Zo + Open Source AI: I Built 3 Real Tools to See What’s Possible

I’ve been saying it for a while now: open source AI models are catching up fast. But after spending a few weeks actually *building* with three of the newest ones – MiniMax M2.5, Kimi K2.5, and GLM-5 – I can say they’re not just catching up. For everyday tasks, they’re genuinely competitive with the frontier models I’ve been paying for.

In this post, I’m walking through three real tools I built from scratch on the Zo platform, each using a different open source model and a single prompt. Practical builds that show you what these models can actually do.

Why I’m Using Zo for This

These open source models – MiniMax, Kimi, GLM – don’t have the same plug-and-play API access as the frontier models. You need somewhere to run them, and that’s where Zo comes in.

[Zo](https://zo.computer) is a single workspace for hosting web apps, running AI models, and building tools. What drew me to it wasn’t just the model hosting – it’s that you can go from prompt to deployed app without stitching together a separate frontend, backend, and hosting stack. Everything lives in one place.

Right now, they’re offering free access to all three models, which made it easy to run real builds without worrying about burning credits. I’m on the basic paid plan, but the free tier gives you enough runway to figure out what these models can actually do.

Build #1: Link-in-Bio Page (MiniMax M2.5)

For the first test, I wanted something simple – a link-in-bio page hosted directly on my Zo space. I gave MiniMax M2.5 a single prompt with all my links (Stack Snacks, newsletter, YouTube, etc.) and told it to build the page.

**How it went:** There was a little bit of funkiness getting it to render initially. The first attempt failed, and I had to restart the chat. But once it got going, it built a clean, colorful link-in-bio page that absolutely gets the job done. It’s not going to win any design awards, but for a one-prompt build? Solid.

I also ran the same prompt through GLM-5 and Kimi K2.5. GLM-5 noticed the existing page and basically cloned it, while Kimi K2.5 was so fast it had the page up before I could even check the preview. The speed on all three models was impressive, even if the link-in-bio use case was too simple to really differentiate them.

**Verdict:** All three models handled this basic task without breaking a sweat. The real differences showed up in the next builds.

Build #2: Credit Card Rewards Calculator (Kimi K2.5)

This is where things got interesting. I wanted to see if Kimi K2.5 could do something more complex: research a brand-new credit card (the Robinhood Platinum), pull all the benefits and perks, and build me an interactive calculator to figure out if the annual fee is actually worth it.

One prompt. That’s it.

**How it went:** Kimi K2.5 went to work immediately. It ran a bunch of web searches, read through multiple articles about the Robinhood Platinum card, compiled all the offers into a markdown file, and then started building the calculator. The whole process took just a few minutes.

The result was a fully interactive tool where I could plug in my estimated spending across categories – travel credits, hotel credits, DoorDash, dining, flights, car rentals, everything else – and see whether the card’s annual fee pencils out based on my actual habits.

After running my numbers, the calculator showed me a net annual value and even compared it against the Amex Gold card. It’s exactly the kind of quick analysis I’d normally spend 30 minutes doing manually in a spreadsheet.

**Verdict:** Kimi K2.5 impressed me the most here. It handled the web research, math, and UI building in one shot. Fast, accurate, and genuinely useful output.

Build #3: AI App Tracker Database (GLM-5)

For the final build, I wanted to test GLM-5 on something with more of a coding challenge – a database-driven app. The prompt was simple: build me a tracker for all the AI apps I’m using, what I’m paying for, and a list of things I want to try. I asked for a table with tags and told it to pre-populate some of the major AI tools.

**How it went:** GLM-5 took a different approach than the other models. It built a proper little API to store the data, then created a front-end page to consume it. It even added authentication, which I hadn’t asked for – a nice touch for anything that might eventually hold real data.

The final result was a clean table interface with tags like “want to try,” “paid for,” and “stopped.” Each entry could be edited inline through an edit panel at the top of the page. You could delete entries, update statuses, and open the actual tools directly from the tracker.

Was it perfect? No – the edit functionality needed a small nudge. But for a single-prompt build that included a backend API, a JSON data store, and a responsive front-end? That’s remarkable.

**Verdict:** GLM-5 is the coding powerhouse of the group. It handled the most technically complex task without breaking a sweat.

My Final Rankings

After a few weeks of using all three models regularly, here’s where I’ve landed:

**Kimi K2.5** is my pick for general-purpose work. It’s fast, handles research tasks well, and produces clean output across a range of use cases. This one has become part of my daily workflow.

**GLM-5** is the one I reach for when I need something more technically complex. If the build involves APIs, databases, or heavier code, GLM-5 handles it with confidence.

**MiniMax M2.5** is solid across the board, but didn’t stand out as strongly as the other two in my testing. Still absolutely worth having in the rotation.

The Bigger Takeaway

Here’s what really changed for me: these models shifted *how* I allocate my AI usage. I’m now using open source models for the everyday stuff – quick tools, simple builds, research tasks – and saving my frontier model tokens (Claude, GPT-4, etc.) for the complex work that truly needs them.

If you’re a solopreneur, creator, or small business owner, this is huge. You can build real, functional tools without burning through expensive API credits or paid subscriptions. The quality gap between open source and frontier models is shrinking fast, and for a lot of daily tasks, it’s already negligible.

Try It Yourself

Head over to [Zo](https://zo.computer) and sign up for free access while it’s still available. Pick a small tool you’ve been meaning to build – a calculator, a tracker, a simple landing page – and throw a single prompt at one of these models. I think you’ll be surprised at what comes back.

If you want more AI tool breakdowns like this, check out the [Stack Snacks newsletter](https://www.stack-snacks.com/) or subscribe to the channel on YouTube.

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*Which of these three models are you most interested in trying? I’d love to hear what you’d build with them.*