Highlight – Debug customer-facing issues with replay

Hacker News

Hey HN, I’m Jay, a co-founder at Highlight and a YC alum; excited to share our replay-based debugging tool! TLDR; if you're using a tool like Fullstory for debugging, or you have trouble understanding the actual impact of your errors (e.g. in sentry), you should give us a try! We built Highlight because we constantly found ourselves flooded with errors and support tickets with very little context on why these issues occurred. To make matters worse, there was never an easy (and less annoying way) of reproducing these errors, other than by asking users to send screenshots, videos, etc. Frankly, this wasn’t a good use of anyone’s time! At the same time, the more product-oriented folks on our team wished that they could better understand UX flows and user frustrations without having to do unnecessary user interviews. Highlight addresses both of these pain points by giving teams reproducibility out of the box! Our platform is installable with a single Javascript snippet and makes it easy and quick to search for relevant user sessions. For product folks, it’s easy to filter down to sessions where users performed specific actions, such as using a newly released feature. For developers, when an error or support ticket shows up in their inbox, they know exactly where to start debugging Today, we’re focusing on session replay and monitoring, but our goal is to expand to an entire suite of debugging tooling focused on helping teams find and fix issues faster. Some features on our roadmap include live session viewing, backend error monitoring, and much more!

Visit product View on Hacker NewsAug 4, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, inbox · 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

84%84% 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, ide, io · Missing: https docs, just released, exist

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

51%51% 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, way · Missing: mobile apps, ios, personal

43%43% 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.

Correct prediction on native model

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