Give me 2 minutes and I will save you 10+ hours of wasted time each week:

1. Eliminate decisions

There are certain decisions which we make every single day that you don’t need to make,

2 of the most common are:

• The clothes you wear
• The meals you eat

Predecide on Sunday what clothes you will wear and what meals you will eat.
2. Reduce distracted work

If you’re working without your full focus on the task at hand, you are wasting hours each day.

• Choose a single task
• Turn your phone off

Instead of 8 hours of distracted work each day, do 4 hours of deep work each day.
3. Batch what can be batched

Each time you switch tasks it reduces your mental energy in what is known as “context switching”

Perform similar tasks together:

• Do all your calls after each other
• Do all your writing in one day
• Do all your editing in one day
4. Say “no” more

If you want to reclaim some time, you’re going to have to start saying “no” to some people.

Whether it’s:

• Events
• Unnecessary meetings
• Social gatherings

If your mind doesn’t scream “fuck yes” when you’re asked, say “no”
5. Avoid unnecessary meetings

Meetings or calls can often last 5-12x longer than they need to.

• If the meeting isn’t a priority - avoid it.
• If the meeting is a priority - shorten it
6. Plans your days and weeks

When you go about each day just winging it, you waste an unimaginable amount of time and energy.

• Take 5 minutes to plan your week each Sunday
• Take 5 minutes to plan each day the night before
7. Set deadlines

There’s a concept called “Parkinsons Law”

Which states that a task will expand to the time allocated to it

• Set tight deadlines
• Force yourself to be uncomfortable and get it done

Deadlines create clarity, clarity creates action.
If you want to save yourself hours of wasted time each week, you will need to be disciplined.

Build your discipline with my Masculine Discipline course through the link below ↓
https://t.co/gdU9mJKK2C

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)

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