I Walked Into a GDG Event Not Knowing What Vertex AI Was. Here’s What Changed.

My first Build with AI session by GDG Prayagraj taught me more than tools it reframed how I think about building for the AI era.

Honestly, I almost didn’t go.

It was the kind of Saturday morning where staying in bed felt like the rational choice. But something about the phrase “Build with AI” on the event poster made me close my laptop and head to UIT instead. I didn’t know what to expect from my first GDG event. I certainly didn’t expect to come back rethinking how I approach software development.

This is an account of what I learned technically, and otherwise.

Walking Into My First GDG Event

There’s a particular energy in a room full of developers who are genuinely curious. Not the kind of polished conference energy where everyone’s networking for business cards, but the scrappier, more honest kind people who showed up because they wanted to understand something they didn’t the day before.

That’s what GDG Prayagraj felt like.

The session was organized around a deceptively simple premise: AI isn’t something you bolt onto an application after the fact. It’s becoming foundational infrastructure and understanding the tools that make it accessible is now a baseline competency for modern developers.

I had used Firebase before. I had read about Gemini. But I had never really understood how these pieces fit together until that afternoon.

Firebase Is Not Just a Database Anymore

Most developers I know, including myself, have a mental model of Firebase that’s roughly: real-time database, authentication, hosting. Useful for prototypes. Sufficient for MVPs.

That mental model is outdated.

What the session made clear is that Firebase has evolved into a full application development platform that is increasingly designed to support AI native features. Firebase App Hosting now handles modern frameworks with edge deployment in mind. Firebase’s integration with the broader Google Cloud ecosystem means that shipping a production grade app with AI capabilities doesn’t require stitching together a dozen separate services manually.

The insight that landed for me: Firebase handles the operational surface area auth, data, hosting, functions so that developers can focus on the actual logic and intelligence of what they’re building. When you’re integrating an LLM into a product, the last thing you want to spend cognitive energy on is authentication flows and deployment pipelines. Firebase absorbs that complexity.

Vertex AI: Where “Deploying a Model” Stops Being Theoretical

Before this session, “deploying a machine learning model” sounded like a project that required a dedicated MLOps team and several months. Vertex AI changes that framing considerably.

Vertex AI is Google Cloud’s unified platform for building, training, and deploying machine learning models and more recently, for building generative AI applications at scale. What makes it meaningful for application developers (not just ML researchers) is that it abstracts away the infrastructure management that makes ML deployment feel inaccessible.

You don’t need to provision servers, manage autoscaling, or handle model versioning manually. Vertex AI provides managed endpoints, model monitoring, and a pipeline system that handles the orchestration. The result is that the gap between “I trained a model” and “this model is serving production traffic” becomes much narrower.

For someone building AI-powered applications rather than researching new architectures, this matters enormously. Vertex AI is what makes it practical to put a real model behind a real product without a six-month infrastructure project.

Gemini, and Why the CLI Interaction Model Matters

The Gemini portion of the session was where things got genuinely exciting for me.

Gemini is Google’s multimodal large language model capable of reasoning across text, code, images, and structured data. But what I hadn’t appreciated before this session was the value of interacting with Gemini through CLI-based workflows rather than purely through a chat interface.

When you bring an LLM into your terminal environment, something shifts. Suddenly you’re not switching contexts between your code editor, a browser tab, and a chat window. You’re querying the model as part of your development workflow asking it to explain a codebase, generate boilerplate, debug an error, or help reason through an architecture decision, all from within the environment where you’re actually working.

Gemini CLI makes AI interaction feel less like using a separate tool and more like having a capable collaborator embedded in your workflow. For developers who spend most of their working hours in the terminal, that’s not a minor convenience it’s a fundamental change in how AI assistance integrates into the craft.

It also signals something broader: LLMs are becoming infrastructure. Not features. Infrastructure.

Why Developer Communities Like GDG Are Genuinely Irreplaceable

There’s knowledge you can get from documentation. There’s knowledge you can get from tutorials. And then there’s the kind of knowledge that only transfers through conversation through a speaker explaining not just what a tool does, but why it was built, what problems it actually solves, and where it sits in the broader ecosystem.

GDG events operate in that third category.

What GDG Prayagraj did well was bring developers together in a context where learning felt low-stakes and genuinely collaborative. There was no pressure to perform expertise. Questions were welcomed. The people presenting weren’t distant industry figures they were developers from the same ecosystem, sharing things they had actually learned by building.

That format is valuable in a way that’s hard to replicate. The Google Developer Groups program creates local anchors for this kind of learning, which matters especially in cities and communities that aren’t traditionally seen as major tech hubs. Prayagraj having an active GDG chapter isn’t a footnote it’s meaningful infrastructure for the developer community there.

Key Takeaways

A few things I’m carrying forward from this session:

• Firebase is worth re-evaluating if your mental model is more than two years old. The platform has grown significantly, and its integration with AI tooling makes it a strong foundation for modern applications.
• Vertex AI lowers the barrier to production ML in ways that matter for application developers, not just ML specialists.
• CLI-based AI interaction is worth exploring seriously. It changes the texture of how AI assistance fits into a development workflow.
• Multimodal models like Gemini represent a shift in what developers can reasonably build applications that reason across text, images, and code simultaneously are now within reach for small teams.

A Note of Gratitude

This event happened because people chose to spend their time organizing it. Ankit Kumar Verma, Anubhav Sir, and the volunteers who made the Build with AI session run smoothly thank you. Events like this have a ripple effect that’s hard to measure but easy to feel.

What Comes Next

I came to this session thinking about AI as a feature category. I left thinking about it as a layer of the stack.

That’s not a small shift. It changes what I read, what I build to learn, and what I think the next few years of software development actually look like. Tools like Firebase, Vertex AI, and Gemini aren’t separate products to evaluate independently they’re components of an emerging architecture for AI native applications.

If you’re a developer who hasn’t engaged deeply with this stack yet, I’d genuinely recommend finding your nearest GDG chapter and showing up. Not because the tools will do the thinking for you, but because understanding them clearly is increasingly the difference between building for today and building for what’s coming.

I almost didn’t go that Saturday. I’m glad I did.