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OneUptime - An Open Source Observability Platform

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OneUptime - An Open Source Observability Platform

Hello HN! We’re excited to introduce OneUptime, an open source observability platform. Unlike other companies, our platform is MIT licensed and is 100% open source (and always will be). We’re not following the open-core model, but instead, we’re committed to keeping everything open. OneUptime is a combination of Incident.io, Pingdom, PagerDuty, and StatusPage.io all in one platform. We’re also planning to add Logs Management and APM to make it a viable alternative to Datadog in the future. We’ve been in the market for a few months and already have some big enterprises paying us, including the likes of Securonix, Dotdash Meredith, and many more. We’re a super tiny team of 3 people and are looking for a very senior engineer to join our team, ideally someone who understands OpenTelemetry. We’re 100% bootstrapped and proud of it. We’d love to hear your early feedback. Please check us out on GitHub here: https://github.com/oneuptime/oneuptime Looking forward to your thoughts and comments!

Visit productView on Hacker NewsNov 10, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, tiny, open · Missing: mac, agents, macos
89%89% 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: including · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, ide · 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.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: bootstrapped · Missing: arr, mrr, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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