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Ethereum smart contracts are Turing complete programs that operate on money and derived assets. With a market capitalization in the three digit billions, there is an interest in quantifying their usage. Despite blockchain data being public by design, large scale analysis of smart contracts is technically challenging to do on a large scale. We summarize methods to analyze contract usage on the Ethereum blockchain and categorize the most popular contracts by their application domain and behavior. Especially, changes in behavior before and after removing gas refunds on the Ethereum blockchain are analyzed. Furthermore, we quantify the adoption of final Ethereum Requests for Comments (ERCs), that standardize smart contracts for certain applications. According to the used metrics, trading related smart contracts, are the most popular. In that context, we explain, why using just a single metric can be misleading. The removal of gas refunds lead to significant changes in contract lifetime and state cleanup.

Presentation about: Using stainless for Full Coverage Unit Test Generation

Note: Navigate the presentation with n and p keys. There are 2D slides.

The lecture Machine Learning for the Natural Sciences promises to focus on applications of machine learning to natural sciences, especially physics and chemistry. However, most of the actual content is repeating machine learning basics, that is already in foundational lectures on machine learning. In the remaining time, a few interesting are presented, but sadly just very shallowly.

There is no such thing as the perfect university exam, but if we agree that its purpose is to give an objective score about an individual’s comprehension of the covered topics, then there is clearly a way to be less wrong when creating exams. This post lists a few DONT’S that can be easily avoided. If you think there is an anti-pattern in this post, you can write me an email and I will add it here.

Lecture summary of the lecture operation systems security, organized with self test toggles. The lecture is concerned with binary exploitation from an offensive as well as a defensive point of view. I can really recommend the lecture, if you are interested in modern security mechanisms implemented by operating systems and hardware.

With my CTF team KITCTF each semester we do introductory talks about how to get started with CTFs. I gave the introductory talk to the introductory talks, i.e. what even are CTFs?, twice.

With KITCTF we participated in the bo01lers CTF and finished 6th. There were some quite fun challenges. Including the resnet challenge, which is a machine learning challenge. I hope to see more machine learning challenges in the future.

Answers to self test questions for the lecture “Data Science I” at KIT. If you spot any errors, write me an e-mail or Discord message.

Self test questions for the lecture “Machine Learning - Foundations and Algorithms” at KIT.