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Now

20/05/2025

The initial idea for this came from Derek Sivers and his now page. I’m going to be using it to share daily or weekly updates about my study, work and my projects. Derek always shares great content out there and still updates his now page, after 9+ years. Last I checked he was learning Mandarin, reading about AI and taking care of his pet rats.

I will post here frequently and share my journey as a software developer.

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Recent Posts

Concurrency In C# Cookbook

How would you solve a problem where you are inheriting from an async method, but you want to implement it synchronously (in C#)?

What about Reporting Progress as a double value as an operation is executing? Or returing any of the tasks that were completed, instead of all?

This is what Concurrency in C# is about. Stephen Cleary is one of the authors I found through his blog post that was very famous among the C# community. This inspired him to write a book only about Concurrency, and async implementations in C#, and share his ideas.

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Desiging Data Intensive Applications - Book Notes

This is an article containing some excerpts, thoughts and obeservations from Designing Data Intensive Applications: The Big Ideas behind reliable, scalable and maintainable systems by Martin Klepmann.

Chapter 1

In chapter 1, we are introduced to the theory of reliable scalable and Maintable applications.

In this book, we focus on three concerns that are important in most software systems:

Reliability: The system should continue to work correctly (performing the correct function at the desired level of performance) even in the face of adversity (hardware or software faults, and even human error).

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Recommendit

TECH STACK: C#, SQL Server, EF Core, MongoDB, Python(BERT Model), Angular

Recommednit is a project I had been working on and off for around a year now.

As a TV Show enjoyer,a fan of many classic shows like The Wire, The Sopranos, Sucession I wanted to make a small project that would suggest shows based on a users’ top 3. Eventually, through some research I found cosine similarity, a formula that calculates measure of similarity between two non-zero vectors in an n-dimensional space. This was the base of my application. But the question was how do I quantify shows to numbers.

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