For one of my projects, and honestly, I was kind of blown away. I’ve used Colab for years, right? But this time, it felt different.
There’s a ton of updates, and I’m not going to bore you with every single little detail — like I sometimes do in my other articles.
Instead, I want to just talk about the things that genuinely caught my attention, the stuff that actually makes a difference when I’m coding, learning, or experimenting.
Colab Is Smarter Than Ever
First thing that hit me: Colab is no longer just a notebook. It’s AI-first now, and I can see it everywhere.
- The agentic AI collaborator blew me away. It’s running on Gemini 2.5 Flash, and it’s not just suggesting completions — it actually looks at your code, helps you transform it, analyze datasets, and basically acts like a coding partner. When I tried it, I felt like my workflow doubled in speed.
- For Pro/Pro+ users, you can now access Gemini and Gemma models with a few lines of code — no API keys, no messy setup. I tested it on a small dataset and it handled tasks I would’ve spent hours on, in just a few minutes.
- Even on the free tier, you get inline AI completions, which is insane. It feels like the tool is reading my mind.
- And if you’re a teacher or want to practice without AI interference, there’s a toggle per notebook to turn AI off. I haven’t tried teaching yet, but just knowing it’s there is a relief.
Honestly, this is next-level. I used to think of Colab as a place to just run code; now it’s like having a personal coding assistant.
Power and Performance
Then I started checking out the backend improvements, and wow…
- Trillium TPUs (v6e-1) are insane. They’re almost three times faster than an A100 GPU. I ran a small neural network test and it was done before I even blinked.
- Paid users can choose their GPU, which I didn’t even know I needed until I tried it.
- Picking the right GPU made my training faster and more predictable.
- Python 3.12 and Julia 1.11 with full GPU/TPU support are now standard. I don’t have to fight compatibility issues anymore.
- All the main ML libraries — TensorFlow, PyTorch, JAX, Huggingface — are constantly updated. When I installed my dependencies, everything just worked, no fiddling around with versions.
It’s like Colab finally got serious about actually handling heavy projects, not just quick scripts.
Workflow Got Way Smoother
The thing I love most is how my day-to-day coding feels smoother:
- Interactive Slideshow Mode is amazing for lectures or demos. I tried turning one of my notebooks into a slideshow, and it looked clean, professional, and interactive.
- The terminal access for everyone is wild. I used it to check packages and debug something quickly — no more messy workarounds.
- Google Sheets integration with smart paste works flawlessly. I just pulled some data from a spreadsheet, and it populated exactly how I wanted.
- Kaggle & Huggingface integration is a huge time saver. I literally imported a Kaggle notebook, made a tiny tweak, and ran it in Colab in under 5 minutes. Huggingface models open instantly too.
- Secrets management finally makes storing API keys easy. I don’t have to scatter them across my code anymore.
- Code actions & LSP? Auto-refactoring and better completions are basically my new best friends. My messy code cleaned itself up in seconds.
- Navigation updates — minimizable comments, TOC run/collapse, snippet search — are subtle but life-changing. I can finally keep long notebooks readable.
Honestly, it feels like Colab is finally built around how I actually work, not just what Google thinks I should do.
Learning & Accessibility
I love that they’re thinking about students and teachers:
- Free 1-year Pro for verified students (in some regions) is huge. Even if I haven’t claimed it yet, it shows Google is serious about education
- Paid tiers expanded globally, so more people can actually experience this.
- Accessibility tweaks — keyboard navigation, focus management, color contrast, tooltips — are subtle, but after a day of coding, I noticed how much they reduce friction.
It’s like Colab isn’t just a tool — it’s a platform that adapts to me.
Package Ecosystem
The libraries are constantly refreshed, which is a huge deal for me:
- Core ML/DS libraries — TensorFlow, PyTorch, JAX, Transformers, Keras, NumPy, Pandas — always updated.
- New packages like Gradio, cuML, Diffusers, W&B, Google-GenAI are included without me touching anything.
- Old stuff like PyDrive 1.3.1 is gone, which keeps the environment lean.
When I ran my setup, everything just worked, and I didn’t waste hours hunting for compatible versions. That’s honestly a big relief.
Why I’m Actually Excited
What started as a simple notebook has quietly turned into an
AI-powered, high-performance, interactive playground. For me, the coolest part isn’t any one update — it’s that all of these improvements actually make my life easier.
Smart AI, insane hardware, smooth workflow, fresh packages, and accessibility features all combined.
I honestly felt a little guilty for taking Colab for granted before.
Now? I actually want to explore more, push bigger experiments, and see how far this platform can go.
Next step for me is probably making a full project demo, showing how I personally use these features to speed up work, play with AI models, and keep everything organized.
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