Binge-watch or download clips from twitch

Hacker News

I wanted to train my devloppement skill so I made TwiClipSaver.com at a side project from my work. This idea come to my mind after creating many youtube video about clips compilation from twitch, for that I needed downloader for the clip and tools to find best clips so I create my own tools and group them in the same place. In my site, you can found 4 sections: - Download page for downloading the clip by their url - Clips page to watch random clips from twitch - Trending page to watch the most viewed clips by game/date - Blog page to see news about twitch Because I think it can be interesting for other I decide to create this website and share with you to have some feedback about the project and maybe someone have good idea to add more feature. I use Django, HTMX and Tailwindcss/FlowBite to make this website. The next feature I would like to add it's the possibility to create user for liking the clip you like the most and rewatch it. If you have any questions I'll be happy to answer.

Visit product View on Hacker NewsNov 23, 2024

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Actual performance

2points

3comments

Did not reach leaderboard

Launch Intel predictions

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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.

best fitHighest predicted score across all platforms for this description.

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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new · Missing: mac, agents, macos

43%43% 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, io · Missing: https docs, excited, just released

40%40% 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

13%13% 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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