Zap – A library for building multi-device applications

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Zap is an application programming library for building multi-device applications that enable communication with other devices. While mobile devices offer a wide range of data sources, such as motion sensors, biometric sensors, microphones, touchscreens and more, traditional PCs like laptops and desktops typically lack these resources. The data sources available on mobile devices are valuable, but are often device-dependent, limiting their widespread use. Imagine if PCs could use the series of data from the accelerometer sensor on a mobile device. A simple example is using a smartphone as motion controller for PC. To overcome the limitation that data sources are confined to a single mobile device, Zap provides its own network protocol and programming interface to access data sources on other devices. The main goal of Zap is to support mobile-PC communication, but it also extends its capabilities to enable mobile-mobile and PC-PC communication. Furthermore, it's not limited to PCs; any devices capable of running Zap implementations(e.g., Kiosk device, Smart TV, etc.) can also participate in this communication.

Visit product View on Hacker NewsDec 5, 2023

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

62%62% 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: single, using · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive

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

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

52%52% 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: ios · Missing: mobile apps, personal, entrepreneurs

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

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Incorrect prediction on native model

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