Dlna-cast, a command line tool to cast PC screen to DLNA devices

Hacker News

dlna-cat is a cross-platform command-line tool that casts screen and media file to remote DLNA device. dlna-cast uses ffmpeg to capture screen and audio, then convert them into HLS streams which could be served by a simple HTTP server. The HLS url will be send to the selected device via uPnP protocol and then you can watch you screen on the remote device (smart TV, typically). This tool is supposed to be cross-platform but currently I don't have a Linux or MacOS device at hand so it can only run on Windows now. It won't be hard to support other platforms though, as there are no platform specific dependencies. HLS is chosen just because it is easy to implement. But the problem of HLS is its high latency (up to 5-10s or more) so it's definitely not for scenarios that require low latency (presentation for example). But as a trade-off the streaming quality exceeds a lot of software screen-casting solutions (Lebocast for example) that have been tested by myself, which make it pretty good to stream music or video playing from your PC to TV.

Visit product View on Hacker NewsNov 14, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, video · Missing: mobile apps, personal, entrepreneurs

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

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, macos · Missing: agents, agent, cursor

34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio, smart · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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