💡 A good idea shouldn’t stay buried in your notes just because you don’t know how to code.

We want more people to be able to build their own products. Over the past few months, we’ve been working on a tool designed to make that path clearer and more accessible.

🤖 AI has already changed how our team works. We use it for development, research, and routine tasks. It helps us learn unfamiliar technologies faster and take on projects we previously lacked the resources to pursue.

As the tasks get more demanding, the limitations become harder to ignore.

An agent can start confidently, miss an important requirement, get stuck fixing the same mistake, or stop just short of a usable result. Someone has to step back in, understand what happened, and get the work moving again.

💸 Our team spends more than $1,000 a month on AI. We feel the cost of those interruptions in both money and time.

We needed an efficient, affordable process that could stay reliable through long, complex tasks.

🧩 So we began building a team of specialized agents. One clarifies the requirements. Another creates a plan. A third implements the solution. The next checks the result. Each has a defined role, its own instructions, and clear expectations.

Getting this right took a lot of experimentation. We rewrote instructions, changed the sequence, and studied failed attempts. Gradually, we found an approach that started producing good results on our own projects.

We’ve already used it to complete real work.

But coordinating that team was still a manual job. We had to launch agents from the command line, pass their outputs between them, and make sure each one received everything it needed.

🧪 We tried existing tools for connecting agents into workflows. Using an existing engine, we also built our own benchmarking service to compare results and understand which models performed best in each role.

Yet we kept running into bugs, difficult configuration, and interfaces that made it hard to understand what was actually happening. There were plenty of tools available, but none of the options we tried gave us the complete experience we wanted.

🛠 Eventually, we decided to build our own engine—and our own development studio around it.

We wanted a studio where someone could inspect every step through a clear interface. Someone else could simply describe their goal, answer the necessary questions, and work toward a finished result without having to follow every technical detail.

The studio will let users assemble a workflow around their task: choose agents, give them tools and instructions, define how they work together, and set the checks their outputs must pass.

🔍 The process should remain understandable whenever a person wants to look closer.

Who is working. What information they received. What they produced. Why they stopped. Which checks passed, and where someone needs to step in.

We’re designing the studio to make complex work transparent—from the initial brief to usable files and changes in a project.

🏗 Right now, we’re building its core engine. And we’re developing the studio with the same agents we plan to bring together inside it. Every day, we use our approach on real work and discover what still needs improvement.

The MVP is still in development. Over the next few weeks, we aim to get it ready for its first end-to-end trials. Then we’ll share more: real tasks, actual results, and the limitations we still need to address.

After those trials, we’ll decide how to make it available—as an application or a service people can use through a website.

🎓 We have big plans. We want to bring development and learning together so people can build increasingly complex things while understanding how they work.

Perhaps you also have an app, a service, or a project you’ve wanted to create for a long time.

🚀 We’re working to shorten the distance between “I have an idea” and “people can actually use it.”