Share this page!

Vladimir Haltakov Profile picture
Twitter logo
12 Nov, 4 tweets, 1 min read
How does decentralization help? An example...

The creator and lead dev of the popular NFT exchange Hic Et Nunc on the Tezos blockchain decided to shut down the project. He pulled the plug on the whole website and the official Twitter account.

Yet, the damage is not fatal 👇
How come?

✅ NFTs are fine - they are stored on the blockchain
✅ NFT metadata is fine - stored on IPFS
✅ Exchange backend code is fine - it is in an immutable smart contract
✅ The website is back online - it is open-source, so a clone was deployed by the community fast

👇
Of course, this is a dramatic event and the quick recovery was only possible because of the immense effort of the community. But it is possible and it took basically 1 day.

Imagine the damage that the creator and lead dev could do if they want to destroy a Web 2.0 company!

👇
For more details on the event consult this thread:

• • •

Missing some Tweet in this thread? You can try to force a refresh
　

Keep Current with Vladimir Haltakov

Vladimir Haltakov Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @haltakov

Vladimir Haltakov Profile picture
15 Nov
Mastering your Machine Learning Interview 🧑‍🏫

I've summarized some great resources for you that will help you with your Machine Learning interview.

Read below 👇
A great book by @chipro distilling a lot of information on preparing for a machine learning interview.

huyenchip.com/ml-interviews-…

Next 👇
A collection of questions by @svpino who has a lot of experience interviewing people for ML positions.



Next 👇
Read 9 tweets
Vladimir Haltakov Profile picture
9 Nov
How I made $3000 in 3 weeks selling AI-generated art? 💰

Last week I showed you how you can use VQGAN+CLIP to generate interesting images based on text prompts.

Now, I'll tell you how I sold some of these as NFTs for more than $3000 in less than 3 weeks.

Let's go 👇
Background

I've been interested in NFTs for 2 months now and one collection I find interesting is @cryptoadzNFT. What's special about it is that the creator @supergremplin published all of the art in the public domain. This spurred the creation of many derivative projects.

👇
The Idea 💡

My idea was to use VQGAN+CLIP to create interesting versions of the CrypToadz. So, I started experimenting with my own toad #6741.

I took the original NFT image as a start and experimented a lot with different text prompts. The results were very promising!

👇
Read 20 tweets
Vladimir Haltakov Profile picture
8 Nov
Why is AI bad at math? 📐

Machine learning models today are good at generating realistic-looking text (see GPT-3), images (VQGAN+CLIP), or even code (GitHub Co-Pilot/Codex).

However, these models only learn to imitate, so the results often contain logical errors.

Thread 👇
Simple math problems, like the ones 10-year-old kids solve, usually require several logical steps involving simple arithmetics.

The problem is that, if the ML model makes a logical mistake anywhere along the way, it will not be able to recover the correct answer.

👇
@OpenAI is now working on tackling this issue.

In their latest paper, they introduce the so-called verifiers. The generative model generates 100 solutions, but the verifiers select the one that has the highest chance of being factually correct.

openai.com/blog/grade-sch…

👇
Read 13 tweets
Vladimir Haltakov Profile picture
3 Nov
How to create art with Machine Learning? 🎨

You've probably seen these strangely beautiful AI-generated images on Twitter. Have you wondered how they are created?

In this thread, I'll tell you about a method for generating art with ML known as VQGAN+CLIP.

Let's jump in 👇
Short History 📜

In January @OpenAI publicly released CLIP, which is a model that allows matching text to images.

Just days after that, some people like @advadnoun, @RiversHaveWings, and @quasimondo started experimenting using CLIP to guide the output of a GAN using text.

👇
OpenAI published an image generation model together with CLIP, called DALL-E, but without the full code and the pre-trained models.

The results from guiding StyleGAN2 or BigGAN with CLIP aren't as accurate as DALL-E, but they are weirdly artistic.



👇
Read 18 tweets
Vladimir Haltakov Profile picture
2 Nov
Creators only get badges 🏅

There is a problem with how value is distributed in online communities today. It seems we take the status quo for granted and don't discuss it much.

The people that create most of the value, get none of the money! Only badges...

Thread 👇
Online communities

I'm talking about platforms like Twitter, Reddit, Stack Overflow etc. They're wonderful places, where you can discuss interesting topics, get help with a problem, or read the latest news.

However, the people that make them truly valuable receive nothing 👇
It usually looks like this:

▪️ Company creates a web 2.0 platform
▪️ Users create content and increase the value
▪️ Company aggregates the demand
▪️ Company monetizes with ads and subscriptions
▪️ Company gets lots of money
▪️ Creators get badges, karma and virtual gold

👇 Image
Read 25 tweets
Vladimir Haltakov Profile picture
13 Oct
Machine Learning Formulas Explained! 👨‍🏫

This is the formula for the Binary Cross Entropy Loss. This loss function is commonly used for binary classification problems.

It may look super confusing, but I promise you that it is actually quite simple!

Let's go step by step 👇
The Cross-Entropy Loss function is one of the most used losses for classification problems. It tells us how well a machine learning model classifies a dataset compared to the ground truth labels.

The Binary Cross-Entropy Loss is a special case when we have only 2 classes.

👇
The most important part to understand is this one - this is the core of the whole formula!

Here, Y denotes the ground-truth label, while Ŷ is the predicted probability of the classifier.

Let's look at a simple example before we talk about the logarithm... 👇
Read 15 tweets

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Thank you for your support!

Follow Us on Twitter!

Like this author's thread?

Get emails when @haltakov has new unrolls!

Check out other threads from @haltakov!

Read all threads by @haltakov

:(