DEV Community: Stephen BJ

ASGI and WSGI

Stephen BJ — Thu, 05 Feb 2026 05:44:03 +0000

HI all,
welcome to an other interesting topic about the servers in python

lore: while i was interviewed interviewer ask me a question why did you chose fastapi instead of django and flask. i was stuck for few min because what i did before i pick the framework just google + GPT's and get the best framework for my project. (actually they told me why it was the best option also). i didn't pick up django because it has very big learning curve but it provide lots of functionalities like ORM, middleware , auth etc., out of the box.

Now, will see why exactly the major and important thing on selection of Fast vs Flask is ASGI ** and **WSGI

ASGI provide the asynchronous feature which WSGI is not offers (best of my knowledge ) my be it is debatable

lets take a process like Make coffee and lemon juice:

steps like

get the milk , sugar , coffee powder
turn on the stove
warm the milk
add sugar and coffee powder
pore it in coup
get the lemon, sugar, water, ice cubes and salt
cut the lemon and squeeze into the coup
add sugar, water, ice cubes and salt
pore it in the coup

this is what the step will follow by the wsgi but in the case of asgi

get the milk , sugar , coffee powder
turn on the stove
warm the milk
get the lemon, sugar, water, ice cubes and salt
cut the lemon and squeeze into the coup
add sugar, water, ice cubes and salt
pore it in the coup
add sugar and coffee powder
pore it in coup

this is how the asgi will do
now we may have question like ok it do only one request can handle at the time direct answer is no. in this place service worker play's an major role for example we may have multiple person doing this . but the problem is kitten size if the kitten fit only for one person we can add multiple people in it

in similar way WSGI can have multiple workers but the workers limit based on the CPU size like Number of core X 2 that is the max

Conclusion

WSGI is not bad. it's just done evolving.
ASGI is what the modern internet expects.

In platform terms:
WSGI = stable, predictable, capped
ASGI = scalable, concurrent, future-proof

in my term based on the context it is mater if the process has move cpu related one and not request intensive both will give the same result

Paddle OCR-VL & DeepSeek-OCR

Stephen BJ — Tue, 21 Oct 2025 09:58:47 +0000

PaddleOCR-VL

What it is

PaddleOCR-VL is a vision-language model (VLM) built for document parsing-meaning not just "read the text"
but "understand the layout, tables, formulas, charts, multilingual text." it is supports 109 languages.

Why it stands out

Multilingual: covers a wide spectrum of scripts (English, French, german, etc.).
Document-layout aware: recognizes text blocks, tables, formulas, charts so it is designed for complex documents beyond simple printed text.
Resource efficiency: the documentation claims faster inference, lower memory consumption compared with many generic large VLMs.

Where to use it

Parsing scanned PDF documents where you need structure and multiple languages.
layout understanding is required (not just OCR, but "what is in the table?").

Limitations

although it supports many languages, real-world accuracy might vary wildly for less common scripts or poor quality scans.
as with any model: OCR + layout parsing is never "perfect" - expect error rates, need post-processing.

DeepSeek-OCR

What it is

Deepseek-OCR is a newer open-source model that targets OCR/document parsing—but with a twist:
they emphasize "vision-text compression" (how many vision tokens are needed to decode text tokens)
document throughput/efficiency.

Why it stands out

Code and weights appear publicly available (huggingFace).
Easy to set up.

Where it could excel

If you care about throughput—running large volumes of pages/documents, wanting to minimize compute per page.
Use-cases where you have relatively "standard" scanned documents and you want a streamlined/inexpensive pipeline.
Potentially situations where compressing document representations matters (archive, storage, indexing).

Limitations

Since it is newer: less maturity, fewer benchmarks publicly compared to paddleOCR-vl.
Compression focus may trade off accuracy in edge cases (complex layout, unusual fonts, low quality scans).
Documentation & ecosystem may be thinner compared to more established toolkits.

where did i find this details?

https://huggingface.co/PaddlePaddle/PaddleOCR-VL "PaddlePaddle/PaddleOCR-VL"
https://github.com/deepseek-ai/DeepSeek-OCR "deepseek-ai/DeepSeek-OCR: Contexts Optical Compression"

Found better Markdown editor

Stephen BJ — Thu, 10 Jul 2025 07:14:25 +0000

Today, I had the pleasure of working with mdxeditor.dev, and I must say — it's an impressively modern and developer-focused rich text editor designed specifically for working with MDX (Markdown + JSX). If you are building content management systems, documentation platforms, or LLM prompt builder this library is definitely worth your attention.

`mdxeditor.dev`?

it blends the simplicity of markdown with the flexibility of reactjs components, offering a great authoring experience for developers.

Features i like

Clear and Modular API — Easy to extend and customize the editor experience.
Image and Media support — Built-in components for handling images, code blocks, etc.
Custom Component Previews — Define how your custom MDX components render in the editor.
Plugin-based Architecture — Add or remove features like headings, tables, or frontmatter with ease.

Getting started (I copied this from their API document )

Installation is a breeze:

npm install @mdxeditor/editor

Then, just drop it into your React component:

import { MDXEditor, headingsPlugin, listsPlugin } from '@mdxeditor/editor';

<MDXEditor
  markdown="# Hello MDX"
  plugins={[headingsPlugin(), listsPlugin()]}
/>

the api is clean and intuitive. it does not take much to get a full-fledged editor up and running.

developer experience

What stood out the most during my usage today:

The documentation is incredibly clear and well-structured — with example, props, and plugin guides.
the plugin architecture makes it very easy to compose editor feature.
everything was 'typed with TypeScript', making development smoother and but little anoying for me (because i am not good at typescript).

Thing i loved

Simplicity + Power - easy enough to get started in minutes, yet powerful enough for complex customization.
Plugin Ecosystem - Everything from code blocks to markdown text can be plugged in or out.
Live Preview Support - you can define how MDX components render in-editor.

Visit mdxeditor.dev →

buymeacoffee

From vision to reality: My journey building with code

Stephen BJ — Tue, 01 Jul 2025 04:40:38 +0000

Hi, I’m Jerin Stephen a full-stack developer, creative thinker, and someone who believes in turning ideas into working systems. I wanted to share a story, not just about coding, but about making things real. If you're someone who loves to build, experiment, or even just imagine this is for you.

It all started with a thought...

I didn’t just want to write code, I wanted to create.

For me, building software has always been about more than just syntax or solving tickets. It is about crafting meaningful experiences, whether that is a dynamic dashboard, a smart automation, or a tool that actually helps someone. I’ve worked with Python, FastAPI, react.js, PostgreSQL — not because they’re trendy, but because they help me build what i envision.

I Love building things Like:

✅ smart user interfaces

The kind that respond fast, feel intuitive, and adapt to the user.

✅ Seamless integrations

Linking systems together so they feel like one — from frontend to APIs.

✅ Clean, scalable backends

Codebases that make senses months later, with logs, logic, and structure that scale.

✅ Tools That Help People

Whether it’s a teammate, a client, or a complete stranger when someone says, "This made my job easier," that is the win.

Why I’m Sharing This

Because I started just like most people - Googling how to make a button work or doing vibe coding

Today, I build things I once thought were too complex. And I have learned something powerful:

You don’t need permission to start. Just curiosity and consistency.

You don’t need a big team. You do not need all the answers. You just need the courage to build, one step at a time.

Lessons I Live By

Start small but start. A simple tool can lead to something big.
Keep things human. design for people, not just for machines.
Be okay not knowing. That is where learning lives.
Use tools. But also shape them. Do not settle for default.

Final Words

I used to chase certifications. Now I chase ideas.

If you're a student, a beginner, or even a tired senior dev:

The things you imagine can become real.

Whether it’s an app, a game, a plugin, or something no one’s seen before bring it to life.

Take your passion. Add some code. And create.

We’re not just developers. We’re makers.

career update

Stephen BJ — Mon, 30 Jun 2025 06:41:21 +0000

Stephen BJ

Jun 27 '25

The Future of Software Engineering — From My Experience

Comments

3 min read

PDF extractor alternatives for Marker PDF

Stephen BJ — Mon, 30 Jun 2025 04:50:44 +0000

When working on PDF extraction tasks for large-scale document processing and knowledge workflows, especially involving llm, i needed an efficient alternative to Marker PDF. i’ve evaluated and tested multiple open-source libraries, checking for their speed, accuracy, layout retention, image extraction, and ease of integration with LLM pipelines. Below is my hands-on review of different tools i explored and how they stack up.

My Evaluation Criteria

i focus the following aspects:

license – open source friendly for commercial projects.
page limitations – whether there's a hard limit.
image extraction – support for embedded or scanned images.
output types – markdown, json and plain text .
layout undarstanding – whether it preserves layout elements like tables, columns, and headers.
ease of use – setup, community, and speed.

sn	library name	license	image extraction	output types	layout details	pypi link	comments
1	PyMuPDF4LLM	agpl-3.0	yes	Markdown, Llamaindex Docs	Limited	link	very fast and effective. minimal setup.
2	pdfplumber	mit	limited	JSON, Text	Yes	link	good for table heavy pdfs.
3	pdfminer.six	mit	no	Text	Yes	link	works but average accuracy.
4	markitdown	mit	no	Markdown	No	link	basic converter, nothing special.
5	nougat-ocr	apache 2.0	yes	MultiMarkdown	No	link	good results, but painfully slow.
6	pdf-to-markdown	mit	yes	Markdown	No	-	decent, but loses some structured data.
7	olmocr	apache 2.0	yes	Markdown	Yes	link	excellent results with structure retained.
8	docling	mit	yes	Markdown, JSON and HTML	Yes	Demo	tested via colab, very effective.
9	EasyOCR	apache 2.0	images only	List, JSON	Yes (image)	link	only for ocr, doesn't retain layout.
10	OCRmyPDF	mpl 2.0	yes	PDF/A	Yes	link	complicated setup, good ocr layer addition.
11	LayoutParser	apache 2.0	no	JSON	Yes	link	use with other ocr tools for layout parsing.
12	pandoc	gpl 2.0	no	Markdown	No	link	did not work well in pdf to md conversion.
13	pypdfDirectoryLoader	open source	no	Text, Markdown and HTML	No	-	works but lacks layout awareness.
14	pdfium	bsd-3	no	CSV, Markdown, Text	No	-	good for low-level pdf rendering.
15	tableau	mit	no	CSV	No	-	only useful for table extraction.
16	pypdf	open source	not clear	Text	No	link	does not handle layout or images well.

My top picks (as of now)

1. PyMuPDF4LLM

super fast and integrates well with llm pipelines like llamaindex. excellent markdown output. limited layout parsing, but still very usable

2. OlmoCR

surprisingly accurate with layout and structure.it even preserved indentation and titles nicely. great for fine-tuning document-based llms.

3. Docling

A great all-rounder, especailly with visual layout parsing.useful for html or json-based workflows.

4. pdfplumber

if you are dealing with tabular PDFs, this is your goto. Fast and battle-tested.

What did not Work for Me

pandoc failed miserably with most PDF inputs for markdown conversion.
MarkerDown and pdfminer.six struggled with layouts and formatting.
OCR-only tools like EasyOCR and OCRmyPDF were limited to images or required post-processing.

Final thought

Marker PDF is solid, but not always the right fit, especially when you need control, open-source licensing, and llm-friendly formatting.tools like PyMuPDF4LLM, Olmocr, and Docling provide a powerful alternative with minimal trade-offs.

if you want something ready for fine-tuning datasets, these three would be my top choices to build on.

Please share your ideas in comment it will be very helpful for me to learn something

document-parsers-list

The Future of Software Engineering — From My Experience

Stephen BJ — Fri, 27 Jun 2025 04:02:20 +0000

As a developer who's used Chatgpt, claude, gemini, versal and cursor I've seen firsthand how AI is reshaping how we build software. I wanted to share my point of view on where this is all headed not from a theoretical standpoint, but based on actual hands-on experience with these tools.

What I Noticed with Each Tool

*ChatGPT * ChatGPT is my go-to for brainstorming ideas, debugging, and getting explanations. It's like having a 24/7 assistant who can break things down or generate alternatives when I'm stuck.
Claude When it comes to code, Claude really stands out. The way it generates clean, readable, and logically sound code is better than most tools I’ve used. I find myself using it when I need reliable and thoughtful solutions.
Gemini Gemini integrates well with Google’s ecosystem. I didn’t use it as much as others, but for Android development and anything Google-related, it’s a solid choice.
Versal Versal blew me away with its UI-building capability. It’s the best tool I’ve come across for visually designing interfaces. It feels like Figma but with AI working behind the scenes.
Cursor Cursor is amazing for code. It’s like a coding partner inside the IDE. You get suggestions, refactoring help, and contextual responses without leaving your environment.

What This Means for the Future of Software Engineering

From developer to designer : With tools like Versal, I don’t have to manually code every layout. I focus more on the structure, flow, and usability. The AI takes care of the repetitive stuff.
From writing code to building systems : ai handles most of the boilerplate and even logic-heavy parts. I’ve started spending more time designing system behavior, not just writing lines of code.
QA and debugging are changing : claude and Cursor are so good at catching bugs or writing test cases that I’m relying on them for quality checks. It saves a lot of time.
Prompting is the new skill :being able to give the right prompt is now more important than memorizing syntax. I’ve realized that a good prompt leads to great results.

*Where i think this is going *

AI-native stacks: We’ll see frameworks and tools built to work hand-in-hand with AI.

More non-coders building apps: Tools like Versal + AI let anyone design interfaces devs will be the ones building smart components behind them.

Multimodal input: In the future, I think we’ll build apps using sketches, voice, or even images. Text won’t be the only way.

Will AI Replace Developers Like Me?

Not really. But it’s clear to me that:

Developers who don’t use AI will fall behind.

I now see my role evolving less coding from scratch, more system thinking and smart prompting. The people who grow with AI will always stay relevant.

Final Words

If you are a developer, start using these tools claude for coding, versal for UI, chatgpt for planning, Cursor for in-editor help. Learn how to guide AI, not just compete with it.

That’s how I see the future, a developer who can work with AI is not just more efficient they’re unstoppable.

buymeacoffee

RAG vs Fine-Tuning vs Alternatives

Stephen BJ — Thu, 26 Jun 2025 12:41:28 +0000

1. RAG (Retrieval-augmented generation)

Overview:

Combines retrieval and generation.
Retrieves relevant documents or chunks from a knowledge base.
The model then generates a response based on both the prompt and the retrieved content.

Cons:

Depends on retrieval quality.
Slightly higher latency.
Requires vector DB setup.

Use Cases:

Chatbots with knowledge bases.
Real-time support systems .

* Assistants with dynamic information.

2. Fine-Tuning

Overveiw:

Update model weights with domain or task-specific data.
Trains the model to behave specifically for your use case.

Types:

Full fine-tuning
Parameter-efficient (LoRA, QLoRA)

Cons:

Expensive in terms of compute.
Harder to update ( Need more GPU and datasets ).
Risk of overfitting.

Use Cases:

Legal or medical assistants
Summarization of company documents
Enterprise chat models

3. Alternatives to RAG and Fine-Tuning

3.1 Prompt Engineering

Design prompts to guide model behavior.
Use zero-shot or few-shot learning.

Pros:

Fast and low-cost
No retraining needed

Cons:

Limited in complexity and flexibility

3.2 Instruction-Tuned Models

Use LLMs already fine-tuned on instructions.
E.g., GPT-Instruct, Mistral-Instruct, Zephyr, LLaMA-2-chat

Pros:

High generalization
Works well with structured prompts

Cons:

Cannot customize deeply

Summary Table

Methods	cost	Setup complexity	customization	fresh knowledge	Ideal For
RAG	medium	medium	medium	Yes	Assistants, dynamic knowledge
Finetuning	high	high	high	No	Specialized domain behavior
Prompt engineering	Low	very Low	low	Yes	Lightweight domain tasks

buymeacoffee

hey how to create plugin using react like dataTable.js?

Stephen BJ — Thu, 08 Apr 2021 09:53:45 +0000

hey guys i wounder is it possible to create plugin or library using react js or any other front end framework ? Please add comment or post related to this

DEV Community: Stephen BJ

ASGI and WSGI

Paddle OCR-VL & DeepSeek-OCR

PaddleOCR-VL

DeepSeek-OCR

where did i find this details?

Found better Markdown editor

mdxeditor.dev?

Features i like

Getting started (I copied this from their API document )

developer experience

Thing i loved

From vision to reality: My journey building with code

career update

The Future of Software Engineering — From My Experience

PDF extractor alternatives for Marker PDF

My Evaluation Criteria

My top picks (as of now)

1. PyMuPDF4LLM

2. OlmoCR

3. Docling

4. pdfplumber

What did not Work for Me

Final thought

The Future of Software Engineering — From My Experience

RAG vs Fine-Tuning vs Alternatives

1. RAG (Retrieval-augmented generation)

Overview:

Cons:

Use Cases:

* Assistants with dynamic information.

2. Fine-Tuning

Overveiw:

Types:

Cons:

Use Cases:

3. Alternatives to RAG and Fine-Tuning

3.1 Prompt Engineering

3.2 Instruction-Tuned Models

Summary Table

hey how to create plugin using react like dataTable.js?

`mdxeditor.dev`?