Coder Legion - Recent posts tagged googleiochallenge

I Tried Gemini 3.5 Flash After Google I/O 2026 - Here is What I Found :))

Sat, 13 Jun 2026 06:05:18 +0000

So Google I/O 2026 happened and honestly I was not expecting this much. Like every year I sit and watch the keynote thinking "okay this time it will be boring" but no, they surprised me again. <3

This time the big thing is Gemini 3.5 Flash and also a new model called Gemini Omni. I spent some time playing with Gemini 3.5 Flash after the announcement and wanted to share my honest first look kind of thing.

What Even is Gemini 3.5 Flash?

So basically Google is saying Gemini 3.5 Flash is their smartest Flash model till now. The Flash series was always the fast and cheap one, right? But this time they are saying it is also crossing 3.1 Pro in benchmarks for coding and agentic tasks. And it is 4x faster than other frontier models in output tokens per second.

That is a big claim. So I tried it.

The speed part is actually true, I can say that much. It feels noticeably snappy. Like when you are doing back and forth with it , asking follow ups, correcting it , it does not make you wait much.

How to Get Started With It

If you want to try yourself, it is quite simple actually.

Step 1 : Go to Google AI Studio

Just open aistudio.google.com. You need a Google account, that is it. No waiting list or anything like that for Flash.

Step 2 : Select the Model

On the top there is a model dropdown. Select gemini-3.5-flash. It is available from today itself, they said it in the keynote.

Step 3 : Try a Prompt

Start with something simple. I tried giving it a small React component to write and then asked it to add error handling also. It did both in one shot, which was nice.

Step 4 : Try it via API also

If you want to use it in your own project, the Gemini API supports it now. Here is a basic example:

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")

model = genai.GenerativeModel("gemini-3.5-flash")
response = model.generate_content("Explain async await in Python in simple words")

print(response.text)

Very straightforward. You can get the API key from AI Studio itself, there is a button called "Get API key" in the left sidebar.

What I Actually Liked

The coding part is genuinely better. I gave it a messy function with some bugs and asked it to fix and also explain what was wrong. It found three issues I had not even noticed. Previous Flash models used to miss the subtle ones.

It understands context well. In a long conversation it does not forget what you said earlier. I was testing a multi-step debugging scenario and it remembered the original code I shared even after 6-7 messages.

Speed is actually fast. I know I said this already but seriously, for something this capable it is very fast. Other frontier models you can feel the thinking pause. This one is much snappier.

What I Did Not Like So Much

Honestly the output length can be a bit much sometimes. You ask a simple question and it gives you a full essay. I had to keep saying "give shorter answer" which is a little annoying.

Also Gemini 3.5 Pro is apparently coming next month, not today. So right now Flash is the best available in the 3.5 series. Pro would probably be more powerful for complex reasoning tasks.

What About Gemini Omni?

This one is different category. Gemini Omni is not just text , it takes image, audio, video, and text as input and can output video. Yes, video output. I have not tested it fully yet because access is more limited, but the demo they showed at the keynote was quite impressive. It is more of a creation model.

For regular developer work Gemini 3.5 Flash is the one to focus on right now.

Should You Try It?

Yes, just go try it. It is free to use in AI Studio for basic usage. The API has some free tier also. If you are building anything with AI :- agents, coding tools, chatbots , Gemini 3.5 Flash is now genuinely a strong option, not just a cheap fallback.

The agentic stuff Google is building around it with Antigravity platform is also interesting but that is a separate topic altogether. Maybe next post.

For now, just open AI Studio and play around. That is the best way to see for yourself.

Google I/O 2026 Wasn’t About AI Models — It Was About Infrastructure

Thu, 04 Jun 2026 15:04:22 +0000

This is a submission for the Google I/O Writing Challenge

Google I/O 2026 introduced major advancements across Gemini, AI tooling, Android, Firebase, and developer ecosystems. But while most discussions focused on AI features and demos, I believe the real story was infrastructure.

As someone who works with backend systems, asynchronous workflows, and AI orchestration pipelines, the most important signal from this year’s event was architectural: AI is becoming an execution layer embedded directly into modern software systems.

Google I/O 2026 Wasn’t About AI Models — It Was About Infrastructure

Every major Google I/O follows a pattern. Announcements arrive. Demos impress. Developers experiment.

But every few years, something deeper shifts.

This year, that shift was not a model release. It was not a new API or a chatbot improvement.

It was infrastructure.

The Contrarian Thesis

Most developers watched Google I/O 2026 and saw:

Gemini integrations across Workspace
AI tooling in Firebase
Smarter Android features
Agent capabilities

All of that is real. All of it matters.

But the pattern underneath those announcements is what fascinated me as a systems engineer.

Google is quietly building an AI-native execution layer.

Not just LLM APIs. Not just chat interfaces.

An orchestration fabric that weaves intelligence into distributed systems the way databases and message queues became standard infrastructure.

What Infrastructure Actually Means Here

When I say “infrastructure,” I am not being abstract.

Consider traditional backend infrastructure:

HTTP routing
Request/response cycles
Database transactions
Queue-based workers
Container orchestration

Now watch what Google I/O 2026 revealed about AI infrastructure:

Traditional	Emerging AI Infrastructure
Fixed execution paths	Dynamic agentic workflows
Deterministic responses	Probabilistic reasoning
Human-written logic	Model-decided routing
Batchable workloads	Real-time orchestration

During Google I/O 2026, Google demonstrated Gemini integrations spanning Workspace, Android, Firebase, and developer tooling ecosystems. What stood out was not any single feature, but the architectural direction connecting them together.

The demos were not isolated features. They were glimpses of a unified execution model.

The Orchestration Problem Nobody Is Talking About

Here is where my engineering background shapes this analysis.

I have worked with FastAPI microservices, asynchronous systems, and Docker-based execution environments. I know what orchestration looks like in traditional systems.

AI-native systems introduce a fundamentally harder problem.

You cannot hardcode agent behavior the way you hardcode an API endpoint.

Instead, you need:

Dynamic workflow routing
Context propagation across model calls
Fallback and retry strategies for nondeterministic output
Observability into reasoning chains
Cost-aware model selection

Google I/O 2026 hinted at solutions to every single one of these problems.

That is why infrastructure matters more than any single model release.

Beyond the Chatbot Era

We spent the last few years assuming AI = chatbot or AI = API call.

The I/O 2026 announcements suggested something different.

AI as embedded cognitive infrastructure.

Not a feature you add. A layer you build on top of.

Firebase AI tooling hints at this. Gemini’s system-level integrations hint at this. The agent tooling shown during keynotes hints at this.

Google is not just shipping products. They are shipping an architectural transition.

What This Means for Engineers

If AI infrastructure becomes as foundational as databases and APIs:

Backend engineers will need to understand probabilistic execution
Orchestration systems will evolve to handle agentic workflows
Observability will expand beyond traces to reasoning paths
Cost modeling will include inference budgets alongside compute

This is not theoretical. I have already started encountering these requirements in real AI workflow systems.

Google I/O 2026 validated that direction.

One Bold Prediction

Within five years, AI orchestration layers may become as fundamental to software architecture as databases and APIs are today.

Not because every app needs AI.

But because systems that do need AI will demand standardized infrastructure — and Google is positioning itself at that layer.

Final Thoughts

The future AI stack may look less like traditional applications and more like distributed cognitive infrastructure.

The question is no longer whether AI will change software engineering.

The question is whether we are ready to engineer systems where intelligence itself becomes infrastructure.

What direction from Google I/O 2026 stood out most to you?

Thanks for reading. If you work on orchestration, AI workflows, or distributed systems, I would love to hear your take in the comments.

My Dad Got an Electricity Bill He Couldn't Understand. Google I/O 2026 Just Made That Problem Solvable.

Wed, 03 Jun 2026 11:36:05 +0000

This is a submission for the Google I/O Writing Challenge

The Moment That Started Everything

Last year my dad got our electricity bill and just stared at it.

He couldn't understand why it was so high. He had no idea which device was the problem. Was it the iron my mum uses every morning? The old fridge that runs all night? The TV nobody turns off?

He just paid it. Like he always does. Because there was no easy way to find out.

That moment stayed with me. So I built something about it, an app where you point your camera at any appliance, pinch your fingers, and instantly see what that device costs you per month in your local currency, what it does to your CO₂ footprint, and one simple habit to cut the cost immediately.

The heart of it was Gemini's vision capability. You point. Gemini looks. Gemini understands. No typing. No forms. Just a camera and a pinch gesture.

It worked. But building it taught me exactly where the limits were.

And then Google I/O 2026 happened.

What I Was Actually Asking Gemini to Do

When you point a camera at an appliance, you are not giving Gemini a clean, labelled image. You are giving it a live camera frame, often at an angle, often in bad lighting, often partially obscured by a cable or a counter.

Gemini 2.0 Flash handled it remarkably well. It could identify an iron, a refrigerator, a standing fan, a microwave, even when the image was not perfect. It returned structured data: what the device is, how many watts it typically consumes, and what that costs per month in Nigeria, Ghana, or Kenya.

But there were moments where it hesitated. Older appliances with no visible branding. Devices that looked similar from certain angles. Situations where the model was confident but slightly wrong, calling a dehumidifier an air purifier, for instance, which changes the wattage estimate significantly.

Those were not failures. They were the edges of what vision AI could do at the time.

What Gemini 3.5 Flash Changes

Gemini 3.5 Flash improves both multimodal reasoning and response speed, which matters enormously for real-time camera experiences. DEV Community

For a real-time camera application, that speed difference is not a convenience. it is the difference between an experience that feels instant and one that makes the user wait.

But the multimodal improvement matters more than the speed. Gemini 3.5 Flash is better at understanding what it is actually looking at in complex, real-world visual contexts. Not studio images. Not clean product photos. Real environments with bad lighting, odd angles, and ambiguous objects.

That is exactly the problem I was trying to solve.

An old Nigerian refrigerator from the 1990s does not look like the refrigerators in most training datasets. A locally assembled electric cooker does not have a recognisable brand logo. The visual diversity of household appliances across Africa, India, Ghana, and Kenya is enormous and the gap between what a model trained on Western product images knows and what actually exists in these homes is real.

Gemini 3.5 Flash's improved multimodal understanding closes that gap. Not completely. But meaningfully.

What Gemini Omni Opens Up

Gemini Omni is a new series of models that combines Gemini's reasoning capabilities with creation, accepting image, audio, video, and text input and outputting video grounded in real-world knowledge. DEV Community

This one stopped me.

Imagine pointing your camera at your kitchen and instead of scanning one appliance at a time, the model watches a short video of your kitchen, identifies every device it sees, and generates a complete energy audit, total monthly cost, total CO₂ footprint, priority recommendations, all in one pass.

That is not a feature I could have built before. That is a feature I could build now.

The pinch-to-scan interaction I built works well for focused, one-device-at-a-time scanning. But the real problem my dad had was not "tell me about this one device." It was "Tell me why this whole bill is so high." Video input that generates grounded output is the architecture that answers the actual question.

The Announcement That Surprised Me Most

Google announced Antigravity 2.0 with new capabilities to orchestrate and build agents, transitioning from AI that simply assists to agents that can independently navigate complex tasks.

When I built my energy scanning app, every interaction was stateless. You scan a device, you get a result. If you scan another device, you get another result. The dashboard accumulated data, but the model had no memory of what it had already seen.

An agent that can remember that knows you scanned the fridge last week, that it already recommended you switch to a more efficient model, that it is now tracking whether your bill actually went down, that is a fundamentally different product.

That is not a scanning tool. That is an energy advisor that lives on your phone and actually follows up.

What This Means For Developers Building in Africa

I want to say something that the Google I/O coverage mostly missed.

Gemini 3.5 Flash delivers frontier-level capabilities at less than half the price of comparable frontier models. LinkedIn

For developers building in Nigeria, Ghana, Kenya, India, where margins are thin, where users cannot pay Western subscription prices, where the business model has to work at a completely different cost structure, that pricing difference is not a footnote. It is the difference between a viable product and one that cannot sustain itself.

Most of the Google I/O coverage talked about what these models can do. The more important story for developers outside Silicon Valley is what these models now cost to run. Frontier vision capability at accessible pricing means the gap between what developers in Lagos can build and what developers in San Francisco can build just got smaller.

That is the announcement I cared about most.

What I Am Building Next

My dad still checks his electricity bill every month and wonders where the money went.

With Gemini 3.5 Flash's improved multimodal understanding, Gemini Omni's video input, and Antigravity 2.0's agentic memory. I have everything I need to build the version of this tool that completely solves his problem.

Do not scan one appliance at a time. Walk through the house. Let the agent watch. Get the full picture. Follow up next month.

Google I/O 2026 did not just announce new models. For developers who are building real tools for real people in the places the tech industry usually forgets, it has moved the frontier to where we actually are.

The appliance scanning app referenced in this post was built independently for the DEV Earth Day Weekend Challenge. All Google I/O 2026 details are from official Google announcements.