At @telestoAI, we have built the entire backend of our competition platform in FastAPI.

Why did we choose this instead of Flask or Django?

πŸ‘‡ This is a thread about why.
1️⃣ Defining schemas for endpoints is brilliantly simple with Pydantic.

You only have to create a Pydantic class and use type annotations in the path operation function.
2️⃣ Dependency injections. This is such a powerful and versatile feature!

Essentially, these are functions that are automatically called during path operations, handing the return value as an argument o the path operation.

One common usage is to get database connections.
But they can do much more! For instance, in our app app.telesto.ai, we use a dependency injection to get the user based on their session cookie.

If an endpoint requires special permissions, the dependency takes care of that.

Dependencies also simplify testing.
3️⃣ Working with databases is simple and fluent.

FastAPI doesn't force any ORM framework on you like Django and you don't have to go out of your way with extensions like Flask.

What you use and how you use it is completely up to you!
4️⃣ Autogenerated Swagger UI.

This is provided by default. Really useful for our one-man frontend team, composed by @nandurr.
5️⃣ Healthy ecosystem.

If you want to build fast, you shouldn't have to spend time pushing the boundaries of the framework or pioneering new features. When most things you need have been already built by others and there are several extensions, you are set. FastAPI is like this.
🏁 Our team absolutely loves FastAPI, and it enables fast backend development for us, which is absolutely essential for a bootstrapping startup.

Our team agrees that it was the best choice.
In the next few days, I will create a thread where I talk more about our backend challenges in detail.

If you are about to start a new project, stay tuned! It is better to learn from the mistakes of others, than from your own :)

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More from @TivadarDanka

11 Feb
Expected value is one of the most fundamental concepts in probability theory and machine learning.

Have you ever wondered what it really means and where does it come from?

The formula doesn't tell the entire story right away.

πŸ’‘ Let's unravel what is behind the scenes! πŸ’‘ Image
First, let's take a look at a simple example.

Suppose that we are playing a game. You toss a coin, and

β€’ if it comes up heads, you win $1,
β€’ but if it is tails, you lose $2.

Should you even play this game with me? πŸ€”

We are about to find out!
After 𝑛 rounds, your earnings can be calculated by the number of heads times 1 minus the number of tails times 2.

If we divide total earnings by 𝑛, we obtain the average earnings per round.

What happens if 𝑛 approaches infinity? πŸ€” Image
Read 8 tweets
4 Feb
If you are building a product, chances are you severely underestimate the importance of idea validation. (Especially if you are a developer.)

Key business assumptions can flop because you fail to look at different angles.

What are some basic questions to ask?

🧡 A thread. 🧡
𝐀𝐦 𝐈 𝐬𝐨π₯𝐯𝐒𝐧𝐠 𝐚𝐧 𝐞𝐱𝐒𝐬𝐭𝐒𝐧𝐠 𝐩𝐫𝐨𝐛π₯𝐞𝐦?

Often, the problem is not important enough to justify the existence of a solution. This is the most basic trap to fall for: there is no market need for the product.
Take a look at the top 20 reasons why startups fail by @CBinsights. The number 1 is no market need, causing around 43% percent of failures.

cbinsights.com/research/start…
Read 11 tweets
2 Nov 20
So, you want to wrap your machine learning model into an API. Flask used to be the best tool for that, but lately, FastAPI has become my favorite.

Here are my five main reasons why! πŸ‘‡
1️⃣ Simple yet brilliant interface.

You define the request body model in Pydantic, write the endpoint function to process it, and finally register the route to the app.

That's it.
You can launch the app right away with uvicorn, ready to receive requests!
Read 10 tweets
1 Nov 20
Have you ever implemented a dynamic function dispatcher in Python, where you can register functions at runtime using the decorator syntax? (Like the routers for FastAPI.)

I did this recently, and I am going to teach you how to do it! I'll walk you through it in the thread below.
We will build an event handler to catch arbitrary events and dynamically execute a function to handle the event.

(A good example is catching events in a webhook listener.)

Time to use some decorator magic!
The usage is straightforward:
1) instantiate the EventHandler,
2) register handler functions for specific events,
3) pass the event to the EventHandler instance when caught.
Read 9 tweets
30 Oct 20
Last week I made a massive 4000 words article about mathematics the No. 1 trending on Medium.

I achieved this by setting three guiding principles, resulting in explosive growth.

Here is a thread about how can you do it too. Image
1⃣ Set out to write the single most important article on the topic. Instead of looking for quick wins, aim to create the best resource out there.

Making this article took me more than a month. Every minute of work was worth it.
Pay attention to all the details, make sure you understand every nook and cranny of the subject.

Explain complex technical details in a way such that even newcomers would get them.

Never compromise on quality. If you think that something can be done better, do it.
Read 12 tweets
28 Oct 20
Neural networks are getting HUGE. In their @stateofaireport 2020, @NathanBenaich and @soundboy visualized how the number of parameters grew for breakthrough architectures. The result below is staggering.

What can you do to compress neural networks?

πŸ‘‡A thread.
1⃣ Neural network pruning: iteratively removing connections after training. Turns out that in some cases, 90%+ of the weights can be removed without noticeable performance loss.
A few selected milestone papers:
πŸ“°Optimal Brain Damage by @ylecun, John S. Denker, and @SaraASolla. As far as I know, this was the one where the idea was introduced.

papers.nips.cc/paper/250-opti…
Read 12 tweets

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