Flowrite: Turn short lists of facts into well-written emails
flowrite.com
flowrite.com
A bit premature for HN?
This could be extended to support output for other mediums, such as Blog posts or social media updates.
It does drive me slightly crazy that this system JUST INJECTS NOISE. You provide it with concise, readable information. It dilutes that information with additional words and scaffolds it over complex sentence structure. The resulting email has a flourish, but takes more effort to read. It's an inverse document summarizer!
Perhaps there are benefits to this. Sometimes you want to make your reader invest a bit of effort in your message. I believe that can improve information retention. The email is also a generally-accepted human API. People might be more receptive to information presented as a letter than as a list.
The biggest benefit is probably on the author's side. This forces you to actually think about what you want to say. The input is basically an outline!
Paradoxically, I think Flowrite could improve communication by making authors and readers do MORE cognitive work rather than less.
It expands "follow up" to "in person or via Skype / phone call" -- where did those platform preferences come from?
when asked "How about a Zoom call on the following Tuesday at 2PM?" it replies "I'll be in a Zoom meeting on Tuesday at 2PM, so let me know if that works for you." Does this mean Flowrite looked at a calendar and detected a conflicting zoom meeting? Or is it trying to accept the proposed meeting time & venue?
I do really like the Blog Outline example, where it expands a general question ("How to align your Marketing, Sales and Customer Success teams to maximize revenue?") to a list of six other questions to dig into the problem more. That might be a powerful way to push people to think more deeply and question assumptions.
GPT-3. Which I think is reasonable - not a lot of work to read it through and replace it with "on Zoom, or next time you're in the bay area".
Good point on the calendar though.
> It does drive me slightly crazy that this system JUST INJECTS NOISE.
is actually an important service. What is noise to the sender is generally not noise to the reader. It would be awesome if at some point in the next few decades we only have to provide information that we need to think about and the computer could find a way to compile it into what the reader is looking for. In other words, if the computer could do what most every secretary was doing in the 1960s.
Not sure whether there’s much of a difference between all these GPT-3 powered services when all that distinguishes you from competition is some (slick) UI and the 500-1000 extra words of “training” you give to GPT-3.
Now, what's the prompt that will get GPT-3 to generate good prompts for us?
We'll call this technique Promptception.
I would love it if I could get recipients to actually devote cognitive work to my communications. Unfortunately, increasing cognitive load is a great way to ensure fewer people read what you write.
Every recipient of a message thinks differently than the writer. For communication to be effective, it must be designed for the recipients, to trigger the right sequence of ideas that will get the message across. I’ve written up the exact same content in 3 or more ways to target specific teams - bullet lists, email, slide deck, etc. It’s always worth my time. Before I did this, I would get several elaborate responses of “why didn’t you write this in X format”, arguing that X was a superior format. But everyone provided different versions of X. You may think conciseness is paramount; not only is it not, the definition of what is concise varies from person to person.
So the first thing I would do with the message would be to put it into a model to detect artificial text, before deciding if I want to see it.
The main character is a friend of the inventor, who can't seem to make any sales. Later, they run into each other again, and the inventor is HUGELY rich.
Turns out he realized that the original idea was a flop, reversed the process, and sold it to law firms.
https://hanselminutes.com/763/tldr-extreme-summarization-as-...
More leads: https://paperswithcode.com/task/text-summarization
It's conjuring, for example "after weeks of hard work" from thin air. There's nothing in the input to suggest that phrase makes contextual sense. Though perhaps the example isn't a real world one.
I think this is not that dissimilar from a human writer. Recruiter email's come to mind as an example of this as well.
Maybe I'm missing the intended use case for this tool?
So now they're resorting to an AI to try to replace non-technical skills because they've utterly failed at identifying value in human beings.
How does it know it took "weeks" of work to do something?
How does it know to schedule a video call instead of, I dunno, an in-person meeting or a lunch date?
Does it learn if the human user corrects what it comes up with?
Somebody needs to rig this up like the old emacs "psychoanalyze-pinhead" hack and see whether two instances of this stuff can talk each other into some rhetorically wierd corner.
Does feedback fine tune or customise the model behind this?
Depending on the questions and how much the answers matter, it may be better to ask each one after they reply to the previous one, so they don't feel overwhelmed being asked all of it at once and put more thought into answering each one.