Tabbing through bugs? One replay to catch them all

Hacker News

Hey HN, For anyone who uses session replay tools to debug, monitor, or analyze user journeys, this feature might come in handy. OpenReplay — the self-hosted session replay tool that helps developers troubleshoot web apps faster — now supports tabbed browsing. Here's how: 1. Capture and replay user sessions that span across multiple browser tabs, all within a single recording. 2. OpenReplay's tracker communicates across browser tabs, ensuring accurate tracking of each tab, even when duplicated or opened with `window.open` without `_blank`. 3. With "co-browsing", you can watch and support users in real-time and see how they navigate across multiple tabs of your app. Why it’s important? 1. This feature is convenient for developers as it allows them to easily identify bugs by reviewing a complete session in one replay, rather than having to reference multiple recordings. 2. It provides a deeper understanding of the user journey and interactions across multiple tabs in your web app, helping in the comprehension of complex user behaviors and paths. 3. It provides accurate feedback on user tab actions such as opening, switching, and closing tabs, assisting in UX/UI improvements. Interested? For more details, you can check out the GitHub repo at https://github.com/openreplay/openreplay.

Visit product View on Hacker NewsFeb 16, 2024

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

92%92% 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 · Missing: reddit linkedin, podcasting, created

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

55%55% 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: apps, users · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive

34%34% 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

12%12% 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.

Incorrect prediction on native model

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