Blogs on EverythingPython

Where did my day go?

Thu, 28 May 2026 00:00:00 +0000

We live in distracting times. An article from the Harvard Business Review says

To execute a single supply-chain transaction, each person involved switched about 350 times between 22 different applications and unique websites. Over the course of an average day, that meant a single employee would toggle between apps and windows more than 3,600 times. That’s … a lot. This kind of toggling is often dismissed as simply “how we work now,” even though it’s also taxing for people and a waste of time, effort, and focus. Yet these trends are likely to continue or get worse in an increasingly digital and remote work world. This should give companies pause.

Vibecoding a status dashboard

Thu, 08 May 2025 00:00:00 +0000

I work in the engineering division of a supply chain company and our development process, as most companies do, involves the setting up of our SaaS platform locally. This means that there are a lot of dependent services that need to be run first that support the overall execution of the platform.

For e.g. - Our platform’s stack requires SQL Server, Solr, Redis, Postgres, RabbitMQ, MongoDB as core services to be running first. Once these are all running and available, a service called WebAPI can be run that makes use of some of the above to run as well. Following this, there are a few more microservices that need to run, but that need not be part of this article since the underlying concept will already come through with the above being demonstrated.

Napkin Math-ing Reddit

Mon, 07 Apr 2025 00:00:00 +0000

I’ve done some Napkin Math - an essential part of system design - in my different jobs in the last decade but it’s been ad-hoc or purpose driven at that moment and I’ve not thought about it much . Today, I came across a tweet from One2N and a rabbit hole of a blog by Simon Eskildsen after that and it made me want to consciously think about it for a few use cases if only to improve my engineering decisions. I’m sure I will get a lot of things wrong, but over time I hope to tune it to be right.

How to make 3B1B videos using Claude and Manim

Sat, 15 Mar 2025 00:00:00 +0000

3 Blue 1 Brown is an amazing Youtube resource to learn Math and Physics concepts from! Manim is an opensource package created by them to help generate the animations they use to teach these concepts. In this article, I will create a similar video with the help of Claude and Manim!

First, install miktex
Then, in powershell, install ffmpeg : run choco install ffmpeg
Create a new virtual environment and install manim and manimgl - uv pip install manim manimgl
Use Claude to generate a manim program for a formula or something you want to demonstrate - E.g. : My Claude Example
Create a program that you got out of claude - Manim Program for x-squared
Execute it - manim -pql xsquared.py ExplainXSquared

This results in this beautiful 3 Blue 1 Brown like video! - https://www.youtube.com/watch?v=5Ql_vAj0ZVs

Streaming Real-Time AI Inference Results into MongoDB

Fri, 28 Feb 2025 00:00:00 +0000

I’m going to be presenting a talk at a Mongo DB User group meetup later today and I thought I’d write about it so that it would -

serve as reading material for folks wanting to go through the content of my talk later
let everyone else know what this is all about.

Let me pre-emptively say, I don’t know why I chose this to be the “TITLE” of the talk. It is extremely dry. But the topic is actually a lot of fun!

Neetcode For Fun - 1

Wed, 19 Feb 2025 00:00:00 +0000

Solving https://neetcode.io/problems/is-anagram . Started at 1:29 AM.

Let’s think about this for a minute.

A word ‘w1’ is an anagram of a word ‘w2’ if all the characters in w1 belong in w2. Duplicates included.

So the simplest solution would be to go through every letter in w1 and then check if it is there in w2. That’s a O(n^2) complexity because to check against the second string, each letter would have to be compared against. Let’s code it up :

Alias Creation in Windows Using Python

Mon, 17 Feb 2025 00:00:00 +0000

One thing I’ve missed in Windows post my switch from Linux is the ability to create aliases for commands.

In Linux, you can create an alias for a command by adding the following line to your .bashrc or .bash_profile file -

1alias ll='ls -l'

This would allow you to use ll instead of ls -l in your terminal.

In Windows, you can create a similar alias by creating a batch file and adding it to your PATH environment variable.

Focus mode - A "kutty" chrome extension

Wed, 29 Jan 2025 00:00:00 +0000

By now you’ve seen a lot of stuff being built using ChatGPT. A lot, Jerry. A LOT.

I’ve built a few apps and extensions as well, but this is the first I’m writing about.

Over the last 3-4 years, “building” software on any scale has seen quite a radical shift.

Here’s an image to scale -

So when I first tried to build a Chrome extension for a company hackathon in 2022, I took around 12 hours, pulling an all-nighter. I looked up documentation, Stack Overflow answers, sample blogs and trudged through the whole process - The way god intended it. And finally completed…something. The end result was something that did the job I wanted but it looked absolutely horrendous and I had to hand-wave over the UX because I had no time before the presentation.

Azure Devops TIL

Wed, 22 Jan 2025 00:00:00 +0000

TIL about the importance of buildContext in Azure Devops Pipelines for a Docker@2 task. 😵

Kept breaking my head trying to copy files over to an image and it kept saying they weren’t found. Tried a hundred combinations of paths and folder structures.

Finally after some github-repo-hunting and documentation reading (No, LLMs didn’t help) , turned out I was building a dockerfile two levels deeper from my root path and the context was set by default as the path where the Dockerfile was present.

I want to learn Python

Sat, 11 Jan 2025 00:00:00 +0000

This is one of the most common questions I get asked by people who are just starting out with Python.

If you have no prior programming experience, how Do you start? Valid question.

Let’s take it step by step.

Most guides start with understanding the syntax of Python, the basic data types, control structures etc. Which is all well and good but I think the first thing you should do is to install Python on your system.

import re

Sat, 11 Jan 2025 00:00:00 +0000

I was writing some tests for work today and I accidentally ended up using the re.match() function instead of re.search() function and I thought I’d write that as today’s mini-TIL.

In a nutshell,

re.match() checks for a match only at the beginning of the string,

whereas

re.search() checks for a match anywhere in the string.

Here’s a quick example to illustrate the difference:

 1import re
 2
 3string = "Was it a car or a cat I saw?"
 4string2 = "Severus was impressed at Harry's Occlumency skills but he did not show it."
 5# re.match() will return None
 6print(re.match("cat", string))
 7
 8# re.search() will return a match object
 9print(re.search("cat", string))
10
11# re.match() will return a match object
12print(re.match("Severus", string2))
13
14# re.search() will return a match object
15print(re.search("Severus", string2))

Output -

FrozenLists

Tue, 07 Jan 2025 00:00:00 +0000

I happened to see this package - FrozenList - get installed when i was installing a set of requirements for an environment for work. I’ve not yet dived deep into which package frozenlist was a dependency for.

But I still wanted to see what it was and Googled it.

Turns out it’s a package created to create a new data structure which is, well, a list that can be frozen.

I want to run LLM Models locally. What do I do?

Fri, 03 Jan 2025 00:00:00 +0000

One of the most sought after things to learn right now is about how to set up your own local LLM server and how to use LLM models locally. With the arrival of some things like Ollama and LM Studio etc, it has become very easy. Ollama is an orchestrator that lets you serve up models locally, models like Llama 3.1, Qwen-2.5, Phi, whatever models you have on HuggingFace. These are available to be served up and many more.

The Apache Airflow piece

Sat, 23 Nov 2024 00:00:00 +0000

Yesterday I learnt how to get Google News articles using Python - [[Read GoogleNews using Python]]

The next piece I need for my planned workflow is running Airflow - locally - at first.

Airflow is a platform for scheduling and executing workflows. To give an example, suppose I run a restaurant. If it’s not doing very well, you don’t really need a workflow, so one way to reduce work is to be unsuccessful 😀👀 But if I run it successfully and get a lot of orders on a daily basis, I would like to have a mechanism to process everything. Let’s say I have the following steps to be done -

GoogleNews using Python

Fri, 22 Nov 2024 00:00:00 +0000

This article is part of something I’m building up to

I wanted to find a way in which I can get news off of Google News programatically and something that doesn’t need to me be authenticated for it. I wondered if I should build a scraper for it, but thankfully, there’s a Python package already that does it - GoogleNews on Pypi

I installed it in a new uv virtual environment using pip install googlenews and it was pretty easy to use .

List comprehension tuples and obj equals

Thu, 21 Nov 2024 00:00:00 +0000

2 Quick TILs today -

You cannot use a tuple in a list comprehension without params -

The correct syntax is [(i,j) for i in a for j in b

The usage of obj= within a formatted string during printing results in both the object’s name as well its value separated by an “=” being printed -

In the imge below, a is a list as used earlier. Therefore -

Timeline charts

Wed, 20 Nov 2024 00:00:00 +0000

I’m not feeling all too well but I didn’t want to break my streak of writing. So today I’m writing about a non-Python related topic - Timeline Charts.

I was doing a profiling activity for work and I was scratching my head for a way to represent the info so as to explain it easily. Then I got to thinking, how does Google build all those charts to track page loads and API runtimes in Developer tools?

Dataclasses

Tue, 19 Nov 2024 00:00:00 +0000

19th Nov 2024

I was trying to write an article on Dataclasses¹ and I looked up an example on the official website²

Easy enough on the face of it, but it led me into quite a rabbit hole. A dataclass allows for the creation of “special methods” for a class when the annotation @dataclass is sighted.

So if I have a class “Superhero” with the annotation :

 1
 2@dataclass
 3class Superhero:
 4 """
 5 A class of superheroes and properties about their appearance 
 6 in Marvel movies
 7 """
 8 name: str
 9 superpower: str
10 appearances: int = 0
11
12 def beckon(self):
13	 print(f"{self.name} with the super power - {self.superpower} has appeared in Marvel movies {self.appearances} times")
14 
15
16superhero = Superhero("Aquaman","Underwater breathing",2)
17superhero.beckon()

It effectively means that :

Class Methods

Sun, 17 Nov 2024 00:00:00 +0000

I was today years old when I learnt about how to use the classmethod decorator in Python.

I’ve always thought of a class method as a method in a class - just intuitively. But there’s actually a decorator called “@classmethod”.

Imagine you have a class C. A class method in this class would be defined as -

1class C: 
2	@classmethod
3	def cl_method(cls):
4		print(f"I'm in the class method {cls=}")

The distinctive marks of a class method are :

Making your first blog with mkdocs

Sat, 16 Nov 2024 00:00:00 +0000

Now, why do this?

What’s wrong with Medium and Substack and Blogger and Wordpress and all the tens of websites out there? Nothing. You could totally just create a blog there and publish. That’s why Content Management Systems exist - so you could focus on the content and not on the management .

But if you’re like me and want some more control over how you publish your blogs, customize them etc. , you will find this and the other upcoming articles in this category interesting.

Iterating over Itertools - 1

Thu, 31 Oct 2024 00:00:00 +0000

I had the opportunity to work with some of the functions in the itertools package in Python over the month and I thought before the month ends in a few minutes, I’ll write about a few of them -

a) The Count function -

This is useful when you want to iterate from a starting number , say ‘n’ up to an indeterminate number that you do not know at the point of starting the iterator. The example below assumes the end limit to be 10 numbers from the point of starting the counter. The second argument to the ‘count’ function is a “step” parameter.

Installing Streamlit

Wed, 30 Oct 2024 00:00:00 +0000

This is a rather simplistic topic but one of my good friends has been trying to setup Streamlit and has been unsuccessful at it. So he asked if I could help him out. If this helps him atleast, I will have done my job.

Hence this topic for today’s mini essay.

Going forward, pretty much every local package I install will use uv. (How to install uv? https://everythingpython.substack.com/p/uv-python-packager-written-in-rust) Operating system under use - Windows (but this will work on Ubuntu as well) So let’s create a folder for our Streamlit apps -

Reading DDIA - Part 3

Thu, 26 Sep 2024 00:00:00 +0000

Chapter 1 - Reliable, Scalable and Maintainable Applications

In [[Reading DDIA - Part 2]] , I read about the second way in which design decisions are typically driven - Scalability. This section is about what the book says is the third factor - Maintainability.

What is a maintainable system?

My thoughts prior to reading the section -

Something they said in the San Diego Machine Learning struck me as my reaction as well - Maintainability is not one of the “concerns” I would have listed for a data intensive system. But now that it is, let’s think about it - What does it mean to maintain a system? My interpretation is - how would the system be set up for functioning smoothly over time? To that end,

Reading DDIA - Part 2

Wed, 25 Sep 2024 00:00:00 +0000

Chapter 1 - Reliable, Scalable and Maintainable Applications

In [[Reading DDIA - Part 1]] , I read about Reliability. The next section is Scalability . First let me talk about the assumptions I made about the term Scalability, what I understand from it prior to reading Martin Kleppman’s thoughts on the topic -

What is a scalable system?

My thoughts prior to reading the section -

Scale refers to how does a system grow. Growth can be needed for multiple reasons -

Reading DDIA - Part 1

Mon, 23 Sep 2024 00:00:00 +0000

(Here’s the preface in case you missed it - [[Reading DDIA - Preface]])

Chapter 1 - Reliable, Scalable and Maintainable Applications

This chapter’s goal is to effectively give us a high level overview of what this book is fundamentally about -

Thinking about data systems along the axes of :

Reliability
Scalability
Maintainability

What is a reliable system?

My thoughts before reading the section :

A system that is available to return results/outputs that are expected of it when it is employed.
If I’m accessing an API, if the API is available 99 out of 100 times, then its reliability is 99%
If a server is available for 23 hrs 45 minutes per day then its availability is 23.75/24 = 98.9%
But is availability == reliability? Let’s see.
A reliable human is someone who does something that he says he will do or is expected to do every time. Similarly a reliable system is one that returns the expected results every time that they are used.
An ATM is reliable if I can withdraw money from it in the denominations that I want, when I want it. For example, the ATM in Puttenahalli , JP Nagar is perpetually unavailable. Everytime I go there, the system is under maintenance. It is 0% reliable.

Let’s read the chapter.

Reading DDIA - Preface

Sun, 22 Sep 2024 00:00:00 +0000

Over the next few weeks, I want to read a book that has been long-pending - “Designing Data Intensive Applications” by Martin Kleppman. I’ve been building data applications for a while now but most, if not all the learnings, have been through trial and error after error after error. I’ve learnt a decent bit from observing more senior engineers, architects, design documents and building architectures little by little on my own, but not yet I think, in a structured, deliberate manner. I’m hoping to learn that in this book along with the other books I will likely have to refer along the way.

The Basics - 1

Tue, 10 Sep 2024 00:00:00 +0000

What is a package in Python?

Hold up, isn’t this one of those terms we use synonymously with other terms like library, module etc?

Well no. Let’s learn what the differences are -

Module

A module can be any file with a .py extension. For example -

`hero.py`

1def hero():
2 return f"Anyone"
3
4print(hero())

This module when executed using python hero.py returns the string “Anyone”. Because a hero can be Anyone.

Getting topics of Interest from HN

Mon, 26 Aug 2024 00:00:00 +0000

Building a Classifier

Fetching stories from Hacker News
Extracting key information from articles
Using AI to determine article topics

Code -

{% gist 5181b0e3c9957617598d4b446609ba01 hn_scraper_llm_topics.py %}

{% gist d6a97bbd3534c532d148ea8a1efc851b strings.py %}

{% gist d2fc27b41a17497466621b7e25c924fa data_models.py %}

{% gist 3bb526888e592de66c82eba391dd08ea keys.py %}

Blogs to read

Thu, 01 Aug 2024 00:00:00 +0000

1st Aug 2024

Postgres (post 1)

Thu, 01 Aug 2024 00:00:00 +0000

Installing Postgres locally and connecting to it from Python

First of all, what is Postgres and why do I care?

Over the course of our working with data, we come across a lot of ways of storing data - cache, in-mem, files, file based databases, SQL databases, NoSQL databases etc. Each option has a specific set of usecases that are best satisfied by said storage mechanism.

Postgres DB is one such storage mechanism - specifically an Open Source, Relational Database.

Concurrency in Python

Sun, 21 Apr 2024 00:00:00 +0000

These next set of posts are going to be about Concurrency and Async in Python. So, one non-tech thing about me is that I run a bookclub that meets once a month. In this bookclub, we discuss books we’ve read that month and at the end of the meet, we have a nice list of books that each person has mentioned and/or discussed. After I get back home, I typically try and get the Goodreads link for each book discussed and create a little more comprehensive list for people to refer to later on.

LC Num 21

Mon, 15 Apr 2024 00:00:00 +0000

21. Merge Two Sorted Lists

First I didn’t understand the problem I think. In the sense that I accepted the inputs as two lists and returned a list - Python list. Then I read it and realized they want the operation in Linked lists.

i took some time to recall how to move along a list and finally figured it out.

The other thing that gave me a minor ankle break was when the test case was two empty lists.

Design Patterns in Python - 2

Fri, 15 Mar 2024 00:00:00 +0000

Creational Patterns - The Factory Method

In the previous article - [[Design Patterns in Python - 1]] , I wrote about the simplest creational pattern - The Singleton. In this edition, we look at another way to create objects - the Factory method.

Again, we start with examining the meaning of the word and why it would have been used for the behavior to follow. A factory in the common usage of the word is a place where products are manufactured, where the factory is the entity doing the manufacturing.

Design Patterns in Python - 1

Thu, 14 Mar 2024 00:00:00 +0000

If you’ve been using Python in production for a while like I have, you’ve either intentionally or inadvertently used some design patterns in your code. But what are they anyway?

Design Patterns are well, patterns or reusable structures of code (templates) - that can be employed to implement solutions to software problems.

These patterns have been bucketed into 3 main categories :

Creational Patterns
Structural Patterns
Behavioural Patterns

On what basis though? What do each of these words mean in a programmatic sense? I will deal with each of the meanings as we come across patterns that fall under them.

Using Decorators

Wed, 08 Mar 2023 00:00:00 +0000

In Python, a decorator is a way to modify or extend the behavior of a function or class without changing its source code. Decorators are a powerful tool for adding functionality to existing code, and they are especially useful when you want to apply the same behavior to multiple functions or classes.

How Decorators Work

In Python, functions are first-class objects, which means they can be assigned to variables, passed as arguments to other functions, and returned as values from other functions. This makes it possible to define a decorator function that takes another function as an argument, modifies its behavior in some way, and then returns the modified function.

About 3.10

Thu, 23 Sep 2021 00:00:00 +0000

Forget the intro posts.

We’re diving in.

I’ve wanted to check out the latest features of a Python release without it being in the middle of an explorative search to optimize your work code or make it more readable for a long time now. And I thought this initial post was as good a place as any to fulfil that long-time desire. So today I’ll be looking into what’s new in Python 3.10. I don’t know what the future posts will hold. We’ll see.