Scheduling with a Late Fee

Hacker News

Hey everyone, I wanted to share something I've been working on: BuddyOk. At it's core it's a event scheduling app. What makes BuddyOk different is that it introduces a little skin in the game to keep everyone on their toes. We're talking a small (or big, you decide) monetary stake to keep things on schedule, because let's face it, we all know someone who could use that extra nudge to show up on time. You set the late fees how you see fit: a base charge plus any additional fees you want to pile on for every few more minutes someone is running behind. And if you're organizing, you can choose whether those fees apply just to guests or to you as well. To add a feel-good angle, a slice of those tardy penalties goes straight to charity. A small way to turn those "Oops, I'm late again" moments into something positive for the community. That's BuddyOk — schedule events with a monetary stake to encourage (or gently prod) everyone to be on time. Whether it's gym buddies, or home service providers, contractors ensuring vendors show up on time, friends who find humor and motivation in friendly stakes. The list goes on. You feedback is encouraged and welcome! More features to come assuming interest. https://buddyok.app Thanks for reading :) PS. I guess this is my spin on Pieter Levels' https://gofuckingdoit.com/ Full disclaimer: The percentage that goes to charity and which charity is still undecided. Your feedback here would be greatly appreciated but bear in mind, I have to eat lol. Also I've been kind disoriented off by this whole anti-effective-altruism, pro-e/acc thing that happened as I was finishing off this app.

Visit product View on Hacker NewsDec 10, 2023

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best fitHighest predicted score across all platforms for this description.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

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AppSumoMay struggle as an AppSumo deal · Strong signals: plus, friendly · Missing: platform, intuitive, reviews

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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 released

34%34% 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, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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