Reading List

📖 My Reading List:

NoSQL for Mere Mortals

An accessible yet thorough exploration of NoSQL database models, this book helped me understand the trade-offs between relational and non-relational systems — essential when thinking about how to scale and structure real-time financial or AI-driven applications. I found it insightful for bridging the gap between traditional data management and the demands of unstructured, high-velocity data environments in AI workflows.

Seven Databases in Seven Weeks

This book provided a whirlwind tour of modern databases — from Redis to Neo4j — each suited to different data problems. I appreciated its practical, exploratory tone, which helped me think more critically about data architecture decisions in AI pipelines and how to align the right data model with the problem at hand, particularly in finance where transactional integrity, graph modeling, and time-series performance all matter.

AI Agents in Action

A deep dive into the rapidly evolving world of autonomous AI agents. As someone studying AI, I found this book powerful for understanding how these agents can be built to make decisions, take actions, and even coordinate with other systems — unlocking real world applications for business automation and intelligent financial advisory systems.

The Rise & Potential of Large Language Model Based Agents: A Survey

This research paper breaks down how LLMs like ChatGPT can serve as intelligent agents that go beyond static prompt-and-response. I found it especially relevant in thinking about future enterprise tools — where LLMs could eventually take over complex financial analysis, forecasting, and strategic modeling tasks that once required a team of analysts.

The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey

This was one of the most forward-looking papers I’ve read. It mapped out the infrastructure and logic behind agents that can plan, reason, and even decide which tools to use — essentially becoming intelligent workflows. From a business strategy lens, it gave me a few ideas on how agent-based automation could transform back-office functions and decision-making in finance.

AI 2041 by Kai-Fu Lee

Blending storytelling with forecasting, AI 2041 paints a vivid picture of what the world might look like within the next two decades. As someone studying data science, I appreciated the speculative lens combined with real-world trajectories in AI development. It helped contextualize what technologies are hype versus truly disruptive — and what it might mean for jobs, economics, and society at large.

The Fourth Turning by Strauss and Howe

This book broadened my perspective. While not tech-focused, it offers a generational theory of societal change that helped me think more broadly about disruption cycles. When layered with AI and automation, the timing of these social and economic shifts seemed even more significant. It’s an unexpected but valuable read when considering the long-term implications of technological adoption in finance and governance.