We

We’re open-sourcing our session replay tool

Hacker News

We’re open-sourcing our session replay tool

Hey HN! We’re open-sourcing highlight.io ( https://github.com/highlight/highlight ), a session replay and error monitoring tool. Highlight.io gives you a high-precision video-like replay of what users are doing when an error or exception occurs in your web app, along with a full-fledged error monitoring experience (similar to bugsnag, rollbar, etc..). The main value prop of highlight.io is that we help you understand the full context surrounding an error and allow you to drill down to the code path that a user invoked (i.e user clicked button X, sent network request Y, and backend code Z was executed). Some of our customers compare this to a “web debugger” of sorts. A picture of what this looks like in our app is here [1]. For some background, when we worked at our previous companies as engineers, we encountered hard-to-reproduce issues spanning across both the frontend and backend. The main issues were (1) if a customer complained about a problem, it was hard to reproduce the issue without asking for a screen-share or jumping on a video call; and (2) when viewing errors caught by tools like BugSnag or Rollbar, understanding the triggered code path required stitching together logs, errors, and trace; all from different sources. Highlight.io is completely open source and written in Go and Typescript. To build the replay capability, we use an open source project called rrweb [2] and have worked closely with their team to add support for features like canvas recording, shadow dom recording, and more [3]. Beyond that, we use the OpenTelemetry spec for our SDKs [4], which has made it pretty straight forward to support several languages, even with our small 4-person engineering team! Our product is completely self-serve at app.highlight.io. Installing it is as easy as a npm/yarn import and installing the backend sdk of your choosing. In addition, given the privacy-centric nature of session replay, we also offer the option to self-host [5]. Highlight.io currently makes money off of our hosted offering, and our self-hosted deployment is completely free. We’re also toying with the idea of an “enterprise” self-hosted deployment, similar to gitlab’s billing model, and thoughts from the community on this front would be appreciated! And as far as what’s next for us: Our customers are asking to render logs and traces on a highlight.io session (and vice versa), and we’re excited to be going deeper into a developer’s debugging stack. The long term goal is to build a platform that connects replay, errors, logs and more so that engineers can “playback” the full state of a web application. Overall, we’re quite new to the open source scene and would love the HN community to share their feedback on what we’re building. If anyone has opinions on where we’re going, or what they’d like to see in an open source monitoring product, we’re all ears. Check us out at highlight.io and at github.com/highlight/highlight to give us a shot. [1]: https://www.highlight.io/docs/getting-started/frontend-backe... [2]: https://github.com/rrweb-io/rrweb [3]: https://highlight.io/docs/general/product-features/session-r... [4]: https://opentelemetry.io/docs [5]: https://www.highlight.io/docs/general/company/open-source/se...

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
97%97% 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: excited, open source, ide · Missing: https docs, just released, exist
80%80% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, users · Missing: plus, intuitive, reviews
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, users · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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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