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Typefully
@typefully
We're launching a Command Bar today with great commands and features.
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@aaditsh
I think this thread hook could be improved.
@frankdilo
On it 🔥
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Easlo
@heyeaslo
Reply with "Notion" to get early access to my new template.
Jaga
@kandros5591
Notion 🙏
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Marc Köhlbrugge@marckohlbrugge
Tweeting more with @typefully these days.
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Jurre Houtkamp@jurrehoutkamp
Typefully is fantastic and way too cheap for what you get.
We’ve tried many alternatives at @framer but nothing beats it. If you’re still tweeting from Twitter you’re wasting time.
DHH@dhh
This is my new go-to writing environment for Twitter threads.
They've built something wonderfully simple and distraction free with Typefully 😍
Santiago@svpino
For 24 months, I tried almost a dozen Twitter scheduling tools.
Then I found @typefully, and I've been using it for seven months straight.
When it comes down to the experience of scheduling and long-form content writing, Typefully is in a league of its own.
Luca Rossi ꩜@lucaronin
After trying literally all the major Twitter scheduling tools, I settled with @typefully.
Killer feature to me is the native image editor — unique and super useful 🙏
Visual Theory@visualtheory_
Really impressed by the way @typefully has simplified my Twitter writing + scheduling/publishing experience.
Beautiful user experience.
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@aaditsh
I think this thread hook could be improved.
@frankdilo
On it 🔥
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I was curious about this paper:
arxiv.org/pdf/2202.05826.pdf
It explains how creating an algorithm able to solve complex tasks while being trained on simple ones only.
The idea is to use multiple time the same block until it finds the solution.
The more complex the problem, the more you iterate before finding the solution.
For the maze, it generates intermediate solutions like this.
This way, a fully convolutional network can learn on small labyrinths, and it became intuitive why it scales if you run it iteratively.
Not that impressive after all.
Stable Diffusion is also based on training a network to solve a small part of a problem, and it's by running it through several steps that you can generate an interesting issue.
It feels that this way of training network is more profound and is underused.