Forem: Nachiappan NK

Fundamentals Matter More Than Ever Before

Nachiappan NK — Wed, 29 Apr 2026 10:30:39 +0000

Today, it feels like we can code at the speed of thought.

With tools like GitHub Copilot and Claude Code, generating code is no longer the bottleneck. You describe what you want, and working code appears in seconds. For experienced developers, this feels like a superpower.

But something interesting is happening.

We are coding faster — but we are not delivering faster.

The Productivity Paradox

At first glance, faster code generation should mean faster delivery—more features, quicker releases, happier users.

But that’s not what teams are experiencing.

The bottleneck hasn’t disappeared.
It has shifted.

Instead of spending time writing code, we now spend more time:

Reviewing AI-generated changes
Debugging unexpected edge cases
Fixing regressions
Understanding code that no one truly designed
Deploying builds just to manually validate behavior

The result?

Speed at the start is creating drag at the end.

Tightening the Loop: Speed Comes From Feedback, Not Just Code

If code generation is no longer the bottleneck, feedback is.

The teams that move fast today are not the ones that write code quickly—they are the ones that learn quickly.

Every stage of the lifecycle is essentially a feedback loop. The earlier and sharper the feedback, the less expensive the correction.

Feedback on Requirements

Most delays don’t originate in code—they start with unclear or incomplete requirements. When inputs are vague, even AI generates confident but incorrect solutions, accelerating mistakes instead of progress. Faster coding only amplifies this problem.

Delivery speed improves when there is a clear path for quick, continuous feedback on requirements. Teams that validate assumptions early, use concrete examples, and clarify edge cases upfront reduce rework significantly. The faster the feedback loop at this stage, the fewer surprises later.

Strong requirement feedback isn’t about critique—it’s about alignment. Breaking work into small, testable slices with clear business value creates shared understanding across teams. When feedback flows quickly and consistently, delivery naturally becomes faster.

Design and Feedback on Design

A lightweight, one-page design can go a long way in accelerating delivery. It doesn’t need to be elaborate—just clear enough to communicate intent. Especially when performance considerations or multiple systems are involved, capturing the high-level approach early helps avoid costly course corrections later.

The goal is not documentation for its own sake, but alignment. Outlining how data will be queried, stored, and how components will interact—anchored to the core business use cases—gives reviewers enough context to provide meaningful feedback quickly. This is where most hidden risks surface.

When teams establish a fast feedback loop around design decisions, they effectively shift critical thinking earlier in the lifecycle. That “left shift” reduces ambiguity, prevents rework, and ensures that what gets built is both intentional and scalable.

Guiding Code Generation

Today, development is less about writing every line of code and more about guiding how code is generated. LLMs produce broadly correct solutions, but they often miss the local conventions of a codebase—naming patterns, architecture, and project-specific decisions. Without clear guidance, this can lead to inconsistencies, even within the same repository.

As a result, developers need to actively steer the model by anchoring prompts in existing code, being explicit about expectations, and refining outputs iteratively. Writing code without LLMs is becoming less common; shaping their output is now a core skill.

Bringing the right context closer to the model is key. Tools like MCP servers and other context-aware integrations help ensure generated code aligns with the system’s design. Good guidance upfront reduces rework later and keeps the codebase consistent as development speed increases.

Review Feedback

As code generation accelerates, manual review becomes the new bottleneck. It’s no longer practical—or effective—for humans to meticulously review every line of code produced at high velocity. The volume is simply too high, and the nature of AI-generated code often requires a different kind of scrutiny.

This is where automated review systems become essential. LLMs can be leveraged not just for generating code, but also for reviewing it—checking for adherence to architectural guidelines, coding standards, and common pitfalls. However, for this to work well, teams must invest in clearly documenting their conventions and design principles. Without that foundation, automated reviews risk being generic and less useful.

Shifting review feedback earlier in the development cycle is critical. Instead of treating review as a final gate, integrating continuous, automated feedback during development helps catch issues sooner. This “left shift” reduces the cost of fixing problems, shortens feedback loops, and ensures that speed in code generation does not compromise quality or maintainability.

Testing Feedback

Unit testing remains critical—but its role is often misunderstood in today’s development environment. What was originally envisioned by Kent Beck as a fast, reliable safety net has, in many teams, drifted into something slower, brittle, and less trustworthy. When unit tests become tightly coupled to implementation details or hard to maintain, they lose their original purpose.

The Test Pyramid still holds strong as a guiding principle. A healthy system has many fast, isolated unit tests at the base, fewer integration tests in the middle, and a small number of end-to-end tests at the top. Faster tests should dominate because they provide immediate feedback, while slower tests should be fewer and more intentional. Ignoring this balance leads to sluggish pipelines and delayed feedback.

Achieving this requires more than just writing tests—it requires designing for testability. Software architecture and code structure must support isolation, clear boundaries, and predictable behavior. When code is tightly coupled or lacks clear interfaces, writing effective unit tests becomes difficult, and teams are pushed toward slower, more expensive testing strategies.

Not all behavior can be validated through unit tests alone. Some scenarios require real environments, integrations, or deployments to validate correctness. This makes automated, reliable deployment pipelines essential. When deployments are fast and repeatable, even higher-level tests can provide timely feedback without becoming a bottleneck.

The goal is not to maximize the number of tests, but to optimize feedback. Fast, reliable tests give teams confidence to move quickly, while a well-balanced testing strategy ensures that speed in development does not come at the cost of stability in production.

Speed Is Now a Feedback Problem

The real shift isn’t that we can generate code faster—it’s that speed has moved elsewhere.

Code is no longer the constraint. Feedback is.

The teams that will outperform are not the ones producing the most code, but the ones reducing the time between idea → feedback → correction. They validate requirements early, align on design quickly, guide code generation intentionally, and rely on fast, trustworthy feedback from tests and reviews.

In this world, velocity is no longer measured by how fast code is written, but by how quickly teams can learn what’s wrong—and fix it.

AI has given us speed at the start.
The advantage now belongs to those who can sustain it through the entire lifecycle.

When Vibe Coding Ships Fast and When It Ships Bugs

Nachiappan NK — Mon, 27 Apr 2026 03:02:33 +0000

Today, it feels like you can vibe code your way through almost anything.

I’ve been using these tools since their early days and with nearly two decades in development, I’ve learned that’s only half the story.

What is vibe coding?

Vibe coding is a style of programming where you rely more on instinct than upfront design. Instead of planning the full solution, you start with a rough idea and figure things out as you go—trying, tweaking, and moving forward. It prioritizes speed and exploration over structure and predictability.

It works well when you’re experimenting or validating ideas quickly, but can become fragile when the code needs to scale, be maintained, or understood by others.

With tools like Claude and GitHub Copilot, this approach has become much faster and more common. In practice, this often looks like continuously prompting for functionality and iterating on the results. However, using these tools doesn’t automatically mean you’re vibe coding the difference lies in how you use them.

Why we love it

Vibe coding feels fast. There’s no heavy planning, no long design cycles just immediate progress. You can take an idea and start building right away, which creates a strong sense of momentum.

It also puts you in a flow state. You’re not constantly switching between thinking, designing, and prompting to code. That makes the process feel natural and even enjoyable, especially when things start working quickly.

At its best, vibe coding makes development feel effortless—and that’s exactly why it’s so appealing.

Where it works

Vibe coding is useful for exploring new product ideas or quickly experimenting with features in existing products. It helps teams move fast, generate early feedback, and bring abstract ideas to life. However, the code produced through this approach isn’t always suitable for long-term use. Instead of merging it directly into the main codebase, it’s better treated as a prototype—something you can deploy quickly for demos or discussions, and later rebuild properly if the idea proves valuable.

Where it fails ⭐

Vibe coding starts to struggle when the generated code becomes too verbose, often adding more than what’s actually needed. This can make the code harder to read and understand, especially for others who weren’t part of the initial flow. At times, the output also doesn’t align well with the larger architecture or design of the system, creating mismatches that are difficult to integrate. What feels like fast progress in the moment can later slow things down when the code needs to be cleaned up, simplified, or reworked to fit properly.

AI-assisted coding (the middle ground)

There’s a practical middle ground between vibe coding and fully manual development—AI-assisted coding with guardrails.

Tools like GitHub Copilot and Claude can be used to generate production-ready code, but with clear constraints. Instead of open-ended prompting, you guide the output with expectations around structure, patterns, and standards. The code is then reviewed, tested, and refined just like any human-written contribution.

In this approach, AI becomes a collaborator rather than a driver. You still benefit from speed, but without giving up control over quality and consistency. The generated code fits better into the existing system because it’s shaped by context—not just intent.

This is where AI starts to shine in real-world development: not by replacing discipline, but by accelerating it.

When to switch

The tricky part with vibe coding isn’t starting—it’s knowing when to stop. What begins as fast exploration can quietly turn into fragile code if you keep pushing it too far.

A good signal to switch is when the code starts being reused, extended, or shared across the team. If you find yourself debugging more than building, or explaining the code more than writing it, you’ve likely crossed the line. That’s the point where speed stops being an advantage.

Another clear indicator is when your idea has proven itself. If you’ve covered multiple use cases—say three or four—and stakeholders are aligned on the direction, you’re no longer exploring. You’re building something real.

And if you expect the codebase to live beyond a short window—anything more than three to four months—it’s a strong sign that it needs proper structure. Code that lasts needs to be readable, testable, and maintainable, which vibe coding doesn’t naturally optimize for.

Switching doesn’t mean throwing everything away—it means slowing down, introducing structure, and rebuilding the parts that need to last.

Decision framework

A simple way to decide when to vibe code—and when not to:

Is this for exploration or idea validation? → Vibe code
Is this meant for a demo or quick discussion? → Vibe code
Is this going into production? → Add design and structure
Will other developers depend on this code? → Avoid vibe coding
Do you expect this to live beyond a few months? → Build it properly
Have multiple use cases already been validated? → Time to switch

The Bigger Picture

Vibe coding is great for momentum, but not for everything. The key is being intentional—use it when speed matters, and step away when stability starts to matter more.

Vibe coding isn’t a replacement for good engineering—it’s a complement to it.

It helps you move fast, explore ideas, and break through the inertia of getting started. But if you rely on it too long, the same speed that helped you early on can become a liability.

As AI-generated code becomes more common, the real challenge isn’t whether to use it—it’s how to manage it. That means developing practices with the right guardrails: clear design expectations, strong testing, and disciplined review and merge processes. Without these, speed can easily come at the cost of quality.

The real skill isn’t just writing code—it’s knowing how to balance speed with structure, and how to integrate AI into that balance responsibly.

Shipping fast feels good. But shipping something that lasts, and can be trusted, actually matters.