9 AI tools so useful it feels like you have your own team of interns.

1. @supernormalapp

Takes detailed notes and transcribes meetings without you lifting a finger.

https://t.co/S3LTggnjyF

- Connect your GMeet/Zoom account
- It takes notes for you
@supernormalapp 2. ChatGPT by @OpenAI

AI bot capable of generating impressively detailed human-like responses for your questions.

https://t.co/o3NhmvOPnN

My friend here used it to understand Docker in Eminem style
https://t.co/1TOS2CrWfu
@supernormalapp @OpenAI 3. AI Avatars

https://t.co/U5dAjo83wn by @dannypostmaa
https://t.co/H1tpIxYsui by @levelsio

Create your own 4k quality AI-generated avatars in seconds.
@supernormalapp @OpenAI @dannypostmaa @levelsio 4. Descript App

Edit your podcast by just editing the transcript. Sound editing like editing a word doc.

Descript will copy your voice and autofill whatever edits you make to the transcript.
@supernormalapp @OpenAI @dannypostmaa @levelsio 5. Magic Studio

Edit out parts of photos you don't need
- Upload the photo
- Select the part you don't want
- Press erase

https://t.co/j7Lr2d2zBt
@supernormalapp @OpenAI @dannypostmaa @levelsio 6. Tweet Hunter (😊)

- Find new tweet inspiration
- Generate thread starters
- Rewrite your content with AI

https://t.co/IOZfSnwglb
@supernormalapp @OpenAI @dannypostmaa @levelsio 7. Narakeet

Create realistic voiceovers for your videos

https://t.co/oWZMIxtUDl

- Upload script
- Select Voice
- Get professional audio & video in minutes.
@supernormalapp @OpenAI @dannypostmaa @levelsio 8. Riverside

Records your podcast locally, uses AI to keep the right speaker in view, mute unnecessary sounds and produces studio-quality podcasts for you.

https://t.co/YrWb32KiMX
@supernormalapp @OpenAI @dannypostmaa @levelsio 9. Taplio

Generate post ideas using AI, Schedule, Analyze Performance and build your audience.

https://t.co/eyws4eabt1
@supernormalapp @OpenAI @dannypostmaa @levelsio Follow @tibo_maker for more on indie making & audience building.

And if you want to support, RT the first tweet below ❤️
https://t.co/OWq5VrCR1V

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Master Thread of all my threads!

Hello!! 👋

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

• Making one master thread and will keep posting all my threads under this.

• Go through this for super learning/value totally free of cost! 😃

1. 7 FREE OPTION TRADING COURSES FOR


2. THE ABSOLUTE BEST 15 SCANNERS EXPERTS ARE USING

Got these scanners from the following accounts:

1. @Pathik_Trader
2. @sanjufunda
3. @sanstocktrader
4. @SouravSenguptaI
5. @Rishikesh_ADX


3. 12 TRADING SETUPS which experts are using.

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
3. When to exit straddles
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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