Push real-time data to mobile apps w/o sockets, polling, or backend

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Hey HN, I just launched Pushlytic ( https://pushlytic.com ), a real-time push messaging library for mobile apps. It lets you stream data to mobile apps without websockets, polling, or maintaining a backend - all using gRPC (this isn't push notifications like APNS/FCM - it's real-time data streaming). Having worked with similar real-time push features at large tech companies using gRPC, I noticed smaller teams and apps often don't have resources to implement this functionality - they either struggle with managing the infrastructure or rely on inflexible third-party services. The library allows you to add real-time features with just a few lines of code and handles all the complexity - connection management, retries, etc. The best part? You can automate workflows to send any structured data you want without running your own backend. Set up triggers based on events, schedules, or conditions - perfect for: * Delivery tracking * Instant deals * Shopping cart promos * Feature announcements Right now we support iOS and Android, with SDKs available on GitHub (working on React Native): iOS SDK: https://github.com/pushlytic/pushlytic-ios-sdk Android SDK: https://github.com/pushlytic/pushlytic-android-sdk Would love feedback from the HN community! No waitlist or demo required - feel free to grab an API key and try it out.

Visit product View on Hacker NewsFeb 18, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · 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: apps, using, code · Missing: mac, agents, macos

82%82% 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

61%61% 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: mobile apps, ios, apps · Missing: personal, entrepreneurs, video

48%48% 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

43%43% 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

16%16% 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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