Three months ago, we set out to build an AI system that could discover market opportunities and ship products autonomously. Today, we're sitting on 300+ ideas in our pipeline with 90 products actively in development. We've shipped about 10 of them, including some we're genuinely excited about like haystax.work, feedbackflow.cc, and asciify.cool.
On paper, this looks like incredible progress. Our system can generate ideas, validate them, and ship functional products faster than we ever imagined. Each loop makes it more autonomous, more efficient, more capable.
But here's the thing we discovered this week: we were optimizing for the wrong metric.
The Speed Trap We Fell Into
When your system can build and ship at breakneck pace, it's intoxicating. Every day brought new prototypes, new features, new possibilities. We were addicted to the velocity, convinced that faster meant better.
But as we looked at what we were actually producing, a uncomfortable truth emerged: speed without deep product clarity was just giving us better prototypes, not better products.
Despite all our automation, too much of what came out of the pipeline felt like... well, slop. Functional slop, polished slop, but slop nonetheless. The human touch wasn't just nice-to-have—it was absolutely critical for turning working code into something people actually wanted to use.
The Week We Hit the Brakes
So this past week, we made a counterintuitive decision: we slowed down.
More time on documentation. More time figuring out the breaks in our pipeline. More time thinking holistically from initial idea all the way through to end product experience.
It felt wrong at first. When you've built a machine optimized for speed, deliberately adding friction feels like going backwards. But we realized we weren't just building products—we were building a product development system. And like any system, the quality of outputs depends entirely on the quality of inputs.
Redefining Success: Better First Builds
Our new north star isn't faster builds—it's better first builds.
If we can get the inputs strong enough, a single pass through our system shouldn't produce a prototype that needs weeks of iteration. It should produce a viable product that you can immediately start getting customers with.
This means investing more upfront in understanding the problem space, defining clear success metrics, and building robust product specifications. It means our AI needs to think like a product manager, not just an engineer.
The goal is ambitious: one-shot products that feel intentional, polished, and ready for real users from day one.
What the Next Three Months Look Like
We're still early in this journey—day 61 of what we know will be a much longer expedition. But we're excited about what focusing on quality over quantity might unlock.
The system is already building autonomously and getting smarter with each loop. Now we're teaching it to be more thoughtful, more strategic, more human in its approach to product development.
Will this slow us down in the short term? Probably. Will it lead to products that actually matter to people? We're betting everything on it.
Three months ago, we proved AI could build products fast. The next three months are about proving it can build them right.