Summary of reading: October - December 2024

Automatic Differentiation (AD) is an important algorithm for calculating the derivatives of arbitrary functions that can be expressed by a computer program. One of my favorite CS papers is "Automatic differentiation in machine learning: a survey" by Baydin, Perlmutter, Radul and Siskind (ADIMLAS from here on). While this post attempts …

This is Part 5 in a series of posts describing the Raft distributed consensus algorithm and its complete implementation in Go. Here is a list of posts in the series: Part 0: Introduction Part 1: Elections Part 2: Commands and log replication Part 3: Persistence and optimizations Part 4: Key …

In the previous post I talked about running ML inference in Go through a Python sidecar process. In this post, let's see how we can accomplish the same tasks without using Python at all. How ML models are implemented Let's start with a brief overview of how ML models are …

Machine learning models are rapidly becoming more capable; how can we make use of these powerful new tools in our Go applications? For top-of-the-line commercial LLMs like ChatGPT, Gemini or Claude, the models are exposed as language agnostic REST APIs. We can hand-craft HTTP requests or use client libraries (SDKs …

Go 1.23 shipped with a new major feature: ranging over functions (also known as "iterators"), per this proposal. This feature is nicely covered in the official Go blog post from August. This article is a rewrite of my older post that described this feature when it was still in …