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Anurag Bhattacharjee
Anurag Bhattacharjee

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The Shift Every Developer Should Be Paying Attention To

We’ve Moved Past “Using AI” — We’re Now Building With It

For a while, AI in apps meant one thing:

call an API, get a response, ship a feature.

That phase is ending.

What’s changing right now isn’t just model quality or bigger context windows — it’s the rise of agentic, multimodal systems that can reason, plan, and act across tools.

Modern AI systems can now:

  • Work across text, images, audio, and video
  • Break a goal into steps
  • Decide when to call APIs, databases, or other models
  • Operate inside real production pipelines instead of demos

This is quietly reshaping how we write software.


Development Is Becoming Goal-Driven

Traditional apps are flow-based:

user clicks → backend logic → response.

AI-native apps are increasingly goal-based:

  • You describe the intent
  • The system reasons about the steps
  • Tools are selected dynamically
  • Humans step in when confidence drops

That shift changes the role of the developer.

We’re moving from:

  • writing rigid control flows to
  • designing systems that can reason safely

What’s Emerging in Real-World Stacks

Across production systems, a few patterns are becoming common:

  • Model routing instead of a single “best” model
  • On-device inference for latency and privacy
  • AI observability (evals, traces, hallucination detection)
  • Human-in-the-loop by default, not as a fallback
  • Treating prompts as versioned system components, not strings

“AI integration” is no longer a feature.

It’s part of the architecture.


The Real Skill Shift

The most valuable skill right now isn’t prompt tricks.

It’s understanding:

  • how models reason
  • where they fail
  • how to constrain them
  • and how to build guardrails without killing usefulness

Developers who treat AI as a thinking system — not just an API — will build more reliable, scalable products.

This isn’t about replacing developers.

It’s about redefining what good engineering looks like in an AI-native world.


I’m curious how others here are approaching this.

Are you:

  • building agent-style systems?
  • adding AI to existing products?
  • or still experimenting in side projects?

Would love to hear what’s working (and what’s breaking) in your stack

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