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Nectar, automated meeting prep for modern teams

Hacker News

Nectar, automated meeting prep for modern teams

Hey HN, Shri and Robin here, cofounders of Nectar. Planning for productive meetings requires a lot more prepping time than you would first expect which is crucial for alignment and support for your teams. While working as ICs and later managers we faced multiple different challenges: 1. Remembering what happened throughout the last week in the first place, and reviewing notes, emails or endless Slack threads (as a reader) just to keep up. This gets compounded with the number of reports 2. Being distracted taking notes and keeping record during group meetings 3. Or using up valuable meeting time for listing out last minute talking points With Nectar we aim to cut meeting prep time down by 90%, and increase its effectiveness by considering all team activity in a given time frame. You get automated summaries of your entire online communication and agenda items for your next meeting. - We currently support data retrievals from Slack and Zoom, and plan to include more depending on our customers needs - We’re starting off with internal management as a use case, since that’s our bread and butter BUT are interested to hear about your use case - You can [schedule]( https://calendly.com/run-nectar/15min ) demos with us starting next week! Would love to get the community’s feedback.

Visit product View on Hacker NewsOct 24, 2023

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, activity · Missing: mac, agents, macos

84%84% 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: lua, ide, io · Missing: https docs, excited, just released

60%60% 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

41%41% 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

27%27% 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 · Missing: web3, chat, crypto

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

Incorrect prediction on native model

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