For the past year, I had been having frequent system freezes on my NixOS desktop and laptop. The system would become completely unresponsive, to the point where only SysRq commands would work.
Toward the end of 2024, a new type of language model was released: the Large Reasoning Model, or LRM. Examples include OpenAI's o1, Qwen's QwQ and Deepseek's R1. Unlike traditional Large Language Models (LLMs), these models improve their accuracy by performing test-time compute, generating long reasoning chains before outputting their answer.
In the prior discussion on the measures of intelligence, the intelligence of a system is defined as its skill-acquisition efficiency, when given a set of priors and experience. A more intelligent system would end up with greater skill after undergoing a similar amount of experience as a less intelligent system. In essence, this is the measure of the generalization ability of the system in a particular domain.
This article is an exploration into whether LRMs and the method of generating reasoning chains represent a path toward higher intelligence as defined above.
These three books are part of a series on DevOps, the first two through an engaging story about how the protagonists turn around a failing company by reducing lead times for deployments.
One of the companies I worked for had an uncanny resemblance to the events described in the books (particularly The Unicorn Project).
This is a book primarily about plant management/supply chain management, but the lessons and concepts translate well to other fields e.g. software development.
Logging is an often overlooked part of software development, and something I have been neglecting only up till recently. In this post, I compare several logging frameworks.