/1 Why is Nginx called a β€œπ«πžπ―πžπ«π¬πžβ€ proxy?

The diagram below shows the differences between a 𝐟𝐨𝐫𝐰𝐚𝐫𝐝 𝐩𝐫𝐨𝐱𝐲 and a 𝐫𝐞𝐯𝐞𝐫𝐬𝐞 𝐩𝐫𝐨𝐱𝐲.

/2 πŸ”Ή A forward proxy is a server that sits between user devices and the internet.

A forward proxy is good for:

1️⃣ Protect clients
2️⃣ Avoid browsing restrictions
3️⃣ Block access to certain content
/3 πŸ”Ή A reverse proxy is a server that accepts a request from the client, forwards the request to web servers, and returns the results to the client as if the proxy server had processed the request.
/4 A reverse proxy is good for:
1️⃣ Protect servers
2️⃣ Load balancing
3️⃣ Cache static contents
4️⃣ Encrypt and decrypt SSL communications

Good read: https://t.co/bNbDjTmain
/5 Over to you: What’s the difference between reverse proxy and load balancer? What are some of the most popular proxy servers?
/6 I hope you've found this thread helpful.

Follow me @alexxubyte for more.

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Tip from the Monkey
Pangolins, September 2019 and PLA are the key to this mystery
Stay Tuned!


1. Yang


2. A jacobin capuchin dangling a flagellin pangolin on a javelin while playing a mandolin and strangling a mannequin on a paladin's palanquin, said Saladin
More to come tomorrow!


3. Yigang Tong
https://t.co/CYtqYorhzH
Archived: https://t.co/ncz5ruwE2W


4. YT Interview
Some bats & pangolins carry viruses related with SARS-CoV-2, found in SE Asia and in Yunnan, & the pangolins carrying SARS-CoV-2 related viruses were smuggled from SE Asia, so there is a possibility that SARS-CoV-2 were coming from
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

πŸ§΅πŸ‘‡(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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