Get fresh prompts and ideas whenever you write - with examples of popular tweets.
The best writing experience, with powerful scheduling
Create your content without distractions - where it's easier to write and organize threads.
Cross-post to LinkedIn
Automatically add LinkedIn versions to your posts.
Discover what works with powerful analytics
Easily track your engagement analytics to improve your content and grow faster.
Love it 🔥
Collaborate on drafts and leave comments
Write with your teammates and get feedback with comments.
Rewrite as thread start
Make it Punchier
Improve your content with AI suggestions and rewrites
Get suggestions, tweet ideas, and rewrites powered by AI.
And much more:
Auto-Split Text in Tweets
Connect Multiple Accounts
Top tweeters love Typefully
100,000+ creators and teams chose Typefully to curate their Twitter presence. Join them.
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.
I forgot about Twitter for 10 years. Now I'm remembering why I liked it in the first place.
Huge part of my new love for it: @typefully. It makes writing threads easy and oddly fun.
This is my new go-to writing environment for Twitter threads.
They've built something wonderfully simple and distraction free with Typefully.
Such a huge fan of what @typefully has brought to the writing + publishing experience for Twitter.
Easy, elegant and almost effortless threads - and a beautiful UX that feels intuitive for the task - and inviting to use.
Luca Rossi ꩜@lucaronin
After trying literally all the major Twitter scheduling tools, I settled with @typefully.
Kudos to @frankdilo and @linuz90 for building such a delightful experience.
Killer feature to me is the native image editor — unique and super useful 🙏
Queue your content in seconds
Write, schedule and boost your tweets - with no need for extra apps.
Schedule with one click
Queue your tweet with a single click - or pick a time manually.
Pick the perfect time
Time each tweet to perfection with Typefully's performance analytics.
Boost your content
Retweet and plug your tweets for automated engagement.
Start creating a content queue.
Tweet with daily inspiration
Break through writer's block with great ideas and suggestions.
Start with a fresh idea
Enjoy daily prompts and ideas to inspire your writing.
Check examples out
Get inspiration from tweets that used these prompts and ideas.
Flick through topics
Or skim through curated collections of trending tweets for each topic.
Check the analytics that matter
Build your audience with insights that make sense.
Write, edit, and track tweets together
Write and publish with your teammates and friends.
Share your drafts
Brainstorm and bounce ideas with your teammates.
Love it 🔥
Get feedback from coworkers before you hit publish.
Read, Write, Publish
Control user access
Decide who can view, edit, or publish your drafts.
I'm sure you've used data augmentation before.
Most people think of data augmentation as a technique to improve their model during training.
This is true, but they are missing something.
Here's a brilliant approach to improve the predictions of your model:
Let's start with a quick definition of data augmentation:
Starting from an initial dataset, you can generate synthetic copies of each sample that will make a model resilient to variations in the data.
This is awesome. But there's more we can do with it.
Imagine you are building a multi-class classification model.
When testing it, you do this:
• Take every sample
• Run it through the model
• Determine the correct class from the result
You have one opportunity to make the correct prediction.
But you can do better:
You can take advantage of data augmentation to give you a better opportunity to make the correct prediction.
Introducing "Test-time augmentation."
You can augment samples before running them through the model, then average the prediction results.
Imagine you are ready to make a prediction.
Instead of running that sample through the model, you generate three versions of it by changing the image's contrast, rotating it slightly, and cropping it a bit.
You now have four different samples.
Run all four images through the model, average the softmax vectors you get back, and determine the final class from the result.
By augmenting the original image, you give the model more opportunities to "see" something different and compute the correct prediction.
The success of Test-time augmentation depends on how good are your augmented samples.
That's where most of your time will go.
Every augmented image will have a lot of influence on the final result. You don't want sloppy variations!
I've found a lot of success using a few slight modifications to the initial picture.
"Less is more" in this process.
The best part: It's a relatively easy way to improve your predictions.
Every week, I break down machine learning concepts to give you ideas on applying them in real-life situations.
Follow me @svpino to ensure you don't miss what's coming next.