DEV Community: Adwaith Jayasankar

A Universe of Triangles.

Adwaith Jayasankar — Sun, 16 Nov 2025 07:09:28 +0000

I love video games... even though it's not that apparent. Recently I've been looking into the making of the Godfather of all first-person shooters, the original DOOM (1993). I'll be honest the creator of DOOM, John Carmack is one name I wasn't familiar with but after I looked into his works? It's kind of weird how he's not a household name yet. I'm pretty sure he is in the world of programmers though. The actual reason I'm looking into all this is because I wanted to learn how video games work, mostly the image rendering aspect. The more I looked into it, I realized how much I'm lacking in basics and what better way to learn the basics than go through Computerphile's Graphic Series with John Chapman?

So, this article is going to be the first of a 5 part series where I break down each video of the playlist, my iteration of trying to break it down simply as John Chapman did for me.

You're saying the matrix stuff I learned in high school is important?

Just a heads up, for the people who already know about matrixes and dread it (like me) I promise you I'm going to make this interesting. For those who don't, prepare to get your mind blown :)

First question, how do we take a 3D object and represent it in the computer?

We use something known as a Coordinate System (Specifically Cartesian Coordinate System). It involves an x-axis and a y-axis, where you can plot points with respect to the origin. The points are separated by a unit of size. So two dimensionally, you can use this system to represent the location of points as shown in the image above.

What about three dimensions? Well one more axis comes into the play, called the z-axis. If you have the power to visualize, the above image is how plotting points three dimensionally might look like.

Yay! Now you know how a Coordinate System works. Now let's jump into binary space partitioning (BSP) which is a method for space partitioning which recursively subdivides a Euclidean space into two convex sets by using hyperplanes as partitions.

Just kidding, don't stop reading yet. Let's now try to represent a simple 3D object, let's say a pyramid (pretty confident you all know what that is) in a coordinate system.

As shown in the image, a pyramid has 5 vertices. However, all we're doing right now is specifying the corners and our representation tells us nothing about the faces of the pyramid.

So how do we represent the faces?

Well one way to go about it is have three vertices per face of the pyramid. Since a pyramid has five faces (including the bottom) that's 5 times 3 equals 15 vertices overall. But... don't you think that's a lot of duplication for the vertices?

For a pyramid it's not a big deal. I get it. What about a 3D object that has hundreds and thousands of points? The duplication depending on the shape can be insanely huge which none of us wants. Bleh.

If you look at the pyramid in the image, we have named each and every point with an alphabet. Using that, I can say one of the faces has points ABC. Where each alphabet represents that point. Similarly, another face can have ADC, AEB and so on and so forth. Congrats, we successfully represented the faces of a pyramid without using 15 individual points!

Wait! We're not done yet. What about the bottom face? The bottom face of a pyramid is a square. So according to the image we can represent it as a square and make it have 4 vertices. In this case it'll be EDCB. So, problem solved? Umm...well yes, but no.

What do you mean by 'yes, but no?'

A better approach is to divide the rectangular base into two triangles, as shown in the image. Why triangles? This can get a little trippy so bear with me XD

The best option for situations like these is to always divide these objects into triangles. In the words of John Chapman,

"Any 3D surface in the universe is able to be approximated by fitting together triangles."

But why though? Why triangles?

It's the simplest polygon. There are only 3 vertices. No brainer.
Triangles are easy and fast for GPUs to process. They are more or less the native language of GPUs.
Most importantly, any arrangement of the three vertices in a 3D space will always be coplanar. Don't worry, I'll explain.

What does coplanar even mean?

Imagine a triangular piece of rigid cardboard. Hold two corners down and try to lift it from the third one. Does the triangle bend? I don't think so.

Now try the same with a rectangular piece of cardboard. Hold down 3 points, try lifting from the fourth one. I don't know about you, but I'm pretty sure you can't do that without bending the cardboard.

That's all what coplanar means. In the case of a triangle, all three points must lie on one plane. It can rotate or tilt, but it must never bend.

What's crazy is that every shape can be divided into triangles! Have a look at the example given below on how a cylinder is divided into triangles.

The more triangles we use, the smoother the edges become.

Before I 'wind' (no pun intended) up this article, there's one more problem we need to address. How do we recognize the faces of an object? Heads up: the names we gave to each vertex comes into play here. Recognizing the faces of an object is a very important concept that comes into play for advanced concepts such as lighting and shadows on 3D objects. We normally call this process as 'winding'.

This was pretty fun. I almost forgot what it was like researching topics on your own and writing articles from scratch. Hopefully my motivation lasts for the next 4 articles in this series.

Thank you so much for reading guys! Have a look at the video I was talking about below:

Unstoppable Reddit Research Agent! – Real-Time Insights with n8n + Bright Data

Adwaith Jayasankar — Sat, 30 Aug 2025 10:11:33 +0000

This is a submission for the AI Agents Challenge powered by n8n and Bright Data

What I Built

Online communities like Reddit are a treasure trove of user concerns, struggles, and honest feedback. Every day, people share their unfiltered experiences—whether it’s about skincare routines, tech products, mental health, or niche hobbies. For businesses, researchers, or even curious individuals, this raw conversation is a window into what people actually think, feel, and need.

But the problem? These discussions are often chaotic, unstructured, and scattered across countless comments—making it overwhelming to extract actionable insights.

That’s where my project comes in.
I built an AI-powered workflow that:

Analyzes Reddit posts and comments in real time

Extracts the underlying concerns, themes, and trends

Automatically generates clear, structured reports inside Notion

In short, it transforms messy threads into digestible knowledge bases—perfect for:

Marketers → tracking sentiment, product pain points, emerging trends

Researchers → organizing authentic, large-scale community insights

Everyday users → turning Reddit rabbit holes into useful, structured knowledge

This workflow bridges the gap between community chatter and real understanding, making online discussions finally usable as living, actionable data.

Demo

n8n Workflow

Github Gist Link

Technical Implementation

This project leverages n8n’s modular workflow engine, Bright Data’s Verified Node, and Google Gemini to build a real-time Reddit insights agent that scrapes posts + comments, processes them, and delivers structured marketing insights into Notion.

Here’s a breakdown of the key nodes and how they fit into the workflow:

Schedule Trigger: Runs the workflow on a timed basis, ensuring continuous monitoring of subreddit activity.

Set Subreddit: Defines which subreddit to analyze (hardcoded or user-input).

RSS Read: Pulls the most recent posts from the chosen subreddit’s RSS feed.

Extract URLs → Aggregate URLs: Cleans and batches the post URLs to prepare them for Bright Data’s scraper.

Bright Data Nodes: (see next section for full detail) Used to initiate, monitor, and download data-rich snapshots (post details, comments, upvotes).

Loop Over Items & Control Flow: Ensures that batch extractions are retried until Bright Data reports readiness.

Data Preparation (Set & Aggregate): Extracts essential fields (title, description, comments, upvotes, etc.) and reduces them into a single object for LLM input.

AI Agent Node: Uses Google Gemini as the underlying model, with system prompts tailored to generate actionable marketing insights in structured JSON format.

Notion Integration:

Create a page → initializes a new Notion page for each insights report.

Md to Notion Blocks → converts AI output (Markdown) into Notion-compatible blocks.

Add blocks as Children → appends the AI-generated insights into the Notion workspace.

This combination allows the workflow to run end-to-end autonomously: from subreddit scraping → to AI-driven summarization → to real-time reporting in Notion.

Bright Data Verified Node

Bright Data’s Verified Node was the data backbone of this project. Here’s how it was used step-by-step:

Initiate Batch Extraction from URL

Operation: triggerCollectionByUrl

Input: A batched list of subreddit post URLs.

Output: A snapshot_id representing the batch extraction job.

Purpose: Kickstarts Bright Data’s web scraper to collect post metadata and comments.

Check the Status of Batch Extraction

Operation: monitorProgressSnapshot

Input: snapshot_id

Output: Status values (running, ready, etc.).

Purpose: Polls Bright Data until the dataset is marked as ready.

Control Flow: Loop & Condition

Loop Over Items + Wait 30 Seconds: Creates a polling mechanism.

If Check → Batch Ready: Ensures data is only downloaded when Bright Data confirms completion.

Download the Snapshot Content

Operation: downloadSnapshot

Input: snapshot_id

Output: Full structured dataset (title, description, comments, replies, votes, etc.).

Purpose: Provides the rich Reddit dataset that powers the AI analysis.

Post-processing (Extract Essential Data)

Fields captured: title, description, comments, upvotes, num_comments, url.

Structured into a clean JSON object for downstream AI consumption.

The Bright Data integration ensures that the AI isn’t relying on static or partial information—it has access to real-time, community-level conversations with full context, making the insights both fresh and actionable.

Journey

The biggest challenge I faced was converting the AI Agent Node’s output into properly formatted Notion documents.

I had to dive deep into the Notion API — learning about blocks, page IDs, and content structure — before I could get everything working smoothly.

This process gave me valuable hands-on experience connecting AI outputs with real productivity tools.

For the future, I plan to build a more detailed workflow and documentation so others can easily replicate and extend it.

Future Improvements

Multi-Subreddit Monitoring – Track discussions across multiple subreddits to uncover community-wide trends.
Notion Database Integration – Push insights directly into databases for better querying, filtering, and reporting (beyond just static pages).
Advanced Filtering – Add layers for sentiment analysis, product mentions, and brand signal detection to deliver sharper insights.
Cross-Platform Export – Support sending reports not only to Notion but also to Google Docs, Slack, Email, or other productivity tools.
Setup Guide for the Community – Publish a detailed tutorial and documentation so anyone can replicate or customize the workflow.
Towards a SaaS Product – Transform this into a user-facing platform where:
- Users can securely input their Bright Data key and Notion credentials.
- Select multiple subreddits (or future sources like Twitter/X, Discord, or forums).
- Choose preferred output channels (Notion, Google Docs, Slack, Email, etc.).
- View insights directly on the web app or integrate with their existing stack.

Vision Statement:
My long-term vision is to build a SaaS platform that democratizes community insights — so anyone, from solo creators to product teams, can tap into real-time online conversations and instantly turn them into structured, actionable knowledge without needing technical setup.

With n8n + Bright Data + AI, I’ve shown how to turn chaotic online conversations into structured, actionable intelligence — directly inside Notion 🔥.

Understanding evolution, through coding !

Adwaith Jayasankar — Fri, 01 Sep 2023 13:17:31 +0000

Recently I came through this crazy video showcasing how evolution works through the theory of natural selection. The video was made by David Miller, you can check out the full video through this link: https://youtu.be/N3tRFayqVtk?si=9DHAYaoSnmo6Z3Ed.

If you just want the revised version of the video, hope on!

What is evolution and natural selection?

Evolution is the gradual change in living organisms over time. It is based on natural selection which is nature's way of favoring traits that help organisms survive and have babies, making species better suited to their environment over time.

What is this video about?

So what David Miller did is that he created a 2 dimensional world where little creatures live that have brains (made up of neural networks) and have genes which the creatures are capable of passing onto the next generation.

He then set up the conditions necessary for evolution and wanted to see how the creatures would adapt to it.

The simulations which he showed were some of the most fascinating concepts I've seen in a long time and it honestly blew my mind away!

What are the conditions necessary for evolution?

1) Self - replication : This refers to just reproduction, as evolution cannot occur without it.

How does it work?
Each creature is simply a data structure that contains tiny bits of information such as:

Coord: Information regarding where it lives in the world.
Genome: Contains the information necessary to pass onto it's kids.
Neural Net: The brain of the creature.

Now if you just copy this data structure and memory, voila! You achieved self - replication.

2) Blueprint: In nature, every creature that is born, has to be made from some sort of a blueprint. This blueprint is referred to as genes, which consists of the information that is passed onto the next generation.

3) Inherit Blueprint: This blueprint that is used to make an individual, has to be inherited. Just like how babies inherit characteristics from their parents.

4) Mutation: Usually when we inherit genes from our parents, the information is copied a 100% accurately MOST of the times, but once in a while, we'll get a gene that has a slightly different information, leading to mutations.

5) Selection: For evolution to occur, there must be some sort of selection procedure on who gets to reproduce and who doesn't.
For humans, it involves escaping from predators, finding mates to reproduce, stay healthy and not catch any diseases etc. but for this simulation, our selection criteria is wayy simple!

SIMULATION 1 : HOW IT WORKS

Couple of points to note to understand the above simulation.

The world size is 128x128 pixels.
Generation Zero or Gen 0 has a population of 1000 creatures.
The tiny different colored dots are the creatures. The different colors showcases the variety in genes.
Steps/Gen refer to the amount of time the simulation will run, in this case about 10 seconds.
Genome length refers to the number of genes each creature has, which in this case is 4. Each genome is a bunch of hexadecimal letters that contain the instructions for how to make the connections in their little neural network brain, which is pictured below.

The selection criteria in this case is that the creatures that are present in the right side of the world gets to live, others do not.

Now let's see the simulation!

Gen 1

Gen 20

Gen 50

Gen 1200

So what can you understand from the above pictures? How were the creatures able to move on their own towards East, i.e the safe side? Let's discuss.

So the creatures that were already on the safe side by luck, got the chance to live and reproduce. But not all the creatures that survived are from the safe side.

See, all the creatures have their brain wired to move in a certain direction, say East, South, West and North. Some of the creature's brains were wired to move towards East, which resulted in them surviving. Now this gene of always moving East was handed down generation to generation, which lead to more creatures moving East and then surviving.

THE CRAZY FACT ABOUT ALL OF THIS IS THAT THESE CREATURES WERE NOT EXPLICITLY PROGRAMMED TO EVOLVE! They did it all on their own.

SIMULATION 2: ARE MUTATIONS NECESSARY FOR EVOLUTION TO TAKE PLACE?

Couple of points to note to understand the above simulation.

The parameters are all same as simulation 1, with a couple of changes.
The genome length is now 24, which means there are 24 genes leading to a smarter creature.
The mutation rate is 0.001, which allows the creatures to get a mutation at a random period of time.
Here the selection criteria is that the creatures that are able to get to the left and right sides of the world as pictured above gets to survive.

Now let's see the simulation!

Gen 0

Gen 25

Gen 100

Gen 700

NOTE: From Gen 700 onwards, some of the creatures get a mutation to move towards left, leading to a higher percentage of survival.

Gen 1000

Gen 8300

Now what happens when we put a couple of barriers? Let's see!

Gen 8301

Gen 9000

Gen 177,000

What do we understand from the above simulations?

Because of mutation the creatures were able to adapt to their environment, which lead to a better chance of survival. When the barriers were put up, they mutated to be able to move all the directions so that they could reach the safe zone.

What happens when there's no mutation? Let's see.

Gen 0

Gen 100

Gen 8000

Till now all seems good and normal right? What happens when we put up a couple of barriers?

Gen 8001

Gen 20,000

Gen 500,000

Gen 1,000,000

Since the mutation rate is 0, the creatures were not able to adapt and change their behavior according to their surroundings which lead to their survival percentage remaining constant.

This shows us that as a species how mutations are so important. Without mutations our species would not live long enough in the future to survive and eventually reach the stars.

SIMULATION 3: BRAIN SIZES

Do brain sizes affect evolution? How large does your brain has to be to ensure continuous survival of your species? Let's take some examples for comparison.

The average human being brain has 80 billion neurons and more than a 100 trillion connections between them.
The average cat brain has 7000 million neurons and 10 trillion connections between them.
The average housefly brain has about 250 thousand neurons and 10 million connections between them.
The brain of a nematode worm consists of 302 neurons and about 7000 connections between them.

The creatures in our simulation has about 20 neurons and 200 connections between them. Take that for comparison!

Some factors to note to understand the above simulation.

We will keep on changing the genome length, which increases or decreases the size of the brain accordingly.
The selection criteria in this simulation is that the creatures that are able to move towards the corners of the world get to live and reproduce.

Let's start the simulation!

Genome length: 2, Neurons: 1

Genome length: 8, Neurons: 2

Genome length: 32, Neurons: 5

Genome length: 1000, Neurons: 127

Let's compare the brain sizes of the creatures

Genome length: 2, Neurons: 1

Genome length: 8, Neurons: 2

Genome length: 32, Neurons: 5

Genome length: 1000, Neurons: 127

You can see here that, as the number of neurons and the connections increases the survival rate also increases exponentially.

SIMULATION 4: THE 'KILL' NEURON

So all this while, David Miller disabled the kill neuron. What it does is that it enables the creature to kill the neurons that are blocking it's path.

Here the orange creature 'kills' the violet creature because it is blocking it's path.

What happens when we enable this 'kill' neuron? Let's see!

Some factors to consider for the above simulation.

The selection criteria is that the creatures that are able to move towards the circle in the center get to live and reproduce.
There are about a 1000 creatures in this simulation.

Gen 0

Gen 200

Around this time, the creatures evolved and found a way to survive by keeping the kill count to a minimum.

Around Gen 700, a mutation occurred. A creature that killed other creatures got into the safe zone and started spreading it's 'killer' genes towards the next generation, which caused the generations after Gen 700 to be full of killers.

Gen 900

Gen 3400

Now what happens when we keep the number of creatures to a minimum, say 100?

We can see that there are zero kills. Since there was enough space for all the creatures to move around, killing the creatures suddenly dropped as an option!

CONCLUSION

This video created by David Miller was a fascinating insight into the world of programming and biology. It further cemented my love for science and it's capabilities. I'm currently trying to learn Machine Learning and AI, so if these are some of the things which we are capable of doing, the possibilities to create lifechanging discoveries are endless!

This article is not sponsored by David Miller, but please do consider watching his video and be a subscriber to his channel. Content like this must be supported at all costs!

Link to his video: https://youtu.be/N3tRFayqVtk?si=9DHAYaoSnmo6Z3Ed

If you've managed to read till here, I thank you from the bottom of my heart for the support. Makes me happy knowing my effort in writing this article contributed to someone learning something new :)

A library of infinite possibilities : The Library of Babel

Adwaith Jayasankar — Sat, 22 Jul 2023 21:24:52 +0000

What is The Library of Babel?

The Library of Babel website is an online project inspired by Jorge Luis Borges' fictional concept. It was created by Jonathan Basile and launched in 2015. The website attempts to simulate the experience of the Library of Babel as described in Borges' story. You can access the website at libraryofbabel.info.

This website contains lots of imaginary "books" made up of random letters and symbols. Each "book" has a unique address like a house with a room number. This website contains every possible combination of 1,312,000 characters, including lower case letters, space, comma, and period. Thus, it would contain every book that ever has been written, and every book that ever could be - including every play, every song, every scientific paper, every legal decision, every constitution, every piece of scripture, and so on.

At present it contains all possible pages of 3200 characters, about 10 to the power 5000 books. For comparison, it is estimated that there may be 10 to the power 80 atoms in the known universe._

You can use a search function on the website to look for specific texts or sentences. If you search for something, the website will try to find a "book" that matches your search.

What's so special about it?

Think of this website as a virtual library that represents all possible knowledge. It's like looking for a specific piece of information in a huge pile of random texts. The website is a fun way to explore the concept of a library with infinite possibilities. _I mean it would be crazy to think that one of the combination could contain the cure to cancer, or how the world turns out in the future and many more.
_

Just one of the many wonderful things on the internet!