Keep – Create production alerts from plain English

Hacker News

Hi Hacker News! Shahar and Tal from Keep Here. We were tired of creating alerts for our applications, so we've built an open-source GitHub Bot that lets you write application alerts using plain English. The code is open-sourced: https://github.com/keephq/keep so you can review it yourself. Every developer and DevOps professional is familiar with the fact that in order to ensure your application works in production, you need to access your observability tool's user interface (such as Grafana, Datadog, New Relic, etc.) and carefully determine how to create alerts that effectively monitor your application. Instead, by installing Keep, every time you open a PR, the bot combines the alert description (alerts under the .keep directory) with the tool context (mostly the configuration of the alerts you already have) to generate (GPT) new alerts that keep you monitored. So, for example, if you create a .keep/db-timeout.yaml and open a PR, the bot will comment on the PR with the actual alert you can deploy to your tool. # The alert text in plain English alert: | Alert when the connections to the database are slower than 5 seconds for more than 5 minutes provider: grafana You can Install the bot and connect your providers via https://platform.keephq.dev (after login, you'll start the installation flow) or just clone the repository and use docker-compose to start the web app and the installation flow. Demo Video - https://www.loom.com/share/23541a03944c4dca99b0504a1753d1b4

Visit product View on Hacker NewsJun 7, 2023

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

68%68% 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.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, 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.

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53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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17%17% 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.

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