Two weeks into building autonomous product development systems, and we just learned the hard way that even AI can suffer from growing pains. Our system—the one that's been churning out products at breakneck speed—finally hit its breaking point.
When Success Becomes the Problem
Picture this: you've built an AI engine that's so good at understanding context and generating products that you keep feeding it more and more information. Product specs, market research, user feedback, technical documentation—everything gets dumped into this ever-growing knowledge base. It's working beautifully until one day, it's not.
That's exactly where we found ourselves on Day 14. Our system had evolved into a massive monolith, and the sheer volume of context we were pumping into it started causing failures. The AI was drowning in its own success.
The wake-up call came when routine operations that used to take minutes started timing out entirely. We realized we'd created something powerful but unsustainable—a classic engineering tale as old as time.
Breaking Down the Monolith
Rather than panic (okay, maybe there was a little panic), we rolled up our sleeves and got to work on a fundamental restructure. We built a chunking system that intelligently defines what information lives where and how different parts of our system communicate.
Think of it like organizing a massive library. Instead of throwing every book into one giant pile, we created sections, categories, and a card catalog system. Now when an AI agent needs specific information, it knows exactly where to look without having to sift through everything else.
The key breakthrough was creating a parent document—essentially a guide that walks every new agent through our system. It's like having a really good onboarding manual that ensures each AI knows the lay of the land without overwhelming it with unnecessary details.
Meanwhile, We Actually Shipped Something
While wrestling with our technical challenges, we managed to launch one of our older projects (and by "older" we mean from last week—time moves differently when you're building at this pace). firstuser.fit is now live and helping people build playbooks to find their first customers.
It feels good to have something tangible out there, solving real problems while we work on the bigger vision behind the scenes.
The Hiremart Vision Gets Clearer
Our most ambitious project, Hiremart, is proving to be exactly as complicated as we expected—and that's exciting. We're discovering that building a marketplace for AI agents requires a lot more human touch and iteration than we initially thought.
But we had a breakthrough: we figured out how to link skills directly to agents right from Slack. Soon, you'll be able to ask a design agent to create an app mockup in Figma, and an engineering agent will automatically pick up that design and build it out.
This isn't going to be another one-shot platform like Loveable or Bolt. We're building something that creates genuine collaboration between specialized AI agents, which we believe will result in significantly better applications.
The Constant Evolution
We're in the thick of migrating files, restructuring systems, and evolving our approach almost daily. It's messy, it's challenging, and it's exactly where we need to be. Building end-to-end self-improving machines turns out to require constant refinement of the machines themselves.
We currently have three new products in development and we're hoping to ship another one tomorrow morning. The pace hasn't slowed down—we've just gotten smarter about managing the complexity.
Every broken system teaches us something new about building robust autonomous development pipelines. Today's fragility becomes tomorrow's resilience, as long as we keep learning and iterating.