The conceptual foundation of OpenML dates all the way back to 2013 when a college boy in Physics major was reading and implementing the Matrix Numerov Method for Solving Schrödinger Equation

AI is not just a consumer of linguistic data; it is a producer of new philosophical questions. It forces us to be more precise about what we mean by "meaning," "understanding," and "thought," turning centuries of abstract debate into a pressing, practical challenge.

Specifically created to solve the massive memory bottleneck and inefficiency of serving Large Language Models in production, vLLM at its core resolves the problem of LLM serving and inferencing. Before vLLM, the bottleneck in serving LLMs wasn't just the model weights themselves, but the KV Cache (Key-Value Cache) - the memory required to store the attention context for generating tokens sequentially. This posts helps us systematically learn KV cache.

In the rapidly evolving landscape of IoT, MQTT has emerged as the de facto standard protocol for data exchange. This post on MQTT is designed to equip decision-makers, solution architects, and IoT professionals with a strategic and practical understanding of MQTT and how to execute it for scalable, reliable, and seamless data movement. Delve into how MQTT can help organization overcome the challenges other IoT protocols cannot address with features such as persistent sessions, retained messages, Last Will and Testament (LWT), Quality of Service (QoS) levels, and more. After reading this guide, you'll be ready to use MQTT to optimize connectivity and lay the proper data foundation to enable any IoT or IIoT use case.

In this post, we explore the core architectural philosophy behind OpenML's backend infrastructure: a deliberate choice of explicit control and standards compliance over framework magic. By anchoring our microservices to the strict rules of the JAX-RS specification, and leveraging precise engines like Jersey and Quarkus rather than comprehensive ecosystems like Spring, we ensure our codebase remains portable and maintain absolute sovereignty over our entire execution environment.

We are all familiar with RAG system as a customized LLM trained with private data. How about applying a codebase as the private data as a RAG for coding agent? ADR (Architectural Decision Records) does this for us