Forem: Nikul Pandya

Rethinking Software Development in an AI-Assisted World

Nikul Pandya — Sun, 25 Jan 2026 08:13:24 +0000

I’ve been using AI-assisted development tools for more than a year now, across real production environments.

This isn’t an experiment.

It’s not a novelty phase.

And it’s not about letting AI “write the app.”

What has changed is where engineering effort is spent.

I still design systems.

I still make architectural decisions.

I still review, validate, and own the outcome.

But writing code is no longer the primary bottleneck in my workflow.

A Shift in the Unit of Work

Before AI, the unit of work in software development was often the line or the function.

With AI, the unit of work becomes the decision.

Instead of asking:

How do I write this code?

I now ask:

What should this component do, and what constraints must it respect?

AI is then used to execute those decisions efficiently, within boundaries I define.

Planning as a First-Class Step

AI is most valuable before any code is written.

When given requirements and access to existing patterns, it can:

Surface architectural conventions
Propose module boundaries
Highlight likely edge cases
Outline responsibilities across layers

This doesn’t replace design work.

It compresses the feedback loop between intent and structure.

Bad plans still produce bad code — just faster.

Implementation With Guardrails

During implementation, AI operates under explicit constraints:

Follow existing conventions
Respect architectural boundaries
Generate changes incrementally
Explain non-obvious decisions

The developer remains the control plane:

Approving direction
Injecting domain knowledge
Rejecting incorrect assumptions
Stopping over-engineering early

This model works because authority never leaves the human.

AI executes.

Developers decide.

Review Becomes More Strategic

AI-generated code still requires review — but the nature of review changes.

Instead of spending time on:

Naming
Formatting
Repetitive refactoring

Reviews increasingly focus on:

Correctness
Edge cases
Architectural fit
Unintended coupling

Static analysis and automated reviewers become more effective when paired with AI-assisted fixes, especially when review feedback is fed back into the generation loop.

AI doesn’t remove review.

It raises the baseline before review even begins.

Failure Modes and Limits

AI is not a reasoning engine.

Common failure modes include:

Confidently wrong assumptions
Shallow domain understanding
Subtle security oversights
Plausible but incorrect abstractions

This is why:

AI must never own final decisions
Tests remain non-negotiable
Security-sensitive code demands extra scrutiny

AI increases throughput.

It does not increase accountability.

What Actually Improves

After long-term use, the most noticeable improvements aren’t raw speed metrics.

They’re qualitative:

Reduced cognitive fatigue
Fewer trivial review comments
Cleaner diffs
More consistent structure across the codebase

Senior engineers spend more time on design and risk, and less time on mechanics.

The Emerging Role of the Developer

AI-assisted development doesn’t reduce the importance of engineers.

It clarifies it.

The developer becomes:

A system designer
A constraint setter
A reviewer of intent, not syntax
The final authority on correctness

Typing is no longer the scarce skill.

Judgment is.

Closing Thought

The future of software development is not AI writing code autonomously.

It’s humans and AI working in a tight, disciplined loop — where AI handles execution and humans handle responsibility.

If you adopt AI this way, the question stops being:

Will AI replace developers?

And becomes:

What should developers spend their attention on now that typing isn’t the bottleneck?

Vibe Coding: My Experience Building a Website with AI (Developer vs Non-Tech Perspective)

Nikul Pandya — Thu, 31 Jul 2025 09:46:37 +0000

🚀 Introduction

AI-powered coding platforms are transforming the way we build websites. Instead of writing every line of code, you can now describe your idea in plain language, and an AI agent generates a deployable project for you.

Recently, I experimented with one of these Vibe Coding platforms to build a static website for my startup. While the experience was exciting, it also revealed a clear gap between how developers and non-technical users interact with these tools and highlighted some current limitations.

💡 1️⃣ What is Vibe Coding?

Vibe coding allows you to create software simply by describing your vision in natural language. The AI agent handles:

Code generation
Deployment
Sometimes even UI design elements

The Promise

Non-technical founders: Build a site without hiring a developer
Developers: Save time on boilerplate tasks and focus on higher-level architecture

But does it truly deliver a no-code experience for everyone? Let's dive in.

⚡ 2️⃣ My Experiment: Building a Startup Website with AI

I started with a simple prompt to create a basic landing page for my startup. Within minutes, the platform delivered:

A functional website layout
A ready-to-deploy project structure

✅ First impression: Great for quickly spinning up an MVP.

🌍 3️⃣ The Multilingual Challenge

Later, I wanted my website to support English and Gujarati. I began by asking the AI to make this change using a general prompt.

Prompt 1 (General Request):

Please make the website available in two languages: English and Gujarati, ensuring that all pages and navigation elements are translated accordingly.

AI's Response:

The AI returned a highly technical solution, suggesting steps like:
Setting up i18n infrastructure with libraries like react-i18next
Creating translation files for each language
Implementing a language switcher component

For a non-technical person, this kind of response is difficult to act on. Terms like i18n and translation schema are not beginner-friendly and would likely require developer assistance.

👨‍💻 4️⃣ Developer Intervention
As a developer, I opened the generated files and saw that components could easily be duplicated for translations. So, I gave the AI a more specific, simplified instruction.

Prompt 2 (Developer-Guided Request)

The website content is relatively small. Instead of a full i18n setup, please create hardcoded translated versions of the pages (e.g., product_eng.tsx, product_guj.tsx) and add a simple language switcher in the header to toggle between English and Gujarati.

This time, the AI:

Created separate Gujarati page components
Added a functional language switch button
Delivered a clean and working solution ✅ without over-engineering the task

🔄 5️⃣ GitHub Integration: A Pleasant Surprise

After completing the website, I linked the project to my GitHub account, and this turned out to be one of the platform's most impressive features:

Any manual code edits I pushed to the main branch were automatically synced with the live deployment

The platform maintained full compatibility between the AI-managed environment and my manual changes

This gave me the best of both worlds:

AI-assisted rapid development

Full developer control when needed

⚠️ 6️⃣ Limitations of Vibe Coding (Current State)

While Vibe Coding is great for static websites and front-end-heavy projects, it still faces big challenges in more complex scenarios, such as:

Backend systems: AI struggles with database schema design, API security, and complex business logic
Data persistence: Managing relational databases, migrations, and scalable architectures isn't fully automated yet
Advanced integrations: Connecting third-party APIs, authentication, and cloud infrastructure often needs manual intervention

In my opinion, Vibe Coding is ideal today for small static sites or quick prototypes, but not for production-grade full-stack applications, at least not yet. This may change as AI coding tools mature, but developers remain essential for now.

📌 7️⃣ Key Observations (Developer vs Non-Tech)

✅ Developers

Can refine prompts and review generated code for better results
Benefit from GitHub sync to combine AI assistance with manual coding
Gain productivity but still need skills for backend, databases, and scalability

⚠️ Non-Technical Users

May receive responses too technical to act on
Might hit a dead end if the AI doesn’t get it right on the first try
Still need occasional developer support for anything beyond a simple site

🧠 8️⃣ My Takeaways on Vibe Coding

Huge potential for rapid prototyping and static site generation
Not yet ready for complex backend-driven applications
Best used in a hybrid workflow: AI for speed, developers for precision and scalability

🎯 Conclusion
Vibe coding is an exciting step forward in software development. It can dramatically speed up building small websites and prototypes. But when it comes to complex systems, backend logic, and database-heavy apps, traditional coding expertise is still essential.

Would you trust AI to build your next production app? Or do you see it more as a prototyping assistant?

💬 Share your thoughts below—I’d love to hear your experiences with AI-powered coding!