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Basedash MCP write

Product Hunt

Build charts and dashboards from Cursor and Claude

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Actual performance

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Traction signals

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, mcp · Missing: mac, agents, macos
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListSuited for BetaList early-adopters · Missing: web3, just like, small businesses
51%51% predicted probability of success on BetaList, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, just released, open source
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: api, 000, profitable
24%24% 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
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: reviews, intuitive, videos
14%14% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, created, podcasting
8%8% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Correct prediction on native model

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Send any website components to Cursor or Claude via MCP

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