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Widely known as fotiecodes, an open source enthusiast, software developer, mentor and SaaS founder. I'm passionate about creating software solutions that are scalable and accessible to all, and i am dedicated to building innovative SaaS products that empower businesses to work smarter, not harder.
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I opened X (formerly twitter) today and saw my timeline melting down. Everywhere I looked, people were talking about gemini-3.0. Someone said "It's over.", I saw screenshots of benchmarks that looked impossible.
Naturally, I was skeptical. We see hype cycles every week.
But then I saw the numbers, went straight to google aistudio, and decided to try it out for myself. I generated a few 3d simulations here and there, expecting the usual hallucinations or minor layout bugs.
It was just flawless.
The design the model generated wasn't just "okay code", it was almost perfect. I was shocked. I immediately started running app ideas I had tried with the previous Gemini 2.5 Pro. The difference is obvious. The ceiling for what you can build in a single shot has been raised significantly.
Perhaps this really is "over," and we are one step closer to AGI in less than 5 years?
Youtube video here: https://www.youtube.com/watch?v=saJp0McT6Jc
Let's look at the hard data, because "vibes" are great, but numbers tell the story of the model's raw horsepower.
Gemini-3.0-Pro is topping the LMArena Leaderboard with a score of 1501 Elo. For those tracking these numbers, that is a massive jump. But the number that actually made me stop and stare was the performance on ARC-AGI-2.
45.1%.
That’s with code execution (ARC prize verified). This benchmark tests a model’s ability to solve novel challenges, things it hasn't memorized. Scoring that high demonstrates a level of reasoning and adaptability we haven't seen before. It isn't just reciting documentation; it's actually figuring things out on the fly.
It also crushed:
WebDev Arena: 1487 Elo (Top spot)
SWE-bench Verified: 76.2% (Coding agents)
MMMU-Pro: 81% (Multimodal reasoning)
These aren't incremental gains. This is a step-change.
To really test this, I went back to a technique I covered in a previous blog post. The red box trick with nano banana, you can find the article here.
The technique used a specific prompting strategy to edit photos with google's nano banana model. I decided to apply that same logic to build an actual web app that can be used, tried it with both gemini-2.5-pro and gemini-3.0-pro.
I used that technique and built fully functional web apps with a single shot prompt.
Caption: ui generated by gemini-2.5-pro
Caption: ui generated by gemini-3.0-pro, one can drag the line in the middle, from left to right to reveal the edited image and the original image
Look at how the UI generated with gemini 3 looks nice and clean. The spacing, the responsiveness, the logic. I didn't touch a single line of code.
Undoubtably, gemini-3 understood the intent immediately. It didn't need five follow-up prompts to fix the margins or debug the state management. It just worked! plus the way it implemented the features is just so nice done compared to gemini-2.5-pro.
Google calls gemini-3 their "best vibe coding and agentic coding model yet," and for once, the marketing matches the reality.
In my testing today, the "AI smell", that clunky, bootstrap-heavy look that usually plagues generated apps is gone. Gemini-3.0-Pro handles complex prompts and instructions to render richer, more interactive web UIs.
The official release mentions it hits 54.2% on Terminal-Bench 2.0. This means it’s not just writing code; it’s capable of operating a computer via terminal. This opens the door for agents that don't just suggest code but actually go implement it, test it, and fix it.
There is also a new mode called gemini-3 deep think.
This is google's answer to "thinking" models. It outperforms the standard pro model on humanity’s toughest tests, like GPQA Diamond (93.8%).
While the standard pro model is fast and incredibly capable, deep think is designed for when you hit a wall. It peels apart the layers of a difficult problem. It’s built to grasp depth and nuance.
I haven't had deep access to this specific mode yet (it's coming to Ultra subscribers soon), but if the standard gemini 3 pro is already this good at zero-shot generation, deep think is going to be a weapon for complex architecture and research tasks.
This is the part that developers need to pay attention to. Google is releasing Google Antigravity.
This is an agentic development platform. It’s not just an IDE with a chat window. It elevates agents to a "dedicated surface."
What does that mean? It means the agent has direct access to the editor, the terminal, and the browser. It can:
Plan a complex task.
Execute the code.
Validate the code.
Debug its own errors.
It’s tightly coupled with gemini-2.5 computer use model for browser control and, crucially, that nano banana image editing model I mentioned earlier.
We are moving from "AI as a tool" to "AI as a coding partner." You aren't just asking for a function; you're assigning a ticket to a junior dev who actually knows what they're doing.
One of the biggest failures of previous models was "drift." You'd give an agent a long-term task, and by step 5, it would forget what step 1 was about.
Gemini 3 seems to have fixed this. It tops the leaderboard on vending-bench 2, which tests long-horizon planning. In simulations, it maintained consistent tool usage for a "full simulated year" of operation.
This means you can trust it with multi-step workflows, booking services, organizing complex data, or managing a repo without constantly babysitting it.
I was ready to be underwhelmed. I was ready to say "it's just another model."
It’s safe to say i was wrong.
Gemini 3 is a massive leap. The speed combined with this level of intelligence is transformative. When I look at the ARC-AGI benchmark and then look at the app I just built in 30 seconds using the red-box trick, it feels different this time.
The friction is gone. The gap between "idea" and "working software" has basically evaporated.
If you are building products, you should consider the switch to gemini-3. The teams that adopt this, especially the new antigravity workflows will simply out-ship everyone else i believe.
We might actually be looking at AGI in the rearview mirror sooner than we think.
Go try it in aistudio. now.