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Google Messages for Web" Alternative

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Google Messages for Web" Alternative

I made an open-source alternative to "Google Messages for web" that utilizes WebRTC data channels to show your phone's text messages in browser. It's an Android app you can install on your phone, with a server that uses Socket.io for signaling and coturn for the TURN server. You can also self-host it if you prefer. All source code is in the repository. Product: https://textondesk.lol/ Repository: https://github.com/realchandan/textondesk/ Demo: https://youtu.be/-O3HVSjb_lo

Visit productView on Hacker NewsJan 20, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, code, open · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
76%76% 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 · Missing: supports, reddit linkedin, podcasting
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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
17%17% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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