I argue that virtual goods such as in-game skins make more sense in a centralized walled-garden approach, and NFTs tracked and traded on blockchains are legally problematic.

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I argue that a cryptocurrency like Bitcoin has a weird property: the more you buy of it, the less valuable it becomes.

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I argue that anonymity, irreversibility and decentralization, while interesting technical features that implemented in a fascinating way using cryptographic primites, are not practical for real-world use.

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Are cryptocurrencies really better than fiat currencies? I argue that the answer is negative.

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The crypto space is a fascinating intersection of technology, economics and human psychology. However, I remain skeptical of the value of crypto tokens and NFTs. Here I explain my thought process using FTX Tokens as an example.

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I use Doc2Vec to try to find pairs of similar API functions between Pytorch and Tensorflow.

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I run experiments to determine whether, or to what degree, GPT-3 has developed an comprehension of spatial relationships.

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I reflect on the core points of Fred Brooks' seminal book, The Mythical Man-Month, that I often recall and apply in my daily work.

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Here I will show a "conversation" with GPT-3 to gauge how good an astrophysicist — or an illusion of an astrophysicist — it is. I will focus on cosmology questions, because that's most interesting part of the field.

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In the previous posts, I used the Doc2Vec neural network architecture to compute the similarities between my blog posts, and explored the quality of the scores. In this final post, I show how I added the final Articles You May Like recommendation sections to the blog — it's live!

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In the previous post, I used the Doc2Vec neural network architecture to compute the similarities between my blog posts. In this second post I investigate the results further by examining clusters in graphs.

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One of the things I learned at Facebook is the power of recommendations. Examples are People You May Know (PYMK), Groups You May Like (GYML) and Pages You May Like (PYML). Inspired by these, I am planning to add an Articles You May Like widget to Bytepawn, based on the semantic similarity of blog posts. I use the Doc2Vec neural network architecture to compute the similarity between my blog posts, and return the top 3 recommendations for each page.

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One of the most common Data Science tasks in a business setting is timeseries forecasting. I was curious what methods and libraries other Data Scientists use, so I posted an "Ask HN" on Hacker News. The post generated 89 comments, most of them high-quality. This is my summary of the discussion.

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I use simple Monte Carlo simulations to estimate some mathematical constants: √2, ϕ, e and π.

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I will attempt to enumerate all the categories of questions commonly asked in technical interview loops, and my experience with them.

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Here I will show a "conversation" with GPT-3 to gauge how good a particle physicist — or an illusion of a particle physicist — it is.

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I wrote a Culture Doc for the Data Science team I lead.

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I have further "conversations" with GPT-3, this time asking more difficult questions about real-world Data Science projects I have personally worked on.

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Recently I have been playing around with OpenAI's GPT-3 and I am very impressed by it. It reminds of the famous Arthur C. Clarke quote, “Any sufficiently advanced technology is indistinguishable from magic.” Here I will show a "conversation" with GPT-3 to gauge how good a Data Scientist — or an illusion of a Data Scientist — it is.

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The Elements of Typographic Style by Robert Bringhurst is the most beautiful book I've ever held in my hand. This stunning piece of readable art shows Bringhurst's love for the craft of design, typography and writing, and his mastery of these subject, a result of his life-long devotion to them. I am not a professional typographer, but I enjoyed glancing at, reading and appreciating every page of this book.

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