Articles

I like hackathons. Hackathons are competitions that run over multiple days — typically a whole weekend — and in which the participants — programmers and designers — solve a challenges in order to win a prize. Typically, there are a few challenges that are all based around some theme — blockchains, sustainability, democracy, and so on.

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As time goes on, smart contract requirements change. Often, a feature is added or a bug is found. But, once a smart contract has been deployed on the blockchain, it can’t be upgraded. The entire value proposition of a blockchain is immutability. Yet, in the real world, contracts change all the time. For example, a labor contract will change when new labor laws are introduced. Similarly, there are many circumstances under which smart contracts change as well, such as

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When building your smart contract platform — whether on Ethereum or any other blockchain — at some you’ll be faced with the question of ownership. In this article, I will explain three different types of smart contract ownership and discuss their advantages and disadvantages.

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Have you ever wondered how your users can spend ERC20 platform tokens to buy assets and other tokens from you? Or — in more technical terms — how to make sure a smart contract executes a particular function as soon as it receives tokens? This post is all about using ERC20 approve() and transferFrom() to allow your users to do much more than just keep tokens in a wallet.

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Over the years I have found out how important it is to constantly challenge yourself. Coding is craftsmanship. A good craftsman will always try to improve the toolbox they use every day. Code patterns belong in any good toolbox. When used wisely, they allow anyone to clearly communicate their program’s intent to both people and machines. Code is not just something that machines execute — it’s also something that people read, understand, and maintain.

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I had the tremendous privilege of speaking about creating sustainable token platforms at TokenSky 2018 in Seoul, Korea. Not only did I have a great time presenting some of the ideas that I’ve come across, I also encountered a lot of stimulating questions from individuals and companies all interested in enhancing their platform using some kind of blockchain token.

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Not many things a business owner has to go through are as scrutinous and unpleasant as a data privacy audit. In the case of Germany, this will include a state commissioner for data protection putting your companies data processing and storage practices under the microscope. Luckily, as it turns out, most of the time you’ll already be doing the right thing. Salting user passwords, allowing customers to delete their account, and allowing users to request all personal data that is being stored about them should be business as usual for most of us.

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Imagine this: You’re pitching an idea for an interesting data science problem that you can solve for your client. The client is sold on the idea and wants to immediately know how fast you can get it done, and more importantly, what the project milestones will look like.

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A great method to improve memory usage of Pandas DataFrames is by converting columns with categorical variables to use the data type categorical.

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I believe that being a data scientist is about communication first, and data science second. This means that two abilities are necessary.

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Using .transform(), you can transform the way you aggregate DataFrames.

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The other day I caught myself writing one too many df.groupby() in Pandas, and thought to myself:

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Scaling Pandas DataFrame aggregations can be quite tricky. I have had a very specific problem to solve that involved aggregates on group by expressions.

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