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Meeting Waste Calculator – See the Real Cost of Your Meetings

Hacker News

Meeting Waste Calculator – See the Real Cost of Your Meetings

I’ve been building Git Digest, a tool that helps founders and team leads stay informed about codebase activity without constant meetings. While exploring ways to boost productivity, I realized many teams regularly underestimate the actual cost of meetings. To illustrate this, I built a straightforward calculator to show precisely how much time and money teams spend on meetings. Just input your team size, meeting frequency, and duration, and instantly see the cost. It’s designed to highlight the impact of inefficient meetings in a practical, easy-to-use way. Try it out here: https://gitdigest.ai/meeting-waste

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Product HuntOn track for Day 1 leaderboard · Strong signals: activity, code · Missing: mac, agents, macos
67%67% 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
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
42%42% 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
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: 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
27%27% 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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