I use what I know as the the "Bill Gates method". Separate things into:
* important points
* questions to ask, either during the meeting or afterwards. If it gets answered during the meeting, I put the answer under 'important points' and cross out the question.
* things for you to do after the meeting
* any commitments others take on which you want to keep track of
I do this by entering them on different areas of the page, others use different symbols at the beggining of the line (?, !, *, @).
As soon as I finish the meeting, I share the notes with everybody.
* Have a tangentially related person attend and take notes so the main participants can concentrate. Instead of jotting down your own questions, speak them aloud for everyone including the scribe to hear.
If you don't have a dedicated assistant, set up a rotation with your team to scribe for each other.
Only jot down a few brief personal notes for yourself.
You had me here. Companies that do this as a matter of course on their key production lines usually become very, very productive.
Its not to catch worker ethics lapses; its to give the team the ability to understand the causes of their exhaustion, and optimise.
Anyway, done properly, meeting notes and general distribution can give individual members of a group a lot more agency over their participation in that group.
> ML multi-speaker speech-to-text every conversation
I honestly love this idea. Any suggestions on what FOSS tooling to use for said speech-to-text that's reasonably accurate? Or is training the ML the "heavy lift" of this setup?
Google has an API for this. Speech is a thing you really need big data for and thus IMO not suitable for FOSS. Why bother with setting up the whole custom data pipeline when plug-and-play is available for a fraction of the cost.
> ML multi-speaker speech-to-text every conversation
Neat idea, do you know of any software that's capable of taking an audio file and producing multi-user text from it? Seems like it would be useful in a wide variety of situations.
Trint[0] has a wonderful UI/UX for exactly this. I'm not sure if they're using the latest & greatest ML models, but even years ago I was pretty blown away. I still am. It's one of the most "right" products I've seen in this generation of web development. Someone realllllllly cared about the details of UX.
Some of that only comes across when you actually use it - when you clean up the transcription immediately after the meeting or the next day. Clicking a mistake word to edit it snaps the video and audio to that point, so its super intuitive to "scrub" through the video just by clicking around the text transcription. Very fast, very natural, very low effort.
I can only imagine how much it will be improved if it used google's newest multi-speaker transcription models. It always had some trouble whenever people started talking at the same time.
Yeah it's way too expensive. I decided that it probably technically is net positive for organizations with a heavy billable hours situation (contract engineering shops) because it can easily save more than $60/mo per person of time/accuracy.
That doesn't mean I was willing to pay $60/mo/pp though, and ended up not.
However, I still think it's the best product in its category right now and given the disappointing state of product design these days, I don't expect anyone to catch up to its interface. I'd love if an open-source group did though.
Google Speech API see other comment for link has speaker diarization in beta i.e automatic predictions about which of the speakers in a conversation spoke.
On top of this you can add 5000 names for company specific entity recognition for product, people, brand names etc.
I use the bullet journal system to take meeting notes. It's more personal as the things I track are important to me. I try to process information and jot down things I need to remind, my tasks, and add questions to follow up on.
