We

We spent the last 18 months building an interactive live video stream

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We spent the last 18 months building an interactive live video stream

Hey HN!! Many of us enjoyed the "If YouTube had actual channels" Show HN post last week. As developers of a similar project, we wanted to share our approach to building a simulated live experience. We’ve spent around 18 months working on this and have managed to add some advanced features, including content-based interactivity. We are super excited to be releasing both the live stream and the code behind it. Our idea was to combine the communal watching experience of television with the modern trappings of the interactive web. If you go to the stream, you will be watching along with everyone else. You can click around, get more information on what’s in the shot, and even purchase what you see without leaving the page. On the tech side, we learned a ton along the way. Today, we are sharing the code so others can hopefully learn something too. To be totally honest, the code embarrasses me. It's way too early to share this and far from a completed open-source project. It’s an extreme example of “doing things that don’t scale” so please do not expect to just fire this up and run it in production. However, it does demonstrate solutions to a number of hard problems we faced—for example, handling graceful updates to streams that are currently live and supporting reliable timed metadata. We would love any feedback and will be hanging around here to answer any questions that may come up. Github: https://github.com/james-a-rob/KodaStream Live stream: https://www.sneakinpeace.com Main tech: TypeScript | FFmpeg | HLS

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
85%85% 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: activity, code, open · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
71%71% 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, month, way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, active · Missing: mrr, revenue, profit
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.

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