Santiago Profile picture
7 Dec, 18 tweets, 3 min read
Interviewing for technical positions is a broken process.

I believe this is going to change soon.

Getting a job in the software industry we’ll look very different within the next 10 years.

Some thoughts: ↓
2. This is the game we are playing today:

Write down in a piece of paper as much stuff as you possibly can. Make sure you embellish the story. Make yourself look like a hero.

Call this a "resume" and email it to as many companies as you can.
3. Companies collect a list of those resumes.

They sort them by the biggest liars: whoever sells themselves the best, goes right to the top.

They choose the top 10 and start making calls.
4. But of course, we all know the game.

Companies don't believe for a minute what you wrote down.

They know you are a good storyteller, but they need a software developer, so they need to ask hard questions.
5. A little detour:

Some people once decided that asking brain teasers during the interview was a good idea:

"How many ping-pong balls can you fit in a school bus."

"We just want to see your thought process" they say.

This doesn't work.
6. After a couple of studies came out proving this was dumb, Big Tech moved on. Mostly.

Unfortunately, many companies didn't get the memo.

In 2021 there are still companies asking "how many gas stations are in the U.S" and "how many public bathrooms are in your city."
7. Not everyone has to go through this, which is good.

Instead, most of us have to find the shortest path in a graph without using Dijkstra's and sort a linked list.

In a whiteboard!

Or even better: in Google Docs while somebody stares at you.
8. This is crazy and it's been going on for quite some time now.

Think about how many good people aren't willing to go through this process.

Or how many brilliant engineers tried, but got turned down because they forgot what the heck is Big O notation.
9. All of this is nonsense. Most people I've talked to agree.

• People don't want to prepare for an interview.

• Managers don't want to conduct interviews.

• The whole process is exhausting for everyone involved.
10. There are several things happening right now that make me hopeful for the future.

First, remote work is putting a lot of pressure on the system.

Many people have realized they have much more control they thought. They can now work for almost anyone from almost anywhere.
11. Second, I believe working for a single employer will become the exception rather than the rule.

More people will spend their time working on multiple projects they care, instead that working for a single company.

As an example, look at what's going on with DAOs.
12. I believe that a "company-less" workforce makes much more sense.

Instead of working for Google, you'll work on Gmail. Instead of working for Netflix, you’ll work on the security of their platform.

Projects, not companies.
13. There's a missing piece in this story:

How do companies know whether you have skills?

I think a couple of things will happen.
14. First, a project-based workforce will work similarly to open-source projects today.

People show up, pick a task, get it done, and get paid.

There are reviewers making sure the work is done appropriately.
15. Obviously, "getting paid" is mostly absent from the open source world today (unless you get a salary from the company that owns the project.)

But this is changing.

Again, look at what's happening with DAOs.

This model seems to make a lot of sense.
16. The second thing:

We will replace the resume (liar, liar, pants on fire.)

I'm hopeful that some of the work with decentralized data and proof of ownership will play a significant role here.

We need to prove our skill-set automatically for anyone that cares to look.
17. There's a lot going on here, but something is clear to me.

Nobody, absolutely nobody, is rooting for the technical interview to stay.

Do you know what happens when everyone hates something? We find a better solution.

But there's something even more important.
18. Look at the last couple years. Look how much the tech world has changed.

Demand continues to rise. Companies are struggling to find talent and even keep who they have.

More power to us.

Who's betting against the internet?

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More from @svpino

3 Dec
When I was at school, I designed a banking application that didn't need authentication (for a class project.)

Yeah, I know it sounds crazy, but I've always been obsessed with "invisible" security.

I see one solution that may get us there for the first time. ↓
The thing that has impressed me the most during my experiments with web3:

You open a website, connect your wallet, and you are in.

That's it.

No "sign up," no "username and password," no "check your email for verification."
I use Dashlane (a password manager.) I have 520 passwords, and I'm reusing 149 of them.

Even better: I have 33 "compromised" passwords.

This is ridiculous.
Read 6 tweets
1 Dec
What is machine learning and why you should care about it?

Let me try to convince you: ↓
2. Let's start with a simple programming exercise:

"Write a function that returns 0 if a supplied number is even or 1 if the number is odd."

If you are a developer, I'm sure you know how to write this function.
3. To simplify things, let's represent this function as follows:

y = f(X)

Where:
• X → The input number
• y → The result (0 if even, 1 if odd)
• f → The function that does the work

We can now get to the interesting part.
Read 16 tweets
29 Nov
One of the things I love about Python: Duck Typing + EAFP.

A short thread that will change the way you write code.

2. The idea behind Duck Typing:

If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck.

In other words: the functionality of an object is more important than its type. If the object quacks, then it's a duck.
3. Duck Typing is possible in dynamic languages.

Look at the attached example.

Notice how "Playground" doesn't care about the specific type of the supplied item. Instead, it assumes that it supports the bounce() function.
Read 11 tweets
22 Nov
For one second, let's forget the idea of a central database.

Imagine a product that doesn't store your data. Instead, you keep that information. You allow others to use it at your convenience and close it whenever you want to.

Let's talk about one example. ↓
2. Think about those people that have built excellent profiles using @kaggle.

They have participated in many competitions, shared their knowledge, and built impressive curriculums.

What would happen if Kaggle decides to ban them?
3. This is not science fiction.

Every single company out there can ban you if they decide to do so.

Maybe it is justified, maybe it isn't, but that's beyond the point.

What happens then?
Read 18 tweets
19 Nov
Deploying a machine learning model is not a trivial task.

Here are some of the questions you may have to answer every time: ↓
1. What's the input format expected by your service?

2. How can we validate the input is valid? What's the appropriate action if it isn't.

3. What transformations are needed to turn the service's input into the model's input?
4. What transformations are needed to turn the model's output into the service's output?

5. Do we need to allow for batch processing of data?

6. How much time do we have to return an answer?
Read 6 tweets
10 Nov
Over the last few months, I've introduced three main improvements to how I build machine learning models.

Keep in mind that my job is focused on Computer Vision, and I mostly use TensorFlow and Keras.

Here are the highlights: ↓
First, I replaced image generators with the tf.data API.

This change alone has had a major impact on training time. But it doesn't stop there:

• My code is much cleaner
• A data pipeline is easily reusable

tf.data is a must.
Together with loading data, I used image generators to perform data augmentation.

Now, I try to make data augmentation part of the model using Keras' preprocessing layers.

These augmentations now happen in the GPU. This is another nice boost!
Read 6 tweets

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