1. Ideate
2. Build
3. Promote
4. Deliver
5. Follow-Up
Let's break them down:
Giveaways are smashing the Twitter algo.
I've gained dozens of followers & newsletter subs.
Small accounts NEED to use them.
They drive growth, but also experience & trust.
Because a Twitter giveaway is a mini-product launch.
Steal my blueprint to create 1 in an afternoon🧵
1. Ideate
2. Build
3. Promote
4. Deliver
5. Follow-Up
Let's break them down:
Grab a piece of paper and write down 3 lists:
1. Concepts you've learned in the last WEEK
2. Problems you've solved in the last MONTH
3. Questions you've been asked the most in the last YEAR
You're Twitter giveaway is somewhere on these lists...
Combine the best from your 3 lists into 1 based on these criteria:
1. Quickly actionable
2. Easily explainable
3. Broadly relatable (beginner)
Once done, you're going to pick ONE that sings to you the most.
But don't throw out the rest. They'll be your next giveaways
Pick 1 the easiest medium to convey your idea:
• Loom video
• Checklist (Notion)
• Written guide (Google Doc)
Give yourself ONE HOUR to script & edit your giveaway.
You get an additional 15 minutes to record, if applicable ;)
Use this super simple framework to script your giveaway:
1. State the problem
2. State your solution
3. Break your solution down into 3-10 steps MAX
Here are some examples:
• 5 ways to manage your manager
• How to write your first newsletter
• 10 tips to ace your next interview
• Steal my 5-step sales call script
THINK:
🍼 BEGINNER
🌉 BROAD
💸 BUCKS
Here is your giveaway mini-marketing plan:
1. Tweet right before you start your 1-hour working session
2. Tweet when you're done, advising that it'll come out tomorrow
3. Schedule the giveaway
Write each tweet separately as it'll help you refine the marketing LIVE.
The most important tweet is the actual giveaway one. Use this checklist:
1. State the problem
2. State your result
3. Tell us it's easy
4. Tell us to act
The tweet should have a call to action for people to get the giveaway (reply, retweet, DM).
Make this clear.
When you're a beginner, just reply to every interested person manually.
Even 100 manual replies will only take you an hour.
When you've got some $ to invest, get Hypefury so this can be automated.
Use my link: https://t.co/YJvq1YHLAN
Here is my process:
1. Set-up autoDM in Hypefury
2. Link to Gumroad page in DM (email capture)
3. Link to Notion/Loom in Gumroad email
This is what will separate your giveaway from everyone else's.
Follow up with each recipient 1 week later to get their feedback.
This will nurture fans.
Ask them what their other problems are.
Combine this feedback and your idea list for next week's giveaway.
Help me spread this message to others who need it:
Retweet the top tweet. https://t.co/u2KzrW0eBj
Giveaways are smashing the Twitter algo.
— Ranbir Singh \U0001fa9e (@ranbirsinghlive) March 12, 2023
I've gained dozens of followers & newsletter subs.
Small accounts NEED to use them.
They drive growth, but also experience & trust.
Because a Twitter giveaway is a mini-product launch.
Steal my blueprint to create 1 in an afternoon\U0001f9f5 pic.twitter.com/44LyV6Krvu
More from All
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)
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)
You May Also Like
Nano Course On Python For Trading
==========================
Module 1
Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...
... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit https://t.co/EZt0agsdlV
This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!
In Module 1 of this Nano course, we will learn about :
# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)
# Using Google Colab
Intro link is here on YT: https://t.co/MqMSDBaQri
Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb
You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want
# Importing Libraries
Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.
==========================
Module 1
Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...
... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit https://t.co/EZt0agsdlV
This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!
In Module 1 of this Nano course, we will learn about :
# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)
# Using Google Colab
Intro link is here on YT: https://t.co/MqMSDBaQri
Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb
You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want
# Importing Libraries
Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.
This is NONSENSE. The people who take photos with their books on instagram are known to be voracious readers who graciously take time to review books and recommend them to their followers. Part of their medium is to take elaborate, beautiful photos of books. Die mad, Guardian.
THEY DO READ THEM, YOU JUDGY, RACOON-PICKED TRASH BIN
If you come for Bookstagram, i will fight you.
In appreciation, here are some of my favourite bookstagrams of my books: (photos by lit_nerd37, mybookacademy, bookswrotemystory, and scorpio_books)
Beautifully read: why bookselfies are all over Instagram https://t.co/pBQA3JY0xm
— Guardian Books (@GuardianBooks) October 30, 2018
THEY DO READ THEM, YOU JUDGY, RACOON-PICKED TRASH BIN
If you come for Bookstagram, i will fight you.
In appreciation, here are some of my favourite bookstagrams of my books: (photos by lit_nerd37, mybookacademy, bookswrotemystory, and scorpio_books)
