. You paste a YouTube URL, set your start and end times, pick your quality, and download exactly what you need. No ads, no artificial limits beyond what YouTube itself imposes, no account required. The tool runs yt-dlp under the hood but wraps it in a simple interface. You get the power of yt-dlp without dealing with command line arguments or documentation. Works on Mac and Windows (sorry Linux users). Built this because I was tired of downloading 20-minute videos when I only needed 30-second clips for training data.

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2points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% 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 · Strong signals: mac, user, computer · Missing: agents, macos, agent
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: friendly, interface, user friendly · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
55%55% 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, users · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
15%15% 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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