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Caleebree – Picking Calendar Slots
Hacker NewsCaleebree – Picking Calendar Slots
This is a side project created because I was a bit tired of back-and-forth emailing to set up a call with someone. It turns out not everyone, like myself, likes to send permanent links to their calendars to let invitees pick a slot. Thus, often, it is an email with a few options written in text. We made composing these emails easier with temporary links to each slot, which was automatically or manually pre-selected. Not a big deal, but our product eased my routine. Would appreciate any thoughts.
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2points
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: email · Missing: mac, agents, macos75%75% 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: created · Missing: supports, reddit linkedin, podcasting57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released44%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, revenue19%19% 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, crypto1%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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