DAOs will go down as the most impactful innovation in Crypto and Web 3.

Here’s a simple breakdown of what DAOs are and why you should pay attention to them:

Decentralized Autonomous Organizations are communities with a shared bank account.

They're a new way for people to work together.

Think of them as the next evolution of the LLC or Company.
So what makes DAOs different from a traditional company?

It all boils down to ownership.

Let me explain.
You may have seen this going around twitter:

Web 1: Read-only

Web 2: Read & Write

Web 3: Read, Write & Own

Sound familiar?
Think of it as phases of the internet:

Web 1: Static read-only websites.

Web 2: Read and write on social media platforms. Earn likes and followers.

Web 3: Read, write, create. Earn equity in the platforms that you create value for.
The simplest way to think about Web 3:

If you create value for a network, you should be able to capture the value you create.

With social media, you get hearts/followers, but there's no way to share in the value you bring to the platform itself.

DAOs provide a better model.
DAOs leverage crypto in a new way — creating/distributing tokens that represent ownership in the DAO.

Create value for the group ⇒ earn tokens that signify ownership.

Imagine getting paid incrimentally in equity that is proportionate to the value you bring to the table.
As the DAO becomes more valuable over time, so do the tokens that represent ownership.

This allows members to share in the growing value of the collective.

It also allows the DAO to recruit more highly curated talent over time.
Work and compensation in a DAO can be task-based.

This gives people more control of their work destiny.

You can work for several DAOs at once.

Focus on providing value with your unique skillset for collectives whose mission you believe in.
People with highly specific and competent skills will not be strapped to one company.

This broadens possible revenue streams, making even more niche creative passions viable career routes.

Don't underestimate how transformative this will be.
Token holders also get to vote and take part in decisions within the company.

More tokens = more votes, but everyone has a seat at the table.

Imagine being able to actually participate in company-wide decisions without first climbing the grueling corporate ladder.
DAOs are not without their drawbacks:

Flattened hierarchies means everyone decides together, but things move slower.

New technology means frequent novel problem-solving.

Brand new territory for regulation and legal issues.
In summary, DAOs are a new type of organization that:

Democratize decision-making

Compensate members with tokenized equity

Make work task-based and flip the script on work-life balance
For more on DAOs, NFTs, and the Creator Economy, check out these accounts:

h/t
@Cooopahtroopa
@mattmedved
@FWBtweets
@punk6529
@JpegViceroy
@gregisenberg
@grimey_anim
If you found this thread helpful, please retweet the first tweet.

And give me a follow! @real_option

Join me as I explore Web 3, Music NFTs, and the Creator Economy.

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Hello!! 👋

• I have curated some of the best tweets from the best traders we know of.

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2. THE ABSOLUTE BEST 15 SCANNERS EXPERTS ARE USING

Got these scanners from the following accounts:

1. @Pathik_Trader
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4. @SouravSenguptaI
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These setups I found from the following 4 accounts:

1. @Pathik_Trader
2. @sourabhsiso19
3. @ITRADE191
4.


4. Curated tweets on HOW TO SELL STRADDLES.

Everything covered in this thread.
1. Management
2. How to initiate
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4. Examples
5. Videos on
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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