Amitai Ruskin

How MEV and Flashbots Shape the Dark Side of Private Transactions

Private transactions emerged from the worlds of MEV (Maximal Extractable Value). While there are a few providers that allow transactions to be included in a block without going through the mempool, Flashbots is the most widely used. Today, private transactions have become hackers' primary way of executing attacks. In this blog post, we will look into how MEV and Flashbots evolved and how it is used in high-value and illicit transactions today.

First Steps In Crypto

Several of my friends have recently asked me for advice on how to get started investing in crypto. My crypto journey started during the summer of 2020 with my first Bitcoin purchase. That sparked my curiosity, leading me to go down the “crypto rabbit hole” and I continued investing and working in...

Teslas Make it Or Break it Moment

Tesla is facing strong headwinds, in the last few quarters the company has been experiencing declining EV sales. This is primarily due to high interest rates impacting retail demand for EVs.

Recently Reuters tweeted that Tesla is scrapping their planned $25K affordable sedan a.k.a Model 2....

Tech Titans

One of my favorite books of all time is the “Steve Jobs” biography by Walter Isaacson. As the name suggests, it tells the story of Steve Jobs and his founding of Apple. I found the book highly inspirational, and I think Isaacson did a great job describing Jobs’s brilliance without over idealizing...

Associating Smart Contract To Crypto Project

This project uses ML to associate smart contracts with a crypto project it is associated with. The project included extracting metadata from Etherscan, extracting ML features and evaluating several ML models to determine which is the best predictor

Chainscan

A project that enables users to monitor on chain events. The application has a web-app and a telegram bot as clients. The service listens to an event stream from the blockchain and alerts users when event occurs with associated event data. The project is written in TS and uses Docker, DynamoDB, Github Actions.

Classifying Scam Tokens Using ML

This project is inspired by Forta's ML bot that identifies scam tokens using ML. The approach taken is using TF-IDF to extract diffrent coding patterns in malicious ERC-20 opcodes. The project then evaluates diffrent ML techniques to determine which is the best. Users can use the app Gradio UI to evaluate any ERC-20. The app uses Etherscan behind the scenes to get the source code, preprocess the data and then infers it using the model weights.