DEV Community: K Yadav

I Built a Vector Database Project from Scratch — Here’s What Actually Happened

K Yadav — Tue, 21 Apr 2026 13:06:03 +0000

A few weeks ago, I decided to stop just reading about vector databases and actually build something with them.

Not a tutorial clone. Not a copy-paste project.

Something messy, slightly broken, and real.

This is a write-up of what I built, what I learned the hard way, and what I would not do again.

Why I Started This

Everywhere I looked, people were talking about embeddings, semantic search, and AI-powered retrieval.

But most tutorials felt shallow. They showed how to use a library, not why things work or what breaks when you go off-script.

So I set a simple goal:

Build a small vector database project that actually solves a problem.

The Project Idea

I built a semantic search engine for personal notes.

Instead of keyword search, I wanted to search like this:

“notes about scaling backend”
“ideas I wrote about startups”
“that thing about caching I wrote last week”

Even if those exact words weren’t in the note.

The Basic Setup

Here’s what I used:

Python
An embedding model (I started with OpenAI embeddings, later tried local ones)
A vector store (initially FAISS)
A simple API layer

The flow looked like this:

Take a note
Convert it into an embedding (vector)
Store it in the database
When searching:

Convert query to embedding
Find closest vectors
Return matching notes

Simple in theory.

Not so simple in practice.

What I Learned (The Real Stuff)

1. Embeddings Are Not Magic

At first, I thought:

“Same meaning = always similar vectors”

Not true.

Small wording differences sometimes gave weird results.

Example:

“How to scale a backend” worked well
“Handling traffic spikes” returned unrelated notes

Lesson:
You don’t just rely on embeddings. You sometimes need:

better chunking
metadata
re-ranking

2. Chunking Matters More Than I Expected

Initially, I stored full notes as single vectors.

Bad idea.

Long notes diluted meaning.

When I switched to smaller chunks (like 200–500 words), search improved a lot.

But then a new problem appeared:

Too many chunks = noisy results

So there’s a balance:

Too big → vague results
Too small → fragmented context

3. Vector Search Alone Is Not Enough

I assumed nearest neighbor search would solve everything.

It didn’t.

Sometimes results were “technically similar” but not useful.

What helped:

adding metadata filters
sorting by both similarity and recency
sometimes even mixing keyword search

4. Performance Sneaks Up on You

At the beginning, everything felt fast.

Then I added more data.

Search slowed down. Memory usage increased.

Things I learned:

Index type matters (FAISS has options, and they behave very differently)
You don’t notice problems until you scale even a little
Testing with 50 records is meaningless

5. Local vs API Embeddings Is a Tradeoff

I tried both:

API-based embeddings

Easy
High quality
Costs money

Local models

Free after setup
Slower (on my machine)
Sometimes lower quality

There’s no “best” option. It depends on:

your budget
latency requirements
privacy needs

Mistakes I Made (So You Don’t Have To)

Treating It Like a Normal Database

This is not SQL.

You don’t query exact matches. You deal with probabilities.

That mindset shift takes time.

Ignoring Evaluation

At first, I just “felt” like results were good.

That’s dangerous.

You need test queries like:

expected input → expected output

Otherwise you’re just guessing.

Overengineering Too Early

I wasted time trying:

fancy pipelines
multiple models
complex ranking

When a simple setup worked fine.

Not Logging Results

I didn’t log queries at first.

Big mistake.

Logs help you understand:

what users search
where results fail
patterns you didn’t expect

What I’d Do Differently

If I started again, I’d:

Start with a very small dataset
Add proper evaluation early
Keep chunking simple
Avoid premature optimization
Focus on real use cases, not benchmarks

Final Thoughts

Building this project changed how I think about search systems.

Vector databases are powerful, but they’re not plug-and-play magic.

You still need:

good data
thoughtful design
constant iteration

If you’re planning to build something similar, my advice is simple:

Don’t try to be perfect. Try to be real.

Build something small. Break it. Fix it. Repeat.

That’s where the actual learning happens.

Read in Detail how Vector Database Works:

Understanding Vector Databases for LLM Applications: A Deep Dive

Unlock LLM power with vector databases. This deep guide covers their mechanics, critical role, and impact on modern AI applications for enhanced understandin...

analyticsdrive.tech

How "Vibe Coding" Turned Me from a Noob into a System Designer

K Yadav — Sun, 05 Apr 2026 15:19:20 +0000

A year ago, if you asked me to explain the difference between a load balancer and a reverse proxy, I would have probably blinked twice and changed the subject. I was a "noob" in the truest sense—I had ideas for apps, but the wall between "idea" and "production" felt like it was made of solid lead.

Then, the era of Vibe Coding arrived.

You’ve probably seen the term trending. Some call it "low-effort" coding; others call it the future of software engineering. For me, vibe coding was the bridge that turned me from someone who struggled to syntax-highlight a for loop into someone who understands how to architect entire systems.

What is Vibe Coding, Anyway?

Vibe coding is the act of using Large Language Models (LLMs) to handle the heavy lifting of syntax and boilerplate while the human focuses on the intent, flow, and architecture. You aren't "writing" code in the traditional, character-by-character sense. You are describing a "vibe"—a set of behaviors and requirements—and iterating rapidly with the AI.

At first, I used it to make simple things: a basic Flask app, a React counter, a generic landing page. But as I leaned into the "trial and error" process, something strange happened. I stopped worrying about where the semicolons went and started worrying about how the data moved.

The Turning Point: When the Vibe Met the System

The transition happened when I tried to build my first "real" project: a farming assistance app called HarvestHub.

Suddenly, the "vibe" wasn't enough. I needed to handle image uploads for crop disease detection, manage user authentication, and ensure the database didn't collapse under its own weight.

Phase 1: The AI-Driven Chaos

In the beginning, I was just pasting prompts: "Make me a button that uploads an image to a server." It worked. But then I needed a way to store that image. The AI suggested an S3 bucket. Then I needed a database to link the image to a user. The AI suggested MongoDB.

I was hitting "trial and error" every five minutes. Every time the code broke, I had to explain the context to the AI. This is where the magic happened. To fix the "vibe," I had to understand the system.

How Trial and Error Taught Me Architecture

You can’t "vibe" your way through a 500 Internal Server Error without eventually learning what's happening under the hood. Through constant iteration, I learned three pillars of system design:

1. State Management and Flow

I realized that if my React frontend didn't know what my Flask backend was doing, the "vibe" felt broken. I started learning about REST APIs and State Management. I wasn't just coding a feature; I was designing a contract between two different systems.

2. Database Schema (The Hard Way)

My early attempts involved dumping everything into one table. When the AI tried to help me fetch complex data, the queries became slow and messy. I had to learn about Normalization and Aggregation Pipelines in MongoDB. I wasn't just "vibe coding" anymore; I was optimizing data structures to ensure the system stayed performant.

3. Scaling and Reliability

When my app "HarvestHub" grew, I couldn't just keep adding code to one file. I had to learn about Modularization and SDLC (Software Development Life Cycle). I started asking the AI: "How do I separate my concerns so this doesn't break when I add a new feature?"

Scaling Up: Building a Dynamic Content Platform

As my mindset shifted from writing isolated scripts to building scalable systems, I wanted a place to handle dynamic content, rapid updates, and real traffic. This led to the launch of Analytics Drive, my tech and news blog.

Building a platform that serves breaking global news, AI trends, and technical tutorials (like deep dives on Trie Data Structures) isn't just about rendering HTML. It became a masterclass in content architecture.

Setting up the blog brought a whole new set of system challenges. I had to focus on SEO analysis, web vitals, caching strategies, and optimizing read queries so articles would load instantly. Once again, I used AI to generate the boilerplate and "vibe out" the UI, but I was the one dictating the underlying architecture of how the site would be crawled, indexed, and delivered to users efficiently.

Analytics Drive

Analytics Drive is your destination for breaking news, in-depth tech tutorials, and expert analysis on AI and Geopolitics. Stay informed with investigations and opinions from the front lines of innovation.

analyticsdrive.tech

Building Tools: The In-Browser Compiler

The true mark of transitioning from a "noob" to a systems thinker is when you start building tools to solve your own workflow problems. While documenting my journey on Analytics Drive, I frequently needed a way to rapidly prototype and test frontend snippets without spinning up a full local environment.

My "vibe coding" journey relied heavily on speed, so I decided to engineer my own solution: a Free Online HTML CSS JS Compiler & Editor.

This was an entirely different architectural beast. It wasn't just a static webpage; it was a complex, client-side state machine. Building this platform forced me to learn about:

Sandboxed Execution: Safely running user-generated JavaScript inside an iframe to create a real-time live preview.
Browser Storage Architecture: Utilizing localStorage so the editor auto-saves code without requiring a backend database or user logins.
Responsive UI/UX: Designing a split-pane interface that seamlessly converts to a vertical stack for mobile developers.

Creating a CodePen alternative from scratch showed me that system design isn't exclusively about backend servers. It is equally about architecting complex client-side applications that run instantly and flawlessly in the browser.

Online HTML CSS JS Compiler & Editor — Free, Live Preview | HTMLCSSJSEditor

Free online HTML compiler and editor. Write and run HTML, CSS, JS instantly in your browser. No sign-up required. Works on mobile.

htmleditor.analyticsdrive.tech

From "How" to "Why"

The biggest change in my journey from noob to designer was moving from the question "How do I write this?" to "Why should I build it this way?"

Vibe coding acts as a high-speed simulator. In a traditional setting, it might take a week to build a broken system, realize it's wrong, and tear it down. With AI, I can build a "vibe" of a system in an hour, see where it fails, and re-architect it immediately.

Trial and error is the best teacher, and AI is the fastest treadmill.

The Reality Check: Is Vibe Coding Enough?

Let’s be honest: Vibe coding alone won’t make you a Senior Staff Engineer at Google. If you don't eventually learn the "boring" stuff—Data Structures, Algorithms, and System Design patterns—you will eventually hit a ceiling where the AI can't help you because you can't describe the problem.

But for me, it was the perfect "gateway drug." It removed the frustration of syntax and allowed me to fall in love with the logic.

My Current Stack & Mindset:

Frontend: React Native (because the vibe of "build once, run anywhere" is unmatched).
Backend: Flask/Python (fast, readable, AI-friendly).
Database: MongoDB (flexible enough to change as my design evolves).
The Mindset: Use AI to generate the bricks, but I am the architect drawing the blueprint.

Conclusion: Embrace the Vibe

If you are a "noob" looking at a blank VS Code screen, don't be afraid to vibe code. Start with a prompt, break things, and then ask the AI why it broke.

I didn't become a system designer by reading a 500-page textbook on day one. I became one because I was tired of my "vibes" crashing, so I forced myself to learn how to build foundations that actually hold up.

Coding isn't just about syntax anymore; it's about systems thinking. And there has never been a better time to start building.

Are you using AI to learn architecture, or just to copy-paste? Let’s chat in the comments!

I Got Tired of Bloated Workflows, So I Built a Lightning-Fast HTML/CSS Editor ⚡

K Yadav — Mon, 30 Mar 2026 19:39:59 +0000

Juggling university coursework, diving deep into data structures, and building full-stack applications usually means my browser is drowning in tabs. Between spinning up local environments for React apps or configuring backend routing, I constantly found myself hitting a frustrating bottleneck: testing simple UI components took way too long.

Sometimes you don't need a heavy IDE or a complex local setup. Sometimes, you just need a blank canvas to quickly test a CSS animation, verify a layout trick, or sketch out a raw HTML component before migrating it into your main project.

I wanted something instant. No logins, no heavy loading screens, just code and result. So, I decided to build one.

Enter the Analytics Drive HTML/CSS Editor.

Online HTML CSS JS Compiler & Editor — Free, Live Preview | HTMLCSSJSEditor

Free online HTML compiler and editor. Write and run HTML, CSS, JS instantly in your browser. No sign-up required. Works on mobile.

htmleditor.analyticsdrive.tech

Why build another code editor?

There are definitely other online editors out there, but many have become feature-heavy, pushing premium tiers or requiring accounts just to save a snippet. As a developer, my focus is always on efficiency and clean system design. I wanted a tool that reflected that philosophy—a lightweight, distraction-free environment that gets out of your way.

Here is what I focused on when putting this together:

Zero Friction: You open the link, and you start typing. That’s it.
Real-Time Feedback: The preview updates instantly as you tweak your HTML structure or adjust your CSS properties. It’s perfect for dialing in those pixel-perfect adjustments.
Minimalist Interface: No cluttered sidebars or unnecessary pop-ups. It's designed strictly for focus.

How it fits into a daily workflow

If you are learning web development, building out personal blog platforms, or just experimenting with UI designs, this tool acts as a perfect scratchpad.

I frequently use it to isolate problematic CSS. If a flexbox layout is breaking in a larger web app, debugging it within a massive codebase can be a headache. Copying just the relevant HTML and CSS into a clean environment like this editor makes it incredibly easy to pinpoint the issue. Once it looks right, I just paste the corrected code back into my main project.

Try it out 🛠️

If you appreciate lean, fast developer tools, I'd love for you to give it a spin. Whether you are quickly scaffolding a landing page concept or just trying to remember how CSS Grid works for the hundredth time, it's there to make the process smoother.

👉 Check out the HTML/CSS Editor here

I’m continuously looking to improve the tools I build, so if you have any feedback on the UI or functionality, drop a comment below. What’s your go-to method for testing quick front-end snippets? Let's discuss!

Level Up Your Life: The Modern, Tech-Savvy Guide to Health, Wealth, and a Greener Home

K Yadav — Mon, 30 Mar 2026 18:22:48 +0000

Let’s be honest: balancing a healthy body, a thriving environment, and a growing bank account while trying to maintain your open-source contributions can sometimes feel like debugging legacy code blindfolded. We are bombarded daily with a million different "life hacks," leaving us more overwhelmed than inspired.

Online HTML CSS JS Compiler & Editor — Free, Live Preview | HTMLCSSJSEditor

Free online HTML compiler and editor. Write and run HTML, CSS, JS instantly in your browser. No sign-up required. Works on mobile.

htmleditor.analyticsdrive.tech

But what if upgrading your lifestyle didn't require a total rewrite of your codebase? What if, instead, it was about making smart, data-driven, and systematic tweaks to your daily runtime?

Welcome to the modern approach to lifestyle architecture. Today, we aren't just relying on willpower; we are leveraging tech-savvy strategies, scientific insights, and smart routines to build a life that feels genuinely good. Let's break down the ultimate blueprint for optimizing your health, your home environment, and your wealth.

Part 1: Optimizing the Machine (Health & Fitness)

You cannot build sustainable software if the hardware—your body—is overheating and running on fumes. Forget the crash diets and the expensive, intimidating gym memberships. The future of wellness is personal, accessible, and rooted in science.

The Power of Plants and Heart Health

It is no secret that what we put into our system directly impacts our longevity. But moving past the hype, the actual data surrounding plant-centric eating is undeniable. Transitioning to a plant-based diet doesn't mean you have to eat salads for every meal; it means strategically fueling your body to lower the risk of cardiovascular disease. If you want to understand the actual science behind this shift, take a few minutes to read about Unlocking Heart Health: Benefits of Plant-Based Diets.

Data-Driven Weight Loss

If your goal is to shed a few pounds, the "eat less, move more" advice is the equivalent of "have you tried turning it off and on again?" It's too vague. We need metrics. We need a plan. By tracking your macros and understanding your basal metabolic rate, you can turn weight loss from a guessing game into a predictable algorithm. Ready to ditch the fad diets? Learn exactly How to Create a Balanced Meal Plan for Weight Loss: A Data-Driven Guide.

Fitness on Your Terms (and Budget)

You don’t need a $200-a-month boutique gym membership to get into the best shape of your life. Your living room or home office is the new fitness studio. With a little creativity and a few basic pieces of gear, you can build a highly effective routine. Find out How to Start a Budget-Friendly Home Workout Plan Today without breaking the bank.

💡 Key Takeaway: Treat your health like a data project. Small, trackable changes in your diet and exercise routines yield massive, compounding results over time.

Part 2: Configuring Your Local Environment (The Tech-Savvy Green Thumb)

Your local environment dictates your output. A sterile, lifeless room drains energy, while a space filled with vibrant greenery boosts creativity and reduces stress. But keeping plants alive can be frustrating if you lack the documentation. Fortunately, bringing nature indoors has gotten a massive technological upgrade.

Mastering Indoor Houseplants

Tired of watching your expensive ferns throw exceptions and turn brown? Indoor plant care is less about having a "green thumb" and more about understanding environmental controls like humidity, light cycles, and nutrient science. Stop guessing when to water and start mastering Simple Tips for Maintaining Indoor House Plants: A Tech-Savvy Guide.

Growing Your Own Food: The Herb Garden

There is nothing quite like cooking a meal with basil or mint you grew yourself. Space is no longer an excuse. With modern hydroponics, smart lighting, and automated watering systems (hello, Arduino projects!), anyone can grow a flourishing garden on their kitchen counter. If you are a beginner, the best place to start is by learning How to Start a Sustainable Indoor Herb Garden: A Tech Explainer.

Scaling Up to Indoor Vegetables

Once you’ve mastered herbs, it’s time to scale up the infrastructure. Imagine harvesting fresh tomatoes, peppers, and leafy greens from your apartment in the dead of winter. By utilizing LED grow lights and IoT (Internet of Things) sensors, year-round fresh produce is entirely possible. Dive into the mechanics of How to Create a Sustainable Indoor Vegetable Garden and revolutionize your grocery list.

💡 Key Takeaway: You don't need a backyard to be a gardener. Modern technology allows you to bring a sustainable, thriving ecosystem right into your living room.

Part 3: Managing Background Processes & Scaling (Productivity & Wealth)

A healthy body and a beautiful home are fantastic, but financial stress and mental clutter can quickly derail your peace of mind. To truly upgrade your lifestyle, you must take control of your time and your income streams.

Winning the Morning

How you spend the first hour of your day sets the execution flow for the remaining twenty-three. If you start your morning reacting to emails and scrolling through social media, you're already caught in an infinite loop of distractions. A structured morning routine is the ultimate life hack for peak performance. Discover the systems highly successful people use and learn How to Build a Morning Routine for Productivity: A Tech-Savvy Guide.

Building Digital Income

We live in an era of unprecedented opportunity. The traditional 9-to-5 is no longer the only path to financial security. Whether you are looking to build a side hustle to pay for your new IoT gardening hobby, or you want to completely replace your full-time income, the internet is your greatest asset. From freelance development to digital marketing and building SaaS products, the blueprints for success are out there. Ready to take control of your financial future? Read How to Make Money Online: Your Ultimate Guide to Digital Income.

The Bottom Line

Designing your ideal lifestyle isn't about perfection; it’s about Agile iteration. It’s about replacing bad habits with smart, tech-driven systems.

Refactor your inputs: Optimize what you eat and how you move.
Upgrade your environment: Breathe life into your living space with smart, sustainable indoor gardens.
Optimize your schedule: Protect your time with a rock-solid morning routine, and leverage the digital economy to build financial resilience.

You have the tools, the data, and the documentation right at your fingertips. The only question left is: which area of your life are you going to deploy an upgrade to first?