VacayBuddy – PTO Management Inside Slack (Open Source)

Hacker News

I built VacayBuddy, an open-source Slack bot that makes managing Paid Time Off (PTO) simple and seamless — all from within Slack. Features: • Request and track PTO directly from the Slack app home • Get notified and approve PTO via Slack modals • Admin dashboard to manage team PTO balances and history • Excel integration for migrating existing PTO data • Works out of the box with SQLite for local setup We use it internally to manage our team’s PTO without emails or spreadsheets. Would love your feedback or contributions!

Visit product View on Hacker NewsApr 26, 2025

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Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, email, open · Missing: mac, agents, macos

92%92% 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 · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% 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

24%24% 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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