Most security work looks like this: find vulnerability, patch vulnerability, move on. Repeat until exhausted.

In Part 1, we learned what makes graph databases different: data is stored as nodes (things) and relationships (connections), and those connections are first-class citizens in the database. We also wrote your first Cypher query to find patterns in banking data. Now it’s time to go deeper. In this article, lets learn how to create your own graph data, master essential Cypher clauses, and write the queries that make graphs truly powerful.

This tutorial is for anyone curious about graph databases, even if you have never worked with a database before. Using simple banking examples — customers and the products they use — we will explore how Neo4j represents data as relationships rather than tables. By the end of this article, you’ll understand what a graph database is, why it’s useful, and how it forms the foundation for building recommendation system

I completed the AI Fluency : Framework and Foundations from Anthropic. Here are my notes

Automating Exploratory Data analysis using PandasAI and chatGPT.

LLMs hallucinate.Your job is to ensure they don’t embarrass you, your company, or your brand.

Disclaimer
This is a personal technical experiment that I conducted independently and is not associated with, endorsed by, or representative of my employer in any way. It should not be considered financial advice or commentary of any kind. The experiment was performed using publicly available documents from the bank where I work. Any errors or omissions are solely my responsibility.

Notes from vibe coding my first agent

Notes from a course I just completed

Understanding the strangler fig pattern . If you are wondering what the tree looks like -

Going down the rabbit hole to understand latency

My notes from the decision intelligence class

Notes on critical success factors :

Notes on stakeholder management:

Notes on Estimates: I have found the Richter-Scale estimation technique explained here .Some key highlights from this article

Notes on writing good acceptance criteria: I have found this article Patterns of effective acceptance criteria useful in understanding how best to write good acceptance criteria.

Notes on Tech Debt: I have found this article A Taxonomy Of Tech Debt very useful in understanding tech debt and various strategies to deal with it

Notes on mentorship: I have found this article How to mentor software engineers on mentoring engineers to be actionable yet devoid of any platitudes. Some of my favourite highlights below -

Page to hold the weekly progress made on the blog