xkylan

Here's a glimpse into how we're building at xkylan Instead of spending most of our time on the products themselves, we've been focused on the machine that builds the products I think the best way to explain it is the Tesla analogy. Tesla's real product isn't the car. It's the factory. The manufacturing line that can produce quality at scale with high uptime (once the factory is dialed in, the cars kind of take care of themselves) We're trying to apply that same thinking to building with AI. Because the cost of high-quality intelligence is dropping so fast, it's becoming feasible to use AI at scale in ways that weren't possible even 6-12 months ago. That creates a lot of opportunity But here's what I'm learning....if you just throw AI at problems without the right systems around it, the output is inconsistent. It's sometimes brilliant, sometimes complete garbage. You end up gambling with your token spend (*partially why I think lots of companies aren't seeing the ROI they expected*) So we've been investing in the machine itself. The prompt architecture, the workflow orchestration, the feedback loops that catch bad outputs before they ship. The boring infrastructure kind of stuff The bet is that if the machine is solid, then every product it touches gets better automatically. Instead of fixing things one product at a time We're still so early, and things constantly need refining. But the approach is starting to pan out