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Revisit – Session recording analysis that "watches" the video

Hacker News

Revisit – Session recording analysis that "watches" the video

Hello HN, I’m the creator of Revisit ( https://revisit.pro ), a session recording and analytics platform. We built this because existing tools (FullStory, Smartlook) force you to manually watch thousands of user sessions to find one bug. We wanted an AI that could watch the sessions for us and just tell us why users are churning. The "AI" Architecture - Store Events: We record DOM events, console logs, network requests, and assets using a lightweight script. - Replay & Render: On the backend, we spin up a headless browser (Puppeteer) to replay the session exactly as the user saw it. - Video Generation: We convert that replay into a physical video file. - Visual Analysis: We send that actual video file to Gemini This allows the AI to "see" the session like a human does. It can detect visual glitches, rage clicks, and confusing layouts that code-based analysis might miss. Project-Wide Chat - Beyond single-session analysis, we built a project-wide chat assistant. Contextual Answers: The AI retrieves the most relevant sessions, "watches" the key moments, and writes an answer backed by video evidence. Features - Deep Context: We capture everything—console logs, network payloads, and assets—so you can debug issues without asking users for screenshots. - Unlimited Seats: We don't charge per seat. Bring your whole team. - Privacy First: Built-in masking for sensitive data before it leaves the user's browser. - And much more. We are currently open for signups and would love feedback on the analysis quality! -Martin

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: user, context, visual · Missing: mac, agents, macos
95%95% 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: gemini · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, answers, users · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
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.
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 · Strong signals: chat, smart · Missing: web3, crypto, cryptocurrency
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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