Ox

Oxynote – Technical knowledge base with live Prometheus charts

Hacker News

Oxynote – Technical knowledge base with live Prometheus charts

Hey HN, I wanted to share a project my friend Dovydas and I (Simon) have been working on for a while. It’s a knowledge base platform built specifically for developers and technical teams. It has a similar feel to Notion and supports live collaboration, but it also has technical "power blocks" for API specs, runbooks, RFCs, as well as live metrics from external sources like Prometheus. On top of that, document changes can be reviewed like GitHub PRs, while still preserving rich text blocks and chart displays. The simplest way to describe it is: what if Notion, Grafana, and the PR review parts of GitHub were merged into one platform? We started working on this because we realised Grafana is good at showing you that something is happening, but it’s not great at telling you why you should care, what "normal" looks like, or which runbook / decision / incident it relates to (it has description fields, but they’re fairly limiting). If you didn’t build the dashboard, you usually end up asking the person who did. Also: dashboards tend to get changed quietly, and the explanations (if they exist) usually don’t. At the same time, Notion/Confluence work well for text, but they’re awkward for technical artefacts (runbooks, RFCs, postmortems, API specs), and the review features feel superficial. That’s why a lot of people, ourselves included, fall back to git-based docs. Git-based docs are closer to how engineers work, but then you’re running a docs "product" (Docusaurus / GitBook / etc.) with pipelines and deployments, auth layers, and live collaboration on long-form writing that’s rarely pleasant. And, of course, observability still sits somewhere else. So we built Oxynote. It’s a technical knowledge base for dev + infra teams, with a bias towards simplicity (we took a lot of inspiration from Linear and the idea that you shouldn’t need a training session to use a tool). It keeps runbooks, procedures, API design docs, RFCs, and postmortems together with a proper review flow, and lets you put relevant metrics charts right next to the text. It also has a feature we call "freshness hooks", which lets you tie docs to related GitHub repos / Docker images / external websites, so you get a nudge when something there changes. This is still an early-stage platform and we’ve got a lot we want to add. For example, live metrics charts currently support only Prometheus, but we’re planning to add SQL databases and other data sources next. It’s completely free and there are no ads. We’d really appreciate suggestions, experiences (positive and negative), and ideas. Thanks!

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Product HuntOn track for Day 1 leaderboard · Strong signals: dock · Missing: mac, agents, macos
96%96% 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: supports, started · Missing: reddit linkedin, podcasting, created
87%87% 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, ide, pipe · Missing: https docs, excited, just released
73%73% 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: way · Missing: mobile apps, ios, personal
37%37% 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
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
15%15% 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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