I write a somewhat more complicated, but still relatively simple async message queue server in modern C++20.

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I write a simple, bi-directional async message queue server in modern C++20.

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I try to get ChatGPT to write the codes in the previous posts. It's able to write the basic message queue skeleton, but cannot implement more complicated features such as delivery semantics with caching.

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I write a somewhat more complicated, but still relatively simple async message queue server in Python.

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I write a simple, bi-directional async message queue server in Python.

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What are some of my buy-it-for-life purchases?

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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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