StandupBot is an easy to use bot that automates your team’s standups, check-ins or any kind of recurring status update meetings, without breaking the bank. Trusted by thousands of teams to run over a million standups in our 8+ year history.
Unlike other tools that try to do way too things and are super confusing to manage, we focus on what you really need to automate your team’s meetings:
⚡️Fast setup: From install to first meeting in under 60 seconds. Great defaults to get you going and super easy to change to your needs.
👥 Multiple teams and projects: Create as many standups or status meetings you need for different projects or teams.
🕘 100% asynchronous: Everyone participates when it’s more convenient for them.
📃 Standup Report: Receive an easy-to-read report via email and Slack when the meeting is done.
👀 “Just following” mode: Select who's actively participating in meetings and who's only following through reports.
📆 Flexible scheduling: Schedule your meetings at the days and times you need. Automatically excuse people from meetings when they’re on vacation.
✅ Participation reports: Team- and individual-level participation reports, so you can easily see who needs some encouragement to share their updates more frequently.
🔔 Automatic reminders: We’ll be the friendly drill-sergeant for your team reminding everyone that hasn’t submitted their standup to do so before the meeting window closes.
No features have been listed yet.
Based on our record, Parse.ly Analytics seems to be more popular. It has been mentiond 3 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I've also seen parse.ly pop up a bit, I might try it to see if it's any decent. Source: over 1 year ago
Parse.ly | Python Data Engineers (NA) & Machine Learning Engineers (EU) | Remote | Full-Time | https://parse.ly Are you a Python programmer based in North or South America, interested in large-scale data processing (terabytes per month, petabytes in our archive), and making use of massively-parallel computing architectures, such as those behind Spark and Dask? Or, are you a Machine Learning Engineer in Western or... - Source: Hacker News / almost 3 years ago
Would be really useful (not to mention polite) if sources were cited when you do this. For example, I think the early points are from the parse.ly report. People might want to click through for context if you let them. Source: about 3 years ago
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