Ti

TipTour, A Tooltip that guides you

Hacker News

TipTour, A Tooltip that guides you

Hi HN! I built TipTour because I'm frustrated with React Tour and similar libraries that force users through rigid, linear onboarding flows. Users should be able to discover your product naturally, not be railroaded through predetermined steps. What TipTour does differently: - Always-on, ambient help - The tooltip follows your cursor smoothly, providing context without blocking anything - Non-intrusive - No overlays, no "Next" buttons, no forced flows. Users stay in control - Discovery-friendly - Add contextual hints that appear as users naturally explore your interface

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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: cursor, user, context · Missing: mac, agents, macos
88%88% 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
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 · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly, interface, users · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · 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
16%16% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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