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Clipable – A self hosted video sharing platform

Hacker News

Clipable – A self hosted video sharing platform

We are thrilled to announce the 1.0 release of Clipable, a self-hosted alternative to Streamable built primarily in Go! A few years ago, a few friends and I were devastated when we realized Streamable had deleted almost all of the videos we had uploaded due to a lack of traffic. We realized then that we would need to host something ourselves if we didn't want something like this to happen again. So, after looking around at the other existing self-hosted options and being left unsatisfied, we decided to make something ourselves. With Clipable, you can easily share video content without relying on third-party services. It offers an intuitive user interface, as well as fast uploading and transcoding speeds, alongside an immense amount of configuration to tune it to your needs. Some of the key features of Clipable include: Self-hosted solution: You have full control over your content and can host it on your own server. Fast transcoding: Clipable uses FFmpeg for transcoding, providing fast and reliable video processing. Common Compatibility: We use Postgres as our Database, and S3 for object storage. So, if you don't want to self-host the storage you could defer it to something like Backblaze or AWS. We believe that Clipable can be a great self-hosted video sharing platform, and we're excited to see what you can create with it. Try it out for yourself and let us know what you think! To get started, please visit our Github repository: https://github.com/clipable/clipable

Visit productView on Hacker NewsMar 14, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, coding · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive, host · Missing: plus, reviews, exclusive
81%81% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
80%80% 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 · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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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