You have ONE goal.
It's not about your skills
It's not about qualifications
It's not about proving worth
It's about being MEMORABLE.
I've interviewed thousands in my career, and I usually know my decision in the first 10 minutes.
Here are 5 tips that will separate you from the crowd:
You have ONE goal.
It's not about your skills
It's not about qualifications
It's not about proving worth
It's about being MEMORABLE.
This is first because it's the MOST IMPORTANT.
You will be remembered for telling your story in an interesting, relatable way.
Study storytelling and practice your stories in the mirror.
EVERYONE talks about their experience, but few people can communicate it memorably.
Improve this one thing and you'll outperform 80% of your competition.
But, to take it further, let's look at the next one...
SCOUR the internet to learn everything you can about the company.
Here are your best sources of information:
- Their webpage
- Linked-In
BUT, this only benefits you if you know what to do with the information...
You do this to show that you're truly interested.
Hiring managers LOVE a candidate who talks about the research they've done on the company.
Tell them your 3 favorite things you read about them.
Every interview ends the same way...
"Tell me why you're the best candidate for this position"
THIS IS THE MOST IMPORTANT QUESTION OF THE INTERVIEW
Here's how you ace it, every time...
All candidates say they are:
- Driven
- Hard-working
- Team players
These are boring and NOT unique.
Share EXACTLY what it is that makes you:
- Unique
- Different
- LEGENDARY
This answer should become second nature.
https://t.co/31QSlUi23u
Learn how to interview, folks! pic.twitter.com/yr1iG2yveS
— Anthony Disney (@buildinglegends) July 23, 2022
At the end of the interview, you will be asked if you have any questions.
Most people blow it by not asking any questions at all.
If they do, they ask about:
- Turnaround time
- Training
- Salary
These do not help you.
Instead...
- What's the biggest challenge facing the team today?
- What does greatness look like in this role?
- What has your career looked like at this company?
The last one gets them to talk about themselves, which ironically makes them remember you.
When talking about why you want the job, don't be like everyone else.
Everybody wants:
- More money
- Promotions
- Opportunities
You blend in by saying you want to grow your career.
You should instead tell them your...
Managers love candidates who know what they are passionate about.
So tell them how this position aligns with your passions and how you will gain fulfillment in the position.
This sends the message that you will work hard because you'll love what you're doing.
Help me get it in front of more people.
RT the tweet below.
Have a great day!
https://t.co/pQv3MbTbrY
I've interviewed thousands in my career, and I usually know my decision in the first 10 minutes.
— Anthony Disney (@buildinglegends) July 28, 2022
Here are 5 tips that will separate you from the crowd:
If you're struggling in your career, need interview advice, or resume services, please see below:
https://t.co/pMGkq0MTIJ
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)
