About the blog

AI Tinkering is a personal research archive focused on the intersection of computer systems, data engineering, and Artificial Intelligence.

The premise of this project is simple: to understand complex intelligent systems, one must understand the infrastructure that enables them. From hardware design to relational query execution and state-of-the-art machine learning algorithms, this blog explores computing as a unified continuum.

Here you will find technical post-mortems, mathematical derivations, reimplementations of classic systems, and original experiments. It is an ongoing effort to cultivate academic rigor, master low-level systems engineering, and document the continuous process of technical learning.

Core Exploration Tracks

01. Systems & Core Infrastructure

• Reimplementing foundational data engines, storage layers, and execution pipelines from scratch.
• Low-level memory management, concurrency models, and performance profiling.

02. Hardware Mechanics & Applied Mathematics

• Digital logic design, hardware accelerators, and circuit fundamentals.
• Linear algebra, probability & statistics, multivariable calculus, and optimization theory behind learning systems.

03. Machine Learning & Algorithmic Foundations

• Deconstructing neural network architectures, custom autograd engines, and training dynamics.
• Empirical benchmarking, research paper breakdowns, and low-latency inference.