Google, my favourite tech firm for reasons exactly as this one, has done it once again. It has got the worldwide community of developers supercharged with one new product. This one is called Gemma 4.
What’s the hype? Well, a completely open-source model that competes with AI models 20 times its size. And this one isn’t just your regular AI chatbot. It has been custom-built for advanced reasoning and agentic workflows. Meaning, AI handles your entire tasks, on your system, even without the need for the internet.
Your personal LLM, if you will.
Of course, that was enough to get AI-savvy people across the world to try their hands on it. And the results are nothing short of revolutionary. Here, I share a list of some of the top such projects, which are simple yet effective use cases that people have managed to bring to life, all thanks to Gemma 4.
But before we dive in, here is a little about the new AI model by Google for those unaware.
Gemma 4: An Open-source AI Revolution
As I mentioned, Gemma 4 is not just another model you open for chat and close five minutes later. Google calls it its most intelligent open model family yet. And all this firepower is meant to think through multi-step tasks, work with tools, generate code, and run on your own hardware. That alone is enough to make the developers sit up straight.
And then comes the part that really fuels the hype: Google says Gemma 4 delivers unusually high intelligence for its size. It comes in 4 sizes, with the larger models ranking among the top open models in the world while competing with systems far bigger than them. That means developers are suddenly getting a model that feels powerful, flexible, and actually usable for real projects. Open, multimodal, agent-ready, and light enough to run in places where frontier AI usually does not. That is exactly why Gemma 4 is starting to feel less like a model release and more like a shift.
You can learn all about the new Gemma 4 here.
For now, we shall look at how developers around the world are putting the capable model to use.
1. Run Claude Code with Gemma 4 for Free
This was a proper “wait, you can do that?” moment for me.
A developer showed how to use Claude Code coding workflows with Gemma 4 running locally on your machine. Which basically means you get Claude’s coding assistant on your own laptop, without paying per prompt and without constantly depending on the cloud. The setup uses Ollama to run Gemma 4 locally, and the tweet frames it as a beginner-friendly process that takes roughly 15 minutes on a laptop.
Why is this cool? Because it turns Gemma 4 from “another AI model release” into something instantly practical. Instead of treating AI like a chatbot tab you open and close, you can plug it into a coding workflow and let it help with writing, fixing, and understanding code right on your system. And yes, the whole appeal here is exactly what got people hyped about Gemma in the first place: no subscriptions, no API key drama, more privacy, and much more control.
What is happening here?
In very simple terms:
- Claude Code = the coding workflow/interface people like
- Gemma 4 = the brain providing the coding help
- Ollama = the engine that runs the model locally on your laptop
The basic setup looks like this
- install Ollama
- download a Gemma 4 model suited to your machine
- install Claude Code in VS Code
- connect Claude Code with Gemma 4 and start coding locally
2. Run Gemma 4 on an iPhone, Completely Offline
When I said ‘your personal LLM’, this was the Gemma 4 project I was referring to.
Imagine an AI model in your pocket. No internet, no cloud connection, and no monthly fee. Sharbel on X showed just that – Gemma 4 running directly on an iPhone. That means the AI model is not sitting on some remote server waiting for your request. It is right there on the phone, handling tasks locally like a pocket-sized brain.
The flow is simple and wild at the same time:
- download Locally AI
- find Gemma 4 under the ‘Manage Models’ option
- download it and use it for on-device reasoning and tasks
That opens the door to all kinds of personal AI experiences. Think private assistants, offline study tools, local note analysis, or even agentic workflows on the go. And that is exactly why Gemma 4 has people so excited.
3. Run Gemma 4 on a Nintendo Switch
In case your local LLM on your iPhone wasn’t enough, here comes Gemma 4 running on a Nintendo Switch. Yes, an actual gaming console. maddiedreese shared Gemma 4 running locally on the device at around 1.5 tokens per second. That speed is obviously not built for high-pressure office work, but that is not the point here. The point is that a modern multimodal, agent-ready model can now be squeezed into places where AI was never really expected to live.
And that is exactly why this use case hits so hard. The workflow itself is simple in spirit:
- take a compact Gemma 4 model
- optimise it enough to run on weaker hardware
- load it onto the Switch locally
- use the console as a tiny offline AI machine
Gemma 4 is making one thing very clear here: powerful AI is leaving the cloud and entering personal devices in all kinds of bizarre, wonderful ways. At this rate, developers are basically treating every screen around them like a potential home for an LLM.
4. Use Gemma 4 for Offline Audio Transcription on a Phone
This is where things start getting seriously fun. ai_for_success showed Gemma 4 E2B being used for audio transcription on a Pixel 10 Pro. In plain English, that means your phone can listen to a short audio clip and turn it into text, locally, without needing a big cloud setup that sends every request back and forth. The post notes that it supports up to 30 seconds for now, which may sound small, but honestly, even that is enough to show where this is heading.
Why is this exciting? Because it takes AI out of the “chatbot box” and turns it into something your device can do in the real world. The flow is beautifully simple:
- record or feed in a short audio clip
- let Gemma 4 E2B process it on-device
- get the spoken words back as text
- all without depending fully on the internet
Imagine the possibilities it opens up: quick note-taking, voice memos, meeting snippets, lecture highlights, or even just converting your random burst of genius into text before it disappears. It is not a full-blown studio transcription yet. But as a glimpse of what small, local AI can already do on a phone, this is absolutely wild.
5. Turn a Mac Studio into Your Own Zero-Token AI Workhorse
This one is pure power-user energy. jessegenet shared Gemma 4 31B running on a Mac Studio, hooked up to OpenClaw, and the line that really jumps out is this: “$0 in token expenses now.” That is the dream, isn’t it? A serious local AI setup that can chat, reason, and run workflows on your own machine, feeling that constant token-ticking in the back of your head.
What is happening here is actually very simple:
- Mac Studio = the muscle
- Gemma 4 31B = the brain
- OpenClaw = the workflow/operator layer
- Result = a local AI assistant that feels much more like your own system than a rented chatbot
Why this is such a big deal: most people experience AI through a website or app. This setup flips that completely. Instead of going to the AI, the AI lives with you, right on your machine. Ready for longer chats, custom workflows, private work, and repeated use without per-prompt pricing pressure from a hosted provider. That is when Gemma 4 starts looking less like “another model launch” and more like the beginning of a proper personal AI workstation.
6. Turn Gemma 4 into a Real-Time Vision Assistant in Your Browser
This one is much like a full-time AI assistant that is way smarter than the standard AI chatbots you use every day. measure_plan built an app that combines Gemma 4’s vision capabilities with Roboflow’s RF-DETR. The result is a browser-based setup that can look at what your camera sees and make sense of it in real time. We can learn from the post that Gemma handles the actual understanding, while RF-DETR does the first-pass object detection. In other words, one model spots what is in the frame, and the other explains what is going on.
That combo opens up a lot of fun possibilities really fast:
- RF-DETR finds the objects in the scene
- Gemma 4 interprets those objects and adds context
- the whole thing runs live in the browser on a local machine
The super-cool project shows Gemma 4 doing way more than chatting or coding. It is starting to act like a visual brain. Point your camera somewhere, and the system can begin identifying what is there, following the scene, and describing it back in the language of your choice. Now imagine such a system as an assistive tool or a smart camera app that helps guide you through a process that is completely new to you. The possibilities are simply wild.
7. Make Gemma 4 Handle Real-world Tasks to Start Your Day
Imagine an AI that checks your calendar at the start of the day, and then sends messages that need to be sent to your contacts, without you even typing a word. OsaurusAI created exactly this in a project with Gemma 4 26B. Running locally at around 50 tokens per second, the AI was able to read a calendar and text contacts. That is a big jump from “AI can chat” to “AI can actually do things for me.”
The idea is simple:
- Gemma 4 does the thinking
- your apps like Calendar and Messages provide the data
- the AI acts like a proper assistant on top of them
Why this matters: once a model can move this fast locally, it stops feeling like a demo and starts feeling like a real personal agent. The kind that can check your schedule, find the right person, and help you take action instantly. All of this, without sending every little request to the cloud.
8. Make Gemma 4 Audit an Entire Code Repository on a Tiny Setup
This is the kind of demo that makes developers grin. UnslothAI showed Gemma 4 E4B (4-bit) completing a full repo audit by executing Bash commands and tool calls locally. The wild part is that it reportedly runs on just 6GB RAM. That is not “AI writes one helper function.” That is AI stepping through a real codebase, using tools, and helping inspect the whole thing, just like a mini coding agent on your own machine would.
The setup is beautifully simple:
- run a compact Gemma 4 model locally
- give it access to basic tools like Bash
- let it inspect files, move through the repo, and reason over the code
- get a code audit without needing a giant cloud setup
This one is much more relatable as it shows Gemma 4 doing actual developer work, not just code autocomplete cosplay. And the fact that it can happen on such modest hardware is exactly what makes Gemma 4 feel so disruptive. Powerful AI is one thing. Powerful AI that fits into ordinary machines is a revolution in itself.
9. Turn Gemma 4 into an Actual On-Device Agent with Agent Skills
This one is a useful feature that Google itself introduced along with the Gemma 4. Omar Sanseviero, who is the Developer Experience Lead at Google DeepMind, announced Agent Skills for Gemma 4 on X recently. Much as the name suggests, Agent Skills work exactly like the skills we have seen with Claude or other AI models. It is an Android app experience launched with Gemma 4, where you can import different skills and let Gemma 4 E2B reason through and use them directly on-device. That means your phone is not just chatting back. It is starting to behave more like a real local agent.
What makes this exciting is how simple the idea is:
- load skills into the app
- let Gemma 4 understand the task
- have it use those skills step by step
- all locally on the device
Agent Skills takes Gemma 4 beyond chatbot territory and into something much more useful: AI that can actually do things on your phone, not just talk about them. And because it runs on-device, it also pushes the whole “personal AI” idea much closer to reality.
10. Make Gemma 4 Turn Images into Songs
I’ve kept the most fun for the last. Once you are done using the new Gemma model for all your work, it is time to have some fun with it. ai_for_success, in his X post, shares how to do just that. He built an agent skill that lets Gemma 4 E2B call Lyria 3 and generate songs. Yes, actual songs. The post says it works for image-to-song, which means you can show the system a visual, let Gemma understand it, and then have it trigger music generation around that vibe.
The flow is super simple:
- give it an image
- let Gemma 4 understand what is in it
- use the agent skill to call Lyria 3
- get a song inspired by that visual input
Why is this such a cool final example? Because it shows Gemma 4 doing what all great agentic models should do: not just answer prompts, but help create something new. One minute, it is reading images. The next minute, it is making music out of them. That is a creative that shows a lot of human touch to it.
Also Read:
Conclusion
These projects show exactly why Gemma 4 feels bigger than a normal model launch.
From coding assistants and offline iPhone LLMs to video understanding, repo audits, agent skills, and even image-to-song generation, developers are already stretching it in all directions. Practical, or for pure fun, Google’s new launch has become the go-to AI model within days of its launch. And all of this, for one very potent reason – it runs locally, all for free.
Such widespread traction early on is usually the clearest sign that a product has landed well. People do not just test it, they start building with it. More importantly, Gemma 4 is showing what the next phase of AI could look like: more personal, more local, more controllable, and far less dependent on giant cloud setups for any of your projects.
Of course, these are the early experiments. The real wave of Gemma 4 projects may only just be getting started. So make sure you stay tuned to this space for more such updates on the new Gemma model.
Login to continue reading and enjoy expert-curated content.
