Flasho (YC W20) -Open source tool to set up transactional notifications

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Hi HN, We just launched Flasho- an open-source tool to set up transactional notifications in minutes. Last year when we were building an avatar-based social app, we realised that building notifications took up significant time and development effort (establishing microservices, integrating with a bunch of different service providers etc). And every time we wanted to set up new notifications or make changes to existing ones, we had to make multiple changes across the codebase which was time-consuming and messy. We realised we needed a better way to manage this. Existing solutions were either too expensive or required us to share customer data with third parties. And that is when we decided to build an in-house tool. This tool turned out to be incredibly useful for us. It was a single dashboard to create and manage all our customer notifications across channels. It saved us weeks of developer bandwidth and we could now create and manage notifications with little to no code. We decided to open source this tool and thus Flasho was born. Flasho is self-hosted(docker/deploy from source). You can just connect your DB, set up event triggers and conditions, create content and connect to the service providers of your choice in a matter of minutes, without writing any code. We have SMS and email notifications now. We plan on adding in-app and push soon. We have currently built it for PostgreSQL. We will make it compatible with more databases in the coming days. We’ve launched it under the MIT licence so any developer can use the tool. We don't intend on charging individual developers. The plan is to generate revenue through a cloud hosted service, priority support, features like multi-user login etc. Check it out and let us know what you think!

Visit product View on Hacker NewsFeb 21, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, dock, new · Missing: mac, agents, macos

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released

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.

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

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

AppSumoMay struggle as an AppSumo deal · Strong signals: host, soon · 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: revenue · Missing: arr, mrr, profit

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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