I Built a Tool to Experience Your Software Like a User

Hacker News

Hi Hacker News! I’m excited to share a project I've been working on: a browser extension that lets you experience your software exactly as your users do. Key Features: - Records browser session, along with mouse events and resize events to give you an actual view into how users experience your software. - Users can annotate videos with actionable feedback. - Speed up, slow down, and add pan and zoom animations to demos (This is more for product people) Why I Built It: There's a huge gap between how developers see their software and how their users do. You'll notice that the first time you see someone using your web app at a resolution you didn't anticipate or clicking on things they thought were buttons. This is a tool to collect feedback from coworkers, stakeholders, and power-users to record new features and user flows. Try It Out: You can check out the extension itself here: https://chromewebstore.google.com/detail/oadbjpccljkplmhnjek... I’d love to hear your feedback and thoughts on how this tool can be improved or further developed. Thanks for taking a look Hacker News!

Visit product View on Hacker NewsMay 22, 2024

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

91%91% 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

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, hacker news, ide · Missing: https docs, just released, exist

44%44% 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: video, google, users · Missing: mobile apps, ios, personal

33%33% 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

10%10% 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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