DEV Community: Romil Jain

theORQL — Debug Like a Space Wizard 🚀

Romil Jain — Sat, 13 Dec 2025 07:27:24 +0000

(Yes, you’ll actually look forward to fixing bugs!)

Imagine this:
You’re building a React or Next.js app. You refresh the browser… and boom an error pops up. You open DevTools, inspect logs, jump back to VS Code, hunt through dozens of files… 🐛 Hello anxiety.
But what if debugging could feel like a guided mission instead of a scavenger hunt?

Enter theORQL — a next-gen runtime debugging tool that ... generates fixes you can apply straight to VS Code. 🚀

💡 What theORQL Really Is

👉 theORQL is a tool that runs inside your Chrome browser during app development.
It:
✨ Catches runtime errors right where they happen
✨ Explains why the error occurred
✨ Offers verified code fixes
✨ Lets you sync fixes directly to VS Code without copy-paste madness

So instead of:

“Hmm… where did that error even come from?”
you get:
“Here’s the root cause — and I’ve got a sugge'sted patch.” ✔️

Yes, actual AI-augmented debugging magic. ✨

How It Feels to Use It

Normal debugging is like:
✔️ Search console logs
✔️ Open network tab
✔️ Reproduce bug
✔️ Go to editor
✔️ Fix
✔️ Test
✔️ Repeat

With theORQL?
🪄 Error appears → tool shows cause → one click to apply fix → sync to VS Code.
No context switch hell. Fewer tabs. Less stress. 🙌

Real users are already saying:

“It saved me hours because it unified everything — errors, logs, network failures — into one view.”

🛠️ Why It Actually Helps

Here’s the practical win for devs:

✔️ Less cognitive load

Errors are explained — not just printed.
You see why something broke, not just that it did.

✔️ Fewer context switches

No more jumping between DevTools and your editor.
It links directly with VS Code.

✔️ Faster iterations

Quick fixes → quicker testing → faster deployments.
Meaning more time focusing on features, not fire drills.

🚀 Real-Life Analogy

Think of classic debugging like being lost in a forest with only a compass. 🧭
theORQL is like having a drone overhead pointing out the trail and saying:

“Hey — there’s a pothole right there. Here’s a plank to step over it.” 🛠️
.
You still walk the path, but the ambushes are way fewer, and you know exactly where you’re stepping.

📌 finally (the final verdict)

If you’re building modern JavaScript apps (React, Next.js), debugging still eats a big chunk of your time.
theORQL isn’t replacing your brain — but it sure feels like having a co-pilot that spots issues, explains them, and helps fix them fast. 💫

🧩 Case Study: Merlin AI — Democratizing Access to Premium AI Models with a Unified Subscription

Romil Jain — Thu, 31 Jul 2025 13:02:50 +0000

📌 Overview

Company: Merlin AI
Product: All-in-one AI assistant with access to multiple top-tier models (GPT-4, Claude 3, Gemini, Grok, etc.)
Subscription Model: ~\$19/month (or \$15.83/month annually)
Positioning: Unified platform offering conversational AI, writing tools, code assistance, and search, all in one interface

🏁 Challenge

Most AI users are siloed into one provider:

OpenAI (ChatGPT Plus) for GPT‑4
Claude Pro for Anthropic’s models
Gemini Advanced for Google’s models This leads to fragmented usage, multiple subscriptions, and increased costs.

The challenge Merlin sought to solve:

How can users access the best of AI in one place, without breaking the bank?

💡 Solution: A Unified AI Access Layer

Merlin AI launched as an aggregator of AI services, offering:

Access to multiple LLMs via one UI
Unified billing through a single subscription
Utility-based credit system to manage costs
Use-case-based tools (summarizer, chatbot, code assistant, etc.)

By integrating APIs from OpenAI, Anthropic, Google, and others, Merlin acts as a middleware platform, offering the right model for the right task — without users managing multiple accounts or costs.

⚙️ How It Works Behind the Scenes

1. Smart Usage Control

Implements a credit-based “Zap” system to abstract costs from users.
Tracks per-model usage and restricts API calls once the user reaches ~\$100/month backend cost.

2. Cost-Effective Partnerships

Likely negotiates bulk API usage rates with model providers.
Implements session-based usage, allowing only one model at a time per user to control load.

3. Fair Use Limitations

Marketed as “unlimited,” but enforces fair use:
- Daily soft caps (~\$16/day)
- Monthly ceiling (~\$100 backend API cost/user)
- Extra usage via top-ups (~\$3.5 for extensions)

4. Interface & Experience

Provides a clean, simplified interface across all models.
Tools built on top of LLMs (summarize, translate, rephrase, etc.)
No-code usage, no developer account setup required.

📊 Impact

Metric	Result
User Accessibility	Significant rise in users gaining access to premium AI
User Satisfaction	Mixed – praised for convenience, critiqued for limits
Cost Efficiency	1 subscription vs ~\$60+ for using each model separately
Churn Risk	Moderately high for power users hitting fair-use limits
Competitive Advantage	Aggregation, simplicity, model diversity

🔍 Limitations & Risks

❌ Not suitable for high-throughput users (researchers, devs)
❌ Opaque credit system—users unsure when/why they hit limits
❌ Performance may vary vs. using native apps (e.g. missing features in Claude/Gemini)
❌ Dependent on third-party APIs — subject to pricing or usage policy changes

🏆 Why It Works (for Now)

Merlin targets the average user who:

Wants multiple models
Doesn’t want to manage APIs
Needs AI assistance for light to moderate tasks

By smoothing the complexity of model switching and flattening the pricing, Merlin offers convenience as a service — a compelling value proposition in a fragmented AI landscape.

📌 Key Takeaways

Merlin AI operates like a "Netflix for LLMs" — one subscription, many models.
It balances backend costs via fair use limits, credit gating, and user behavior modeling.
Ideal for casual to moderate AI users, but not for high-scale or enterprise usage.
The model may inspire white-label AI frontends in other industries (health, law, edtech).

Why learn AWS?

Romil Jain — Wed, 26 Apr 2023 08:11:19 +0000

👨‍💻 As a developer, it’s important to have a solid understanding of AWS and its services.

🏢 This knowledge will come in handy in any company you work for, as you’ll likely encounter situations where AWS expertise is required or to understand the implementation of AWS.

💻 Additionally, having knowledge of AWS can give you an edge when working on personal projects, as you can host them at an affordable price.

🔧 It’s also important to have a general understanding of which service to use for which task. Popular AWS services include EC2, Lambda, Route53, CloudFormation, S3, Load Balancing, VPC, CloudWatch, CloudFront, Glacier, SNS and RDS.

📚 Edureka offers great tutorials for those looking to gain a comprehensive understanding of AWS services. Also, you can try reading some blogs on Medium

👍 So, it’s definitely worth investing time to learn AWS and its services to enhance your skills and stay ahead in the game.

Arrays Basics

Romil Jain — Sat, 06 Nov 2021 16:34:15 +0000

In Arrays, we can store multiple items of the same type together.

int A[5]; → means that we have declared an array of size 5
when we count arrays we count from zero , so here count is represented by index.

A[0]=14; → means we have stored 14 at index 0
A[1]=2; → means we have stored 2 at index 1

We can declare above array easily by using code blocks
int A[10]={3,5,6,7,8,9}; → this is called declaration + assigning the value

I would recommend you to study C language from GeekForGeeks and learn about structures

HINT :- To print all array values


for(int i=0;i<5;i++){
  printf("%d", A[i];
};

Questions that we all have before learning Data structures and Algorithms.

Romil Jain — Sat, 06 Nov 2021 14:11:54 +0000

Why learn data structures?

So data structures are the core and most basic subject for programmers, Any application you build, you have to use data structures in order to optimize the app and deliver the best version of it, without DS it’s not possible to develop applications.
You should learn data structures until you know how to code them and you can develop your own data structures.

Which programming language is suitable?

You can choose any language but the most popular is C & C++. C language does not have any built-in data structures so you can learn DS from the base. I would prefer you to start with C lang. after that you can switch to C++ for competitive coding.

What are Algorithms?

Algorithms are those that are used on data structures. Algorithms is a very vast topic. some popular algorithms are face recognition algorithm (used by our smartphones), location tracking (algorithms used by Google maps), and many more. You can find them on the internet.

Welcome post

Romil Jain — Sat, 06 Nov 2021 03:57:05 +0000

Hello Dev community,
This is my first blog, Let me introduce myself, Currently I am learning MERN stack and finding some open source projects on react.js (if you know any please let me know). I have done my graduation in 2020 (b.sc maths). I also work as a graphic designer or you can say UI/UX designer, I am passionate about coding and love to build things. you can visit my website to find more details about me at https://jainromil56.github.io/home/

Thank you for reading.