Me

Meetter – Reduce time wasted by meetings

Hacker News

Meetter – Reduce time wasted by meetings

Hey, HN! This post is not about useless meeting tips, nor about some new tool that drains remaining calendar hours. Like most of us, I’m very frustrated by the amount of time wasted by meetings. Why most of us here hate meetings is well described in PG’s essay: http://www.paulgraham.com/makersschedule.html There is also a great solution that worked for YC: office hours. Ok, but how can this help a regular maker in a regular company? Most makers struggle with the meetings scheduled by their colleagues, so let’s focus just on the company internal meetings. What if every maker will have her/his own office hours few times a week at different times? Yeah, that probably won’t work. Makers & managers often need to meet in 2+ groups... OK, what if all makers within the company will have office hours at the same time? Would be nice, but that won’t scale as it will require too many conference rooms and switching rooms fast enough may not be possible if someone has six 10min meetings with different folks… Oh, wait, but what about video meetings? There is no room limit there and switching rooms takes seconds. Many of us work in distributed companies with a lot of meetings online already. Great, but there is still a problem with agreeing on times and booking small meetings within those hours. Also, what to do if there is urgent discussion and everything is overbooked ahead of time with non-urgent managerial stuff? OK, so that is what we are trying to solve with https://www.Meetter.ai and would love to hear what you think about the early version. Please check How Meetter Works section on the landing page for more details. Here is an open demo account just for HN: https://hn.meetter.ai/signup-demo After sign-up, just try to post few agenda topics with random people and see if you can join office hours scheduled Tue/Wed/Thu at 8 AM PT.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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.

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