In the past couple of months, 50,000,000 people read my Tweets.

That's more than the population of Canada.

My Secret?

I use the same 7 templates every time (Grab them for free) ↓

📑 Listicles

If you're starting out this one is perfect for you.

Curate lists of:

- Tools to use.
- Books to read.
- Creators to follow.
- Podcasts to listen to.

Add a question & you got a banger.

Grab this template ↓
X life-changing [books, podcasts, courses,etc..)

1. ______________
2. _____________
3. ____________
4. ___________
5. __________
6. _________
7. ________

→ What did I miss?/What else?
🔖 How to do X

This one will get you authority and fans.

Break down something you did in steps.

List them down & encourage people to go for it.

Grab the template ↓
An easy X-step process to [desired outcome]:

1. ______________
2. _____________
3. ____________
4. ___________
5. __________

6. Thank me later
🆚 A vs B

We hate to admit it.

But humans love to compare things.

Why? Your brain LOVES contrast.

Steal A vs B Template ↓
How to achieve [desired outcome]

Old/bad/slow/wrong [way or action]

❌ Bad way#1
❌ Bad way #2
❌ Bad way #3

New/good/fast/right [way or action]

✅ Good way #1
✅ Good way #2
✅ Good way #3
❓ Asking questions

The Twitter algorithm loves replies.

The best way to get replies is = Ask questions.

Steal these examples & template ↓
How did you make [X} thing?
How did you make your first $1 online?

What is your favorite [X]?
Who is your favorite creator on Twitter?

In ONE WORD, what stops people from doing [X]
In ONE WORD, what stops people from building an online audience?
🛠️ Build in public

People get excited when you share what you're building.

Take it a step further by asking for advice.

Here is a template you can use ↓
[X] is an essential [asset/tool/step].

I'm planning to do [the thing you want to build].

What would you do if you were me?
🌶️ Hot Takes

This one is Twitter's secret weapon.

If you use it right, you can easily go viral.

if you want this one to work: Be BOLD.

Grab the Template ↓
[Popular Opinion] is bullsh*t.

In reality [take a stand against it].

Example:

Having high engagement on your Tweets is bullshit.
In reality, likes ain't cash my friend.
✨ Lessons learned

The comobination of:

Doing something impressive.
+
Sharing your lessons learned.

Gives you an edge over everyone else.

Use this Template to Ace it ↓
I've achieved [impressive thing] in just X [days, weeks, months]

Here are X Lessons I learned:

1. Lesson #1
2. Lesson #2
3. Lesson #3
4. Lesson #4
5.Lesson #5
TLDR; 7 Tweet templates to get you millions of impressions:

🆚 A vs B
📑 Listicles
🌶️ Hot Takes
🔖 How to do X
🛠️ Build in public
❓ Asking questions
✨ Lessons learned
If you enjoyed this thread:

• Follow me @albadawee on my journey from 0 → $1M.

• Like/Comment/ RT to help your friends up their content game. https://t.co/A3b40ZXd34

More from All

ChatGPT is a phenomenal AI Tool.

But don't limit yourself to just ChatGPT.

Here're 8 AI-powered tools you should try in 2023:

1. KaiberAI

@KaiberAI helps you generate beautiful videos in minutes.

Transform your ideas into the visual stories of your dreams with this Amazing Tool.

New features:
1. Upload your custom music
2. Prompt Templates
3. Camera Movements:

Check here

https://t.co/ivnDRf628L


2. @tldview TLDV

Best ChatGPT Alternative for meetings.

Make your meetings 10X more productive with this amazing tool.

Try it now:

https://t.co/vOy3sS4QfJ


3. ComposeAI

Use ComposeAI for generating any text using AI.

It’s will help you write better content in seconds.

Try it here:

https://t.co/ksj5aop5ZI


4. Browser AI

Use this AI tool to extract and monitor data from any website.

Train a robot in 2 minutes to do your work.

No coding required.

https://t.co/nNiawtUMyO
How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)

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