DEV Community: Yuvaraj Kannan

Build a Developer Portfolio for Free in 1 Minute

Yuvaraj Kannan — Fri, 06 Mar 2026 20:41:41 +0000

Every developer eventually reaches a point where they hear:

“You need a portfolio.”

But building one usually takes hours:

designing the UI
deploying the website
writing project descriptions
optimizing it for recruiters

Many developers delay it for months.

So I wanted a simpler way to generate a portfolio quickly.

Why Every Developer Needs a Portfolio

A resume tells recruiters what you say you know.

A portfolio shows what you have actually built.

When hiring developers, recruiters usually check:

GitHub
portfolio website
live projects

A portfolio helps you showcase your work in a single place.

This is especially important for:

students
self-taught developers
early-career engineers

The Problem With Most Portfolio Builders

Most portfolio tools fall into one of these problems.

Too complex

They require design skills or heavy customization.

Too expensive

Some builders require subscriptions or paid templates.

Too time-consuming

You spend hours setting up something that should be simple.

A Simpler Approach

I built a small tool where developers can generate a portfolio in about a minute.

You just enter:

your name
bio
tech stack
projects
GitHub / LinkedIn

Then the system generates a clean developer portfolio automatically.

You can try it here:

👉 https://preocr.io/portfolios

Example Portfolio

Here is an example portfolio generated using the tool:

👉 https://preocr.io/u/yuvaraj-kannan

It includes sections like:

introduction
projects
tech stack
contact information

This makes it easy for recruiters to quickly understand:

what you build
which technologies you use
how to reach you

What a Good Developer Portfolio Should Include

A good portfolio is usually simple.

Typical structure:

Home
About
Projects
Skills
Contact

Projects

Projects are the most important part.

Include:

what problem you solved
technologies used
GitHub link

Tech Stack

List technologies you actually work with.

Example:

Node.js
Python
React
PostgreSQL
Docker

Contact

Always include:

GitHub
LinkedIn
email

So recruiters can reach you easily.

Final Thoughts

Building a portfolio doesn’t have to be complicated.

Sometimes a simple and clean portfolio is more effective than a heavily designed one.

If you don’t have a portfolio yet, you can generate one quickly here:

👉 https://preocr.io/portfolios

It takes about one minute.

If you’re a developer reading this, feel free to share your portfolio in the comments 👇

OCR for Handwritten Text in 2026 – What Developers Should Know

Yuvaraj Kannan — Sun, 22 Feb 2026 20:44:20 +0000

Handwritten OCR is still one of the toughest problems in document processing.

Printed text? Easy.

Handwriting? Chaos.

Different writing styles, slanted letters, messy scans, mixed printed + handwritten PDFs — all of this makes extraction hard.

If you're building OCR for handwritten text, here’s what actually matters in 2026.

🔍 The Real Problem Isn’t Just Accuracy

Most developers focus only on:

Which OCR model is more accurate?
Which API handles cursive better?
Which tool supports more languages?

But there’s a bigger issue:

👉 Many pipelines run OCR blindly on every document.

Even when:

The PDF already contains digital text
Only a few pages are scanned
OCR is not required at all

That’s wasted compute.

That’s higher cost.

That’s slower pipelines.

🧠 Modern OCR Tools Compared

Some popular options today:

Google Cloud Vision OCR – High accuracy, cloud-based
DeepSeek OCR – AI-native multimodal reasoning
Tesseract OCR – Open-source classic
MinerU – Strong structured document parsing

Each has trade-offs in:

Cost
Compute
Accuracy
Scalability

But choosing the OCR engine alone isn’t enough.

⚙️ Smarter Architecture for Handwritten OCR

The more scalable approach looks like this:
Document
↓
Detection Layer (Is OCR Needed?)
↓
Handwritten OCR Engine
↓
Post Processing
↓
Structured Output

Instead of brute-forcing OCR on everything, you:

Detect scanned vs digital PDFs
Run OCR only when required
Route intelligently
Optimize compute

This architecture reduces cost significantly in production systems.

📚 Full Deep Dive (Comparison + Architecture)

I wrote a detailed breakdown here:

👉 https://preocr.io/blog/ocr-for-handwritten-text-in-2026

In that post, I cover:

Tool-by-tool comparison
Cost vs accuracy tradeoffs
Pipeline architecture design
Practical considerations for Python developers

If you’re building document AI systems, it’ll save you from common mistakes.

🚀 Final Thought

In 2026, handwritten OCR isn’t about “which model is best.”

It’s about:

Intelligent routing
Pipeline optimization
Cost efficiency
Production scalability

The winners won’t just have better OCR.

They’ll have smarter document pipelines.

Reducing OCR Cost in RAG Pipelines with Page-Level Detection

Yuvaraj Kannan — Wed, 11 Feb 2026 19:43:42 +0000

When building Retrieval-Augmented Generation (RAG) systems, most people focus on embeddings and vector databases.

But one major hidden cost lives earlier in the pipeline: OCR processing.

Many ingestion pipelines blindly run OCR on every page of every document. That’s inefficient — especially when many pages already contain native, machine-readable text.

The Smarter Approach

Instead of applying OCR everywhere, evaluate each page first:

Does it already contain digital text?
How much of the page is image-based?
Is the layout complex (tables, forms, structured content)?

With page-level, layout-aware detection, you only run OCR where necessary.

Example

decision_result = preocr.needs_ocr(
    document,
    page_level=True,
    layout_aware=True
)

for page_info in decision_result["pages"]:
    if page_info["needs_ocr"]:
        text = run_ocr(page_info)
    else:
        text = extract_native_text(page_info)

This approach can reduce OCR calls by 30–60% in mixed documents.

Why It Matters

Selective OCR means:

Lower cloud costs
Faster ingestion
Cleaner embeddings
Better retrieval accuracy

If you want a full breakdown of the architecture, diagrams, and optimization strategy, I wrote a detailed guide here:

👉 https://preocr.io/blog/how-to-reduce-ocr-cost-in-rag-pipelines

Optimizing RAG isn’t just about better models — it starts with smarter document ingestion.