Knock – Notification Infrastructure for Developers

Hacker News

Hey HN! I’m Chris – one of the co-founders at Knock (https://knock.app). Knock is notifications infrastructure for developers. We provide a set of flexible API primitives you can use to power complex notification systems, without having to roll your own notifications infrastructure in-house. In short: we built notifications so you don’t have to. We learned about scaling notifications systems the hard way. Previously, my co-founder and I worked together at Frame.io, where we saw the upside of product notifications as well as the pain involved in building and maintaining them. With Knock, we’re building the product we wish we had then. Knock gives you: - Faster time to market. Our customers have used Knock to ship real-time in-app feeds and 3rd-party chat integrations (Slack, Discord, Teams) in under an hour. - A flexible notification engine. Power use cases such as batched notifications, conditional sends based on read or device status, and per-user preferences. - A toolkit built for product notifications. Knock comes with isolated environments, versioning, and complete observability from API request, workflow execution, to notifications sent. We’re excited to share Knock with you today and to get your feedback on what we’ve been building. The best place to start is in our docs (https://docs.knock.app/). You can learn more about what we’re up to in this Twitter thread: https://twitter.com/samseely/status/1504462708095262724?s=20&t=nIv6QpdMJ-scflu8i1Z2gA.

Visit product View on Hacker NewsMar 17, 2022

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

83%83% 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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

40%40% 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

26%26% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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