ImpasseBreaker – Double-blind negotiation

Hacker News

Hey folks -- love your feedback on our negotiation platform, www.impassebreaker.com. IB uses double-blind comparison of two users' "bottom line" financial positions to determine if they have a match and can then stop wasting time nickel and diming in endless negotiation. If there's a match, IB sends an email to both users reporting the amount of the match, currently the mid-point between the "ceiling" and the "floor" and if no it sends an e-mail saying no match. A couple of use cases: real estate sales, salary negotiation, lawsuit settlement . . . We have other features on the drawing boards -- payments, settlement document drafting, among them -- for now, we'd love your feedback and thoughts on the current site. Login and use are free and you can register with a Facebook or linkedIn account. Many thanks in advance for your thoughts and feedback --stephen@impassebreaker.com.

Visit product View on Hacker NewsMar 3, 2014

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best fitHighest predicted score across all platforms for this description.

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Hacker NewsMay not resonate with HN audience · Strong signals: io · 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.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, email · Missing: mac, agents, macos

40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

32%32% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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