Decide when and where you’ll train.
Look at your schedule and ask, “When am I most likely to succeed?”
Mornings. Evenings. Lunches. Whatever’s best.
Work with your schedule, not against it.
Men, if you want a good physique, read this:
Decide when and where you’ll train.
Look at your schedule and ask, “When am I most likely to succeed?”
Mornings. Evenings. Lunches. Whatever’s best.
Work with your schedule, not against it.
You won’t always feel like training. But the truth is nobody does.
Motivation gets you started but it always runs out.
Never depend on it.
It doesn’t matter which. It’s easy to overplan.
But the truth is they all work. You just need to give one your focus.
• Squat
• Deadlift
• Flat + Incline Bench
• Overhead Press
• Pull Ups
80% of your results will come from these.
- Stop procrastinating
- Become a top 1% male
- Build unstoppable habits
- Gain top tier confidence
Read "Iron Clad Discipline" and become an unstoppable man.
Click here: https://t.co/mWjyFiLttX
Yes, this means don’t avoid legs.
And trust me, women like legs as much as they do biceps.
Take pride in building everything, not just what’s on show.
The goal isn’t to get ripped, it’s to become an athlete.
When you think of yourself as one, you don’t rely on motivation.
You know how to act because you know who you are.
When in doubt, just ask, “What would an athlete do?”
You’re not perfect. Workouts will be missed.
Most people beat themselves up about this, but instead accept you’ll miss one session here and there.
Missing one won’t make a huge impact.
Missing two sets up a pattern. Make a rule to never miss twice.
This account exists to help men:
• Build power
• Build strength
• Build masculinity
Have a good day!
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)
