Evolution of Deep Learning by Hand ✍️ As my tribute to Geoff Hinton's Nobel Prize, I drew this animation to illustrate the key idea behind Hinton's major contributions to deep learning over the years, with artistic liberty.
OAuth 2.0 Explained.
OAuth 2.0 is an authorization framework that enables applications to access a user’s data on another service (like Facebook or GitHub) without sharing the user’s password.
It’s essentially a digital handshake between the app, service, and user, with everyone agreeing on what is shared.
Now that we’ve covered what it is, let’s dive into how it works.
The process generally follows 6 steps with 4 components typically involved:
🔸 Client (app wanting access)
🔸 Resource owner (user)
🔸 Authorization server
🔸 Resource server
To understand the process, let’s take a look at how a game would connect to a player’s Facebook account.
Step 1) Request access
Within the game (client), the player (user) clicks on a “connect with Facebook” button to link their profile and find friends.
Step 2) Redirect to service
The game redirects the player to Facebook’s (service’s) login page.
Step 3) Permission request
After logging in, the data that the game is requesting access to will be shown to the player which they can either allow or deny.
Step 4) Authorization code
If the player gives their approval, Facebook redirects the player back to the game with an authorization code (from authorization server). The code is a temporary credential that proves the player’s consent.
Step 5) Exchange code for token
The game now sends the authorization code along with its own identification to Facebook’s server in the background. Facebook identifies the authorization code and the game’s identity and returns an access token.
Step 6) Use the token
The game can now use the access token to request the agreed-upon data from Facebook (from the resource server), like the player's friends list.
In this process, the player’s Facebook credentials were never shared, but the game was able to access the agreed-upon player data from Facebook. This is what OAuth 2.0 facilitates; allowing third-party applications to access data from services in a secure manner without sharing credentials.
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LSTM by hand✍️Excel ~ I designed this exercise to show it is possible to calculate a simple LSTM by hand. Green🟩: Short Term. Blue🟦: Long Term. +equations. +medium.👇Join the 'AI Math' community. Download xlsx.
Autoencoder by hand✍️Excel~ I designed this exercise to show how an Encoder-Decoder network convert input to code and reconstruct input from code. It is annotated with equations, PyTorch, and graphs. I also made a medium version.👇Join the 'AI Math' community. Download xlsx.
I miss building simple, working software.
At some point, we decided to complicate everything for no reason. Today, people can't build anything without using three frameworks, 17 libraries, and a swarm of microservices.
And here is a funny paradox:
To understand how these complex systems work, we've had to build systems and tools that generate data we can later analyze. But the more data we produce, the harder it is to process and make sense of it.
We are in the middle of an observability crisis. The tools we have are inefficient, and we don't have enough people to keep systems running.
A few weeks ago, I met the team @resolveai, and they have built a fundamentally new approach to observability and incident management:
Instead of depending on humans to run a system, Resolve built a Production Software Engineer who runs the system using AI while letting people supervise.
And it's not only crazy, but I think this will fundamentally change how we monitor and maintain systems in production for years to come.
I recorded a quick video to showcase a simple example of how Resolve works behind the scenes.
There are two main things I'd like you to notice:
1. The tool can correlate data across logs, metrics, and traces coming from different systems. You don't have to do any work to get the information that matters right in front of you.
2. (This is the big one!) The tool can diagnose what's happening and give you instructions on how to solve it. It can produce causal relationships across the entire system stack.
Resolve is backed by investors like Replit's founder Amjad Masad, Reid Hoffman, Jeff Dean, Fei Fei Li, Andy Price, among others.
They are currently working with a select number of companies and want to onboard a few more. If you are interested in trying them out, go to this link:
https://t.co/6Q1eCoOUZH
Honestly, this is one of the most impressive uses of AI I've seen.