Plan your next event by asking when not

Hacker News

Hello fellow hackers! I love creating products that solve people's needs. One day, my wife asked me if I knew of any tools that could help plan a week-long company getaway. The requirements were fairly straightforward: - 14 people - A 7-day trip - Sometime in the next 6 months They weren’t concerned about choosing the exact time yet; they just needed to know which dates would work for everyone—or put another way, when does it NOT work? I searched but couldn’t find anything that met this specific need, so I decided to build it myself. The concept is simple: think of it as an inverse Doodle. You select a date range for the potential event and then ask your friends, family, or coworkers to mark the dates they are unavailable. This way, you can easily identify the best possible time slot. This approach allows flexibility in planning. For instance, if there’s only a 5-day stretch available, that could work too.

Visit product View on Hacker NewsApr 16, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: wife · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

49%49% 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: new · Missing: mac, agents, macos

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

43%43% predicted probability of success on AppSumo, 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

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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